It’s no secret the automotive sector is racing to find ways of tapping into the potential of generative artificial intelligence (GenAI) to design and build the next generation of vehicles. This technology has much promise, from redefining manufacturing processes to helping carmakers design smarter, safer and more efficient vehicles.

Much of the time, proprietary automotive innovation is kept under lock-and-key as a critical competitive advantage, but recently Toyota has shared the development of a new tool that is enabling designers and engineers to collaborate more efficiently and easily.

GenAI is a type of artificial intelligence that doesn’t just focus on processing data: it uses advanced machine learning techniques—particularly deep learning—to generate new content. The technology has the potential to enable carmakers to optimize vehicle designs and structures, producing lighter, more aerodynamic and more fuel-efficient vehicles. However, the GenAI is still in its infancy and has encountered challenges evaluating complex variables such as manufacturing limitations and detailed safety regulations.

“Generative AI tools are often used as inspiration for designers, but cannot handle the complex engineering and safety considerations that go into actual car design,” said Avinash Balachandran, director of the Human Interactive Driving (HID) Division at the Toyota Research Institute (TRI). TRI is a division that focuses on incorporating next-generation technologies into the carmaker’s manufacturing processes.

TRI recently shared a GenAI process that could overcome those limitations to assist vehicle designers. These designers can already use publicly available text-to-image generative AI tools as an early step in their creative process—but TRI’s new technique combines early design sketches and engineering constraints into the process. Reconciling design ideas with engineering constraints early in the process results in fewer iterations to reach the final design.

For example, Toyota’s designers introduce constraints such as drag, which impacts fuel efficiency, into the generative AI process. Subsequent iterations would optimize drag within the parameters defined by the designer.

“This technique combines Toyota’s traditional engineering strengths with the state-of-the-art capabilities of modern generative AI,” said Balachandran. “It was motivated by the advancements in text-to-image generative AI tools, where you could type in a prompt, and it generates an image adhering to the stylistic guidance of that prompt. The inspiration for this technique and these tools was not to just spur creativity, but also to shorten that iteration loop between engineering and design,” he says.

Balachandran’s team had to tackle the difficult task of reconciling a sleek and elegant design with the realities of engineering performance and safety requirements. Designers and engineers often have very different backgrounds and ways of thinking about how a vehicle looks and performs—requiring a significant amount of back-and-forth between them to achieve a feasible solution, which can slow down the design process.

“To overcome these limitations, we built an AI model that can incorporate precise engineering constraints—like minimizing aerodynamic drag—to maximize the performance of these potential cars,” said Balachandran. “This will cut down on the number of iterations considerably and allow designers and engineers to work more closely and quickly.”

Adding those engineering constraints to the generative AI model allows the user to set limitations on the AI’s generative designs, requiring it to apply those constraints to the design. As a result, the generated design will account for factors that improve performance, safety, and reliability while satisfying the designers specific needs.

By the time the vehicle design goes to the engineering team, some of the job has already been done. “Reducing these iterations allows for faster vehicle design processes as well as improved efficiency for the design and engineering teams,” said Balachandran.

The technique has the potential to significantly accelerate electric vehicle (EV) design, in particular. “If you have superior aerodynamics, you can improve the range of that vehicle without increasing the size of the battery,” said Balachandran. “This is powerful, as large batteries are not only expensive to make but also use the limited resources that we have to build them. By focusing on drag first, we hope that we can make a big difference in the design of EVs…At the end of the day, we hope that these tools can offer value for any vehicle design though we targeted drag first as it has an outsized impact on EV designs.”

The new generative AI technique optimizes aerodynamic drag in successive iterations based on parameter inputs from the designer. (Image: Toyota)

The technology can factor in any measure that is inferable from the image itself—including drag. In fact, drag is inferable because shapes have particular drag coefficients that the AI can measure. Other factors that impact ride handling, such as the wheelbase and ride height, can also be optimized by the AI.

It strikes a balance between amplifying the designers’ capabilities and the engineers’ constraints. “We spent a lot of time working with designers to understand their pain points so that we can develop techniques that added value to them,” said Balachandran. His team focused on ways the AI could assist designers by helping them focus on the parts of their job where they could apply their creativity to the fullest. They discovered that multiple iterations between the designers and engineers posed a significant challenge because it took them away from the creative process where they could add the most value—and they enjoyed the most.

Toyota’s generative AI tool also creates digital prototypes of vehicles, which are put through simulated real-world test, enabling engineers to identify potential flaws early in the development process, avoiding potentially costly flaws during production.

For example, a designer can request that the tool design a vehicle based on an initial prototype sketch with qualitative parameters such as “sleek” or “like an SUV.” The tool would interpret the request and create a few designs as requested—while still optimizing quantitative performance metrics such as aerodynamic drag.

“We’re leveraging generative AI tools that are trained on thousands of other images of vehicles,” said Balachandran. “Part of the power of these tools is that they can use the knowledge gleaned from this corpus of data to help a designer explore this subjective space and push themselves creatively.”

The tool is currently being used for vehicle handling characteristics such as drag, ride height, chassis position and structural integrity. Balachandran’s team is working with its partners across Toyota’s network to enable designers to incorporate the technique into their own workflows.

“The hope is that, by using this tool, they can expand the power of design ideas while at the same time drastically improving the speed of design development,” said Balachandran. “Generative AI is a powerful new tool. We are exploring, across our many research areas, how to leverage it responsiblyso it can amplify our people.”

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The global economy is under intense pressure to move to a net zero-carbon model—and industrial manufacturers have the potential to play a crucial role in achieving that ambitious objective. In fact, automation may provide innovative solutions that enable manufacturers to be commercially and environmentally successful.

Rockwell Automation, a supplier of industrial control and automation solutions, outlines how manufacturers can use Industry 4.0 technologies—and the rich data they can generate—to support energy transition to net zero carbon in its Industrial Decarbonization report.

“Decarbonization will be a story of innovation and optimization,” said Steffen Zendler, process industry strategy and marketing manager at Rockwell Automation. “Our customers have an installed base from which they need to earn profits today to invest in the energy transition tomorrow. Process efficiency is key to balance these competing ambitions.”

Companies can start this ambitious process by identifying what the report calls “zones of convergence,” where actions taken to increase operational efficiencies and reduce costs can also lower emissions—achieving quick wins that don’t negatively impact their ability to compete in the market. The report outlines five pathways for manufacturers to identify those zones of convergence, balancing the need to remain competitive with the need to reduce ecological impact of their operations.

The five pathways are:

• Energy efficiency: optimizing energy use and reducing energy consumption enables industrial companies to cut costs while reducing carbon emissions. The report points out that the majority of industrial greenhouse gas (GHG) emissions result from burning fossil fuels for energy.

• Extending asset lifespan: as equipment ages it becomes more susceptible to breakdowns and energy use inefficiencies. Improved asset optimization, including automation measures such as predictive maintenance and bringing legacy assets into a company’s digital platform, can help reduce costs and downtime, and reduce energy consumption.

• Optimizing inputs and resource use: Industrial processes can be quite energy- and natural resource-hungry, resulting in a large carbon footprint. Optimizing production processes, and identifying opportunities for efficiency and waste reduction, allow manufacturers to use only the materials they need in the most effective manner.

• Faster responses to incidents: A machine or process failure on the shop floor can have significant environmental consequences and result in an increase of emissions. Implementing IoT-based solutions such as sensors and alerts can enable operators to solve potential problems before they manifest, or respond more quickly and effectively to unexpected breakdowns.

• Reducing need for on-site staff: The less people needed on site, the less cost involved and emissions generated in transporting them to and from the work site. This is particularly true of industries that currently need operators in remote or sensitive locations, such as offshore oil, where those costs are magnified.

Any one of these pathways has the potential to enable industrial manufacturers to achieve efficiencies throughout their operations—but will require a thorough review of the way they manage their business. “Industrial processes will need to be reassessed and improved to increase efficiency, reduce virgin material use, and drive down GHG emissions across the industrial value chain,” according to the report.

To achieve these objectives, and to maximize the potential of Industry 4.0 technologies, companies will need to generate, harness and gain insight from data throughout their operations. But this presents a particular challenge for industry, since many industrial manufacturers still gather data the old-fashioned way via pen and paper and input it into spreadsheets or systems that don’t necessarily communicate across the enterprise.

However, Industry 4.0 does offer solutions that can address those concerns. Internet of Things-based sensors can track variables such as energy use, emissions levels and others, often in real time. That data can be processed and used to identify opportunities to increase efficiency, reduce emissions, lower resource use and reduce costs. In turn, the data can be used to inform where and how automation is implemented across the production process. If done properly, it can create a virtuous circle: these improvements can generate more data, which in turn can be used to find further efficiencies.

In fact, automating operations can provide the context and insight needed to implement sustainable change, enable faster responses to problems, free up workers for higher-value tasks—including reducing the number of people required on site—and reduce the potential for error not only for internal business processes but also in preparing emissions disclosures to regulators. And while that amount of data is difficult if not impossible for individual employees to stay on top of, Industry 4.0 technologies such as machine learning and artificial intelligence can be deployed to support data analysis and business decision-making.

“Our customers really want to understand which activity would derive the greatest return on their investment and the largest in terms of reduction for emissions,” says Anissa Thomas, Global Sustainability Leader at Sensia Global, in the report. “Within each stage of the customer sustainability journey, there’s a common thread for digitalization: connecting disparate assets, visualization, and contextualization of data to improve decision making and drive automated workflows. We refer to this as ‘intelligent action.’ Sustainable operations are driven by intelligent action.”

A big factor in decarbonization will be the shift from fossil fuels to renewable energy sources—as well as capturing, storing or using industrial emissions in situations where alternative power sources aren’t feasible. Critical infrastructure is needed—asap—to support the transition away from burning fossil fuels. As well, proven and reliable automation technologies will be needed to enable manufacturers to capitalize on the potential of renewables.

New skillsets will be needed to support this transition, alongside infrastructure and new technology. In fact, it is estimated to need 22.7 million new jobs. Industry 4.0 can help companies meet that demand by deploying automation measures to take on some of the work wherever possible, support training for workers who will be performing new tasks and ensuring that operating procedures stay consistent during the transition.

The report focuses on one particular renewable energy source: hydrogen, which is not dependent on sunshine or wind. Green hydrogen has the potential to be a viable alternative to fossil fuels for industries that employ high heat in their processes—such as cement production and iron smelting—and which may find transitioning to electricity difficult if not impossible.

The International Energy Agency (IEA) estimates that renewable energy sources will need to provide 90 per cent of the world’s power by 2050—and an investment of $4 trillion by 2030 will be required to make this possible. While this seems to be a daunting goal, companies are already creating innovative solutions to the challenge: according to the IEA, many of the technologies required to reduce emissions are already at the demonstration or prototype phase today.

The companies that can leverage innovative solutions to reduce emissions, increase production to meet growing demand, and maintain a healthy profit margin will be the ones poised to succeed in the economy of the future—and existing Industry 4.0 technologies for automation and process optimization promise to power those innovations and help boost the bottom line of those companies. Is your company one of them?

The post How Automation Can Help Manufacturers Succeed in a Decarbonized Economy appeared first on Engineering.com.

The rise of Industry 4.0 has brought technological transformation to many metalworking companies—particularly in the form of automation. However, not every company has the resources for a wholesale overhaul to fully automate its shop floor.

This doesn’t mean small metalworking companies are destined to miss out on the benefits of automation, though. By taking a few deliberate, strategic steps, these companies can successfully integrate automation into their business.

This roadmap shows how it can be done.

Broadly, there are four main stages of the automation journey that small manufacturers can follow to maximize their investment dollars in automation.

Stage One: Quick Wins

This is the most basic level of automation: tweaking existing processes with automation. This stage isn’t about transformation, it’s about using automation to enhance the processes already in place to resolve bottlenecks, improving the productivity of workers and the machines already in the shop.

“The very first thing shop managers need to do when it comes to automation is identify the exact process that’s most worth automating,” said Justin Geach, Global Director of Marketing at Master Fluid Solutions, an Ohio-based company that supplies metalworking fluid products and support to the global metalworking community.

“What we often see as most successful with our customers is automating processes and tasks that are easily repeatable and need to be performed all the time, since those are the processes that usually achieve the fastest return on investment (ROI). For that reason, we recommend automating some of the more basic but critical tasks such as part loading and unloading and tool breakage detection or coolant fill systems to enable lights-out production.”

There is no shortage of tasks that can be automated in a metalworking operation; that’s why it’s essential for small businesses to identify the most important task to automate first.

In fact, the first step may not be related to the shop floor at all—but may be found the office: “in the early stages, the best place to start is with software to automate business processes and record keeping,” said Geach.

By targeting a specific problem or bottleneck and investing in automating it, the returns on that investment can be used to implement further automation. Those returns can manifest themselves quickly after making that first step. Workers benefit from increased efficiency; an automated process enables them to focus on higher priority tasks and accomplish them with less effort and strain, leading to improved job satisfaction. Higher productivity can also result in higher throughput, faster time-to-part and time-to-market and improved profit margins.

It can be beneficial to bring in an automation specialist to help guide the process. “Working with an automation consultant can help shop managers plan out long-term goals and ensure they’re investing in the right equipment each step of the way,” said Geach. “Investing in automation can be a big expense for small businesses, so gaining an outside perspective from a consultant or well-regarded automation installer is a great place to start.”

Stage Two: Partial Shop Automation

Once a manufacturer implements some basic automation processes, it’s time to start connecting them together into a bigger picture. In this stage, equipment and robots work together to perform multiple tasks in longer workflows, reducing the number of manual touchpoints along the way. By eliminating significant waste and boosting productivity.

“For partial automation, we can recommend machine part loading and unloading systems for the higher production parts to enable lights-out manufacturing,” said Geach. “Keep in mind these systems should be flexible in order to quickly adapt to new jobs, as parts can frequently change in small business and job shops. Invest in basic equipment and systems to minimize bottlenecks in your production.”

It’s important for companies to avoid investing in the wrong kind of automation—to really take care to find the right fit of technology for the business. “For example, fully autonomous robotic systems are usually not necessary for small metalworking shops because there is often too much process variation to make large scale automation feasible,” said Geach. “Large scale robotic and material transfer systems may be better suited to production manufacturers.”

Another pitfall to avoid is not having the right personnel to install, program and troubleshoot automation equipment. Provide workers with incentives and opportunities to train in these areas to enhance their skillsets, grow their careers and continue providing value to the company.

Stage Three: Extensive Automation

The next step is to further integrate automated processes into a comprehensive operations-wide process, where most of the processes are automated by robots and other Industry 4.0 technologies, and where products can move automatically between stations with minimal worker interaction. “Later on in the process, it might make sense to expand to more advanced automation, depending on business and facility needs,” said Geach.

This high level of automation can benefit a wide variety of metalworking companies, helping to significantly enhance productivity and throughput. It also has the potential to create a virtuous circle, where improvements to the company’s bottom line enable further adoption of Industry 4.0 enhancements.

Stage Four: Full Automation

This final stage involves full automation of a metalworking shop, where all operations flow seamlessly in a single, fully integrated workflow with minimal to no manual touchpoints. Full automation enables a continuously improving operation with no wasted materials or steps and can contribute to a circular economy.

While this may not be the end game for every company, it does model the efficiencies that automation can bring to a metalworking company, enabling the company to determine the level of automation that best suits its business objectives.

Ensuring Employees are On Board

Throughout the process, it is crucially important to keep workers engaged in the transformation. They may recognize that automation can increase productivity, free up employees from repetitive tasks, and reduce the need for humans to work in potentially hazardous environments. But some of those workers may be concerned that robots will be taking their jobs.

A company looking to automate needs to approach the technology with the mindset of making better use of human employees rather than replacing them. The company’s workforce will be important partners in maintaining, implementing and monitoring the automation technologies being introduced.

“Automation should be understood as a complement to your existing workforce—not something to disrupt or replace them,” said Geach. “That’s why it’s important to implement automation at a sustainable pace. Investing in too much technology up front could overwhelm employees and lead to improper implementation, impacting overall ROI.”

A workforce can be resistant to change, particularly if it disrupts processes that workers have been using for a long time or requires a significant overhaul of the way they work. Geach recommends centering the discussion with employees around the ways automation will benefit them in their jobs—making it more than just how it affects the company’s bottom line. “Automation makes people’s lives easier so there are often a lot of positive aspects to highlight for frontline workers…it provides a learning experience to build skills for the future if employees want to take on the new challenges. It can also allow them to work on more meaningful projects and jobs.” He stresses that it’s also important to be upfront in addressing any fears about job cuts or how automation could impact their security. “Discuss the implementation roadmap so employees know what changes to expect to their processes, and when,” he said.

If You Fail to Plan, You Plan to Fail

While automation is complex, it is possible to implement it in a realistic and productive manner. This requires a thorough plan from the outset. “The most important thing to keep in mind about automation, especially for small businesses with limited budgets and resources, is simply to have a plan and know exactly what you want to accomplish with it,” said Geach. “Identify specific goals you want to achieve and then invest in technology accordingly.”

The post A Roadmap to Automating Small Metalworking Shops Without Breaking the Bank appeared first on Engineering.com.

In the age of IoT, more assets are being connected to digital platforms to maximize their performance and life span. One crucial way this is helping businesses save money is through enabling the implementation of predictive maintenance.

By deploying sensors connected to a digital platform—even for legacy assets—and using machine learning algorithms to monitor asset performance, companies can get ahead of potential machine breakdowns. Deploying those technologies could also enable businesses to automate their maintenance efforts.

Predictive maintenance differs from conventional preventative maintenance. Conventional preventative maintenance is based on a fixed schedule, where machines are pulled offline at regular intervals regardless of their state so an engineer or technician can examine them.

In contrast, predictive maintenance monitoring is condition-based and takes place in real time. This approach uses IoT-enabled sensors to collect and analyze machine performance data on a continual basis and identify, in real time, potential performance issues before the machines fail. This approach helps companies schedule maintenance when it’s most convenient and cost-effective, resulting in reduced downtime and lower cost. It also leads to shorter downtimes required for inspection and testing, a reduction in the number of repairs and replacement parts required and more time for maintenance technicians and engineers to dedicate to other tasks.

However, implementing a predictive maintenance program takes some prudent planning and preparation. It also requires a modest investment in the sensors and communication protocols required to gather the data and feed it into the predictive maintenance system.

When it comes to specifying sensors for a manufacturing environment, there are a lot of choice on the market. To be sure you make the right choice, there are five factors that need to be considered when determining the right sensor package for an operation’s predictive maintenance system.

Russ Freeman, product portfolio manager with RS Group, a global supplier of products and services for designers, builders and managers of industrial equipment and operations, says knowing exactly what you’re sensing is just the beginning. “If you know nothing about a sensor, but you get these five questions answered, it puts us in the best position to help you,” he said. By answering all five of these questions, you give an industrial products supplier the best chance pf making sure you have the right sensing options for your application.

First: determine what data you are trying to capture. The more precisely you can identify what you need to measure, the better the choice you’ll make in selecting a sensor. “How big is it? What color is it? What texture is it—wood, metal or plastic? Does it change textures? Does it change color? Does it change size?” he asked. While these may seem like obvious questions, it is still important to ask them.

Second: define the range from which the sensor will need to collect data. A machine being monitored from an inch away will require a different kind of sensor than one being tracked from several feet away. “Distance is important because if I need a sensor rated for three feet, I know I can’t use an inductive proximity sensor,” said Freeman. This will help narrow the range of sensors on the market to a list of suitable options.

Third: determine any constraints on power that you plan to use for the sensor. You must match the input power of the sensor to the power available at the site.

Fourth: establish the kind of output needed. A PNP (positive-negative-positive) sensor produces a positive output from the sensor’s input, while an NPN (negative-positive-negative) sensor produces a negative signal. The operator needs to know which input the application requires. Another factor to consider is whether a relay output is needed.

Fifth: what’s the operating environment? The configuration of machinery, available space, indoor or outdoor, or whether it is clean or dirty, will all influence the choice of sensor. A conveyor belt on a busy factory floor will need a different sensor than the same conveyor in a clean room or healthcare facility. “Different environments involve different considerations when selecting a sensor,” said Freeman, adding that you want to be sure the sensor you select will withstand the operating environment.

Automation Maintenance

While a solid predictive maintenance program can reap rewards for any manufacturing operation, highly automated factories may have the most to gain from moving to a predictive maintenance regime.

“Automation customers want to produce products at an increased rate, with higher quality and be more efficient doing it,” said Freeman. “No matter what control platform they choose to accomplish these goals, they are all dependent on repeatable, high-speed and accurate feedback from their sensors.”

There are two main data collection and analysis platforms for automation: cloud computing and edge computing. Cloud computing involves collecting data locally on the machine and then sending it to remote servers in the cloud to be processed and analyzed. The processed data is then sent back to the work location to aid in decision-making. With an edge computing system, the data is collected and processed at the machine, either in its control panel or on the machine itself. Rather than outputting the data to another location for analysis, the data being transmitted has already been processed. As a result, edge computing requires much less IT infrastructure than cloud computing, making it a more flexible option for smaller companies looking to retrofit existing automated equipment, and is more secure since it’s not transmitted to a remote location.

Predictive maintenance can deliver an excellent ROI for companies trying to generate better data and improve the efficiency of their operations. It’s essential for these companies to choose the sensors best suited for the job, especially if they aim to automate their predictive maintenance systems. Equipped with the right sensors, they will be able to generate the data they need to prevent costly breakdowns and maximize the benefits of Industry 4.0.

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Universal Robots’ UR10e collaborative robot at work in the MT Solar facility. (Image: Universal Robots)

Many small manufacturers are investigating automating their operations: not only is the technology more affordable, it’s also easier to set up, manage and integrate with a company’s other processes than ever before.

MT Solar is a Montana company that makes stands and fixtures used in solar panel installations. The firm recently incorporated automation into its operations to meet a 300 per cent increase in demand for its parts every summer; the company has experienced challenges in finding skilled workers to help meet the demand.

MT Solar focuses on fast lead times for making its products. The company had staggered sales and low volume. “The dollar value tied up in inventory for a fast delivery solution allowed us to help our customers feel good about buying from us versus the competition,” said Travis Jordan, founder of MT Solar. “But about four years ago, we had hit the breaking point of that process and we had maxed it out. Our batch sizes had been brought down as small as we could bring them.” The company brought in a CNC plasma cutter—it helped speed up the production of parts, it wasn’t helping the company meet its end goals. It had plenty of inventory of finished products, but at a level that wasn’t ideal for business.

The Wrong Way to Automate

“It was a classic case of the wrong way to automate,” said Jordan. “It’s the right way to automate for maybe a car manufacturer, but we’re buying it from our local welding shop. So we have a disjoint between robot welding that that requires high speed and a shop who’s got a batch run of 500 parts and wants to start to automate.”

Jordan compares it to a grandfather building dining tables for his children and grandchildren. “If he’s going to build one table, he might as well build four because takes very little extra time to build four than it does to build one,” he said. “Problem is, if he’d had 16 grandkids, he’d have been stumbling over piles of table legs to try to get things done.” That’s why scaling operations for a small manufacturer, particularly one that works in batch orders, may not be the right approach.

MT Solar finally landed on a cobot as the solution. “We said, what do we need to deliver out the door to the customer? And how many of those do we need to deliver per day?” said Jordan. With those targets in mind, the company created an assembly line that combined manual processes and the cobot. “Maybe you’re at a rate of 10 or 12 per day,” said Jordan. “You divide the day up into blocks, then you divide all your segments of all your parts into blocks and you say, how many of these parts can I produce in that time block? How many of these parts can I use the robot to produce? Well now I have 45 minutes for this robot to work, which is a long time in an automation space.”

Under this approach, it’s not important how fast a robot can make the part, but rather how repeatable and how well can it make the part without human intervention. “I don’t care if it takes it 20 minutes to make the part and the robot can do it in five,” said Jordan. “If I only need one every 20 minutes, this robot’s the right answer for me.”

MT Solar acquired the cobot from Vectis Automation, an automation integrator—which is a company that installs and starts up technology in a manufacturing setting.

“[MT Solar’s] peril was similar to the majority of manufacturers today—it’s tough to find skilled-trades labor, and those skilled workers don’t want to do the boring work anyways (nor should they),” said Josh Pawley, Vice President of Business Development at Vectis. “The business aspects of increased productivity, a more inviting place to work, and improved quality and lead times were all factors too. They were able to easily offload the ‘boring’ weldments to the cobot and increase productivity on them at the same time.”

Jordan found that the cobot not only improved the manufacturing process, it also helped his employees improve their skillsets and perform better. “The cobot allows me to get collaborative with my 16-year-old kid who can run the little tablet and my 60-year-old gray-haired guy who’s been welding all his life and says, that just doesn’t sound right.” MT Solar’s employees don’t have to be technologically savvy to work with the cobot. “There’s no real entry barrier there,” said Jordan. “I’m not scared to throw somebody new at it: they pick up the basics in 15 minutes, and then you can have somebody tweaking stuff.”

Keep the End Goal in Mind

Through MT Solar’s experience in getting into automation, Jordan concludes that there are two paradigm shifts that have to occur for small companies to become efficient at automation. 

“The first paradigm shift is you have to accept that you’re going to solve the small problems first,” he said. Many companies, understandably, may want to focus on automating the most complicated problem first—the one that gives them the most trouble. Jordan thinks that’s the wrong approach. Instead, companies should consider finding efficiencies and solving smaller problems first, to optimize their existing processes as much as possible before automating them. “Maybe your first step of automation might be moving your pallet one step closer to your jig so that you can take one step rather than two to get something set,” he said. “You save a minute here, a minute there, a minute on the next one.” Jordan tends to hear lots of pushback on that approach from operators, who don’t think it’s worth automating something that only saves them a minute. “Automate the easy things first, grab your low-hanging fruit, because then that buys you time and money to begin to tackle the more and more difficult ones,” he said.

“The second biggest key point to automating is to make sure you get your end goal of your measurable output solved first and then focus all of your automation against the end goal,” added Jordan. Manufacturers may take the approach of wanting to automate each piece of the process, assuming the sum of the efficiencies gained at each step will result in time saved in making the finished product. However, changes to each step need to be oriented to the broader goal of efficiency—not just making each stage faster.

Even if it takes longer to make each individual part, time can still be saved in getting finished products out the door in time if the process is automated the right way,” said Jordan. “Why not cut 30 beams at once? The saw is going anyway—cut the whole bundle at once. But what do I do with the 29 beams I didn’t need? I have to stack them on a pallet, and later on I have to reach down, pick them up and reposition them. That time savings of cutting that many parts I didn’t need was burning up my guys because they automated the work cell rather than automating the process.”

Finding the right automation technology for the job, and not automating for the sake of automation, helped MT Solar respond to demands from its customers and improved the company’s manufacturing processes—demonstrating how small manufacturers can make use of automation to maximize their busines

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After years of R&D and niche applications, manufacturers are embracing 3D printing as a part of their critical manufacturing operations. However, as capabilities expand and engineers design specifically for additive, some companies are finding that conventional printers don’t always meet their needs when it comes to printing very large or very small components. That’s where a customized 3D printer is increasingly becoming a viable solution as the next step in a company’s tool kit.

“Within the last few years, lots of companies bought their first [additive manufacturing] systems, especially OEMs,” said Felix Bauer, head of sales at AMCM, an EOS Group company based in Germany that specializes in customizing EOS 3D printing machines to meet specific customer requirements. “They’ve made their way out of the little baby steps and now it comes to very specific use cases.”

Those companies would then work with specialists such as AMCM to develop a custom 3D printing solution. Customizing a 3D printer could involve modifying or adjusting a component of a standard machine—such as the laser—according to the part design, material or throughput needs. If the application requires it, a company like AMCM could also develop a completely new machine for the manufacturer.

The need for custom machines is more common in metals printing. While polymer additive has been around for decades, metal additive manufacturing is still new, and expertise is less widespread.

A common use case for custom 3D printing is the need to manufacture a metal component that is large and bulky in some sections, but small and filigreed in others. Engineers could find themselves needing two machines in one to print both parts of these components. When an engineer encounters barriers with what is available on the market is when a call to a specialist company that can design customized printers would add significant value to the operation.

The increase in the use of metal has resulted in more standard metal printers coming into the market; Bauer estimates that there will be a greater demand for customization of 3D printers in the next few years. “I really think that some applications make more sense on a customized machine, where you tune the machine towards the material or towards the size of the part,” he said. “These types of applications will benefit from customized machines.”

AMCM broke a world record printing an additive metal part 1,000 mm in height, using a modified EOS M400 printer.

Printing Large and Small Parts

Standard 3D printers have a finite work area that constrains the size of the finished part each machine can print. However, there is growing demand at opposite ends of the work area for either very large pieces or very small ones.

At the large end of the spectrum are parts such as rocket engines. AMCM teamed up with the aerospace startup Launcher to 3D print rocket engines. This required a machine with a work space larger than the ones found on the market in order to print its rocket combustion chamber. The chamber needed to be printed as one single part for optimal cooling, better maintenance and lower cost—crucial elements to Launcher’s business model.

Those components usually have dimensions approximately 450 x 450 x 1,000 millimeters and are made of copper chromium zirconium—but Launcher couldn’t find a conventional printer on the market that could fit the finished chamber.

“It became clear that they needed a certain adjustment to the machine, because it was not tall enough or there was just a little something missing with the machine that really could help enable the business case,” said Bauer. “That’s when we came in and made the machine that fits perfectly for the rocket guys.”

Launcher worked with AMCM to customize an EOS M4K industrial printer to produce a large single-part copper alloy component with internal regenerative cooling channels. A key component of the printer is the 1-kilowatt laser, which is ideally suited for higher conductive materials such as copper. The M4K can be equipped with that laser as well as the more conventional 400-watt lasers commonly found in laser printers. The machine can print using copper, tungsten and titanium.

Customized printers are also increasing in popularity when it comes to creating very small components. One example is the anti-scatter grid (ASG) used in CT scanners to absorb scattered radiation and boost resolution—an instrumental component in an important technology. An ASG has a thickness of only 100 microns and a positional accuracy of a mere 25 microns. AMCM partnered with Dunlee, a medical imaging and x-ray component supplier, to develop a customized EOS M 290 printer that could manufacture ASGs.

The grids are made from tungsten because the material can withstand high temperatures, is very wear resistant and is highly effective in blocking radiation. However, the material is notoriously difficult to process, and being for medical use the component needs to be made to precise and exacting standards.

“They are extensively filigreed, very fine parts that cannot be printed with a normal laser—you have to adjust the machine a little to create those very special parts,” said Bauer. The printer enabled Dunlee to double its production capacity in one year.

Whether the parts that need to be printed are exceptionally large, or microscopically small, Bauer anticipates that the market for customized machines will grow significantly.

The examples cited above are indicative of the broader trend in some industrial sectors to seek out customized 3D printing solutions—the same sectors where additive manufacturing already had a solid foothold. In the North American market, AMCM sees the most opportunity in aerospace, particularly the private-sector space industry, while in Europe the demand is high in the medical sector. The company also takes on business from OEMs and their top-tier suppliers.

Standard, Customized or Both?

While customized 3D printers can create a lot of options when it comes to part design and manufacturability, this type of capital equipment investment is not a decision to be taken lightly.

“The very obvious first consideration is, does the part fit from a size perspective in today’s machine,” said Bauer. “And the next thing I would also take into consideration is whether the materials needed to make those specific parts available on today’s machines—as well as whether the machines use these materials in the right quality or quantity.”

Some printing applications make sense on a customized machine, where the printer is adjusted to best use the material or print at the size needed. But there will always be a useful role for standard machines.

“There are some applications that require an economy of scale where you have to have these lower costs in making your batches,” said Bauer. He also noted that, while there is a growing demand for customized machines, the standard machines are becoming more and more capable and adaptable to take on those out-of-the-ordinary printing jobs.

“I really think that in five years, it’ll be a pretty common thing to get your production needs covered by the best-fitting machine,” said Bauer.

For a manufacturer facing a decision on custom additive, it would be worthwhile to reach out to a company that specializes in making those modifications. “It might be surprisingly easy to customize the machine for you,” said Bauer. “Maybe it’s that little extra temperature, that little extra envelope, that little extra powder…or it could be a requirement to make it quicker.”

The post When One Size Doesn’t Fit All: The Rise of Custom 3D Printers appeared first on Engineering.com.

(Image courtesy of Automotive Testing Technology International.)

What is CAE?

CAE is the process of using computer software to improve product design and find solutions to engineering problems in a variety of industries. Common tools used in CAE include simulation, optimization and validation of both products and processes; these can be deployed throughout the manufacturing cycle, from design to testing to implementation.

“When it comes to design, most design groups are only able to explore a few different paths due to limitations of prototyping costs and/or engineering simulation tools,” said Dr. Masha Petrova, VP of marketing at OnScale, an Ansys company. “They have to rely on the ‘gut instinct’ of engineers to drive design directions. As technology becomes more sophisticated, engineering software must advance to allow for testing exponentially larger numbers of design permutations. It’s not practical to explore all of these possibilities empirically.”

CAE enables manufacturers to run digital tests and simulations on a product—so by the time a physical prototype is created, the design is already as optimal as possible. Those digital tests include finite element analysis, as well as fluid and thermal analyses, among others.

There are three stages in CAE: pre-processing, solving and post-processing. Pre-processing involves modeling the system and physical properties of the product. In addition to modeling the product itself, this phase models the environment the product is intended to work in, including pressure, force and temperature, so as to create the most accurate simulation possible.

Solving involves running simulations of the model to test its functionality in optimal as well as adverse conditions. These simulations emphasize why the accuracy of the model is so important. After all: the more precise the simulation, the better the product.

Finally, the results of the simulations are analyzed in post-processing. That analysis is used to refine the product and inform the next round of simulations.

Advantages and Drawbacks of Using CAE

By using CAE manufacturers are able to build and test a physical prototype within hours, as opposed to days or weeks.

Some of CAE’s advantages include:

• Saving time and money by reducing the need for creating several physical prototypes and enabling manufacturers to make more efficient designs faster.
• Creating designs with fewer errors—and whose errors are easier to fix—compared to manual designing.
• Reducing the effort needed to create models by automating model design.
• Reducing labor duplication by using computer coding to perform repetitive tasks.
• Creating designs with improved accuracy and precision compared to manual designs, and which are easier to store and share.
• Enabling better decision making sooner in the development process to make easier and less expensive changes.

While CAE is a useful tool, manufacturers still need to consider certain factors when thinking about investing in the technology. A digital platform runs the risk of hardware failure and computer breakdowns, which could result in lost work. It can also be prone to viruses and hacking which could also lead to a loss of work. Employees may need to be trained on CAE software, resulting in additional monetary and time costs. In addition, the cost of a new CAE system—as well as maintenance and updating—may be out of reach for some manufacturers.

The Cloud

Creating highly accurate simulations of complex geometries can be a formidable task for even today’s powerful computers. Running a CAE platform can require a significant amount of computing power from sophisticated IT infrastructure—which many manufacturers might find out of reach for their operation.

For those businesses, cloud-based CAE services may provide the solution. High-performance cloud computing services can enable smaller companies to access CAE without having to lay out a significant investment in purchasing and maintaining their own potentially expensive hardware.

“A cloud-based approached is necessary to enable engineers to fully take advantage of… powerful solvers, give them the ability to solve very large, real-world problems, and to realistically set up and conduct design of experiments,” said Petrova.

Products on the Market

There is a wide variety of cloud-enabled CAE software products available to manufacturers. Here are a few examples of the offerings on the market.

Altair One

Altair One is an integrated platform that brings together all of Altair’s products and HPC services under one service. By creating a unified development environment, Altair One enables multi-specialist teams to access HPC and artificial intelligence (AI) to generate sophisticated simulations. As a result, Altair can deliver access to a unified development environment for the execution of complex projects.

Ansys Cloud Direct

The scalable Cloud Direct platform offered by Ansys provides access to on-demand computing that includes interactive workstations as well as high performance computing clusters. Users can access this HPC power from desktop applications, and a broader collection of apps can be accessed via web browser. The company has partnered with Microsoft Azure to create a secure cloud environment for the design and simulation testing of products. Ansys claims that by removing hardware-related constraints, its product can increase simulation throughput.

CATIA by Dassault Systèmes

CATIA is well-known in the aerospace, automotive and machine industry sectors. Powered by Dassault Systèmes’ 3DEXPERIENCE platform, the software features a social design environment where 3D dashboards enable designers to use common and trusted data sources to design products collaboratively, in real time and concurrently across the organization. The development platform is also easily integrated with a company’s existing processes and tools, making it easier for staff across disciplines to communicate and collaborate with each other.

Creo Parametric by PTC

Creo Parametric is part of PTC’s Creo family of computer aided design (CAD) software products that enable manufacturers to design products at every step of the product life cycle. Creo Parametric, the suite’s flagship, allows for 3D model design with sophisticated features such as sweeps, revolves and extrusions, which makes it particularly useful for engineers and manufacturers. Creo Parametric includes extensions that enable designers to use 2D CAD, 3D CAD and parametric and direct modeling capabilities to create, analyze and share designs.

Fusion 360 by Autodesk

Autodesk’s cloud-based software platform features 3D modeling and simulation, CAD, computer aided manufacturing (CAM), CAE and printed circuit board (PCB) functionality for product design and manufacturing. It also includes generative design tools and integrated data management capabilities that enable teams to collaborate on and manage product data in real time.

NX by Siemens

Siemens NX is an integrated software solution that is flexible and powerful. It supports product development at every step from concept design to final production. The software’s toolset coordinates work across engineering disciplines, safeguards data integrity and helps to streamline the CAE process. It enables an iterative process that helps produce results quickly and efficiently. Siemens’ continual development cycle ensures that the software is consistently up-to-date and keeps it on the cutting edge.

SimScale

SimScale’s multi-purpose CAE tool is entirely cloud-based, and functions on a software as a service (SaaS) model. There is no special hardware component to the product; it runs directly on a browser, and can allow multiple simulations to run in parallel. SimScale allows for a variety of simulation analyses, including computational fluid dynamics (CFD) and finite element analysis (FEA).

Thanks to cloud-enabled CAE products, the high-performance processing required to maximize the potential of CAE to create sophisticated products is within reach of more and more companies. It’s clear that CAE is no longer on a manufacturer’s wish list—it’s becoming a must-have tool for businesses to create efficient, reliable and cutting-edge products while remaining competitive.

This story is one in a series underwritten by AMD and produced independently by the editors of engineering.com. Subscribe here to receive informative infographics, handy fact sheets, technology recommendations and more in AMD’s data center insights newsletter.

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One of a manufacturer’s most important decisions could be to determine which production technology is the best one to manufacture their part. Does it make sense to stick with the traditional manufacturing processes or make the jump to new technologies such as 3D printing? That decision has many factors: part design, budget, complexity of the component, weight, number of parts to produce, how many parts to keep in the warehouse, carbon emissions, lead times and more.

Most design software can help inform these decisions based on design and material data, but industry is starting to see a new generation of new software developed specifically as a decision support tool for industrial manufacturers that automatically analyze a component’s design to determine if, and how, additive manufacturing is the solution to produce the part.

“We enable manufacturers to decide whether to use additive manufacturing over traditional manufacturing methods when it can save them time or money, reduced carbon emissions or provide a supply chain benefit,” says Omer Blaier, co-founder and chief executive officer of Castor, an Israeli software developer that’s trying to take some of the guesswork and intuition out of this decision-making process. “We’re not the doctors. We’re giving tools to the doctors to take better decisions,” he says.

Inputs for Output

The software analyzes a variety of files, including cut files, computer aided design (CAD) files and 2D drawings—not only on a technical level but also a financial level. This type of software performs this analysis at scale as well, looking at large numbers of files at the same time.

“You can upload the whole dashboard of a car, the whole inventory list of spare parts, the whole assembly or sub-assembly of a machine that a manufacturer wants to optimize,” said Blaier, adding that the software will assess whether it makes sense to use additive manufacturing to produce some or all of these parts or if traditional subtractive manufacturing is still the best option.

Castor can analyze thousands of parts at once and identify opportunities to change the design of certain parts if that makes using additive manufacturing to manufacture them more desirable. “For example, it will recommend to combine multiple parts into one or to make parts hollow if it thinks that additive manufacturing can create a change in the weight of a part,” says Blaier.

Tool maker Stanley Black and Decker was looking for a more efficient and profitable process for manufacturing one of its low-volume, high-complexity parts, which was made from highly customized components. Refining and innovating those components would usually take about eight weeks per iteration using traditional manufacturing processes.

The company tried Castor to find a better way to make the parts, using the software as a decision support system for identifying where additive manufacturing would be a viable alternative. The software provided a technical and cost-saving analysis for a full machine design, including recommendations for which 3D printing style, printer and printing material were most suitable for the part’s mechanical properties.

“[It] played a fundamental role in the process which led to the integration

of the first 3D printed metal production part at Stanley Black and Decker,” says Moses Pezarkar, Manufacturing Engineer at Stanley Black and Decker.

“The hardware is there already,” says Blaier. “Additive manufacturing equipment such as 3D printers can do amazing things, but the software, the applications, are lacking a way to determine how to use the benefits of these new manufacturing techniques.”

Combining technical and financial analysis is particularly innovative approach. Blaier says the software is 50 per cent technical analysis and 50 per cent economic analysis and can be customized to take into account a wide range of financial variables. “Companies can change the software to work according to the titanium price they know today, according to the hourly rate they pay today to their engineers, the electricity cost they pay today in their manufacturing technique, and the analysis is aligned with their customized input,” says Blaier. “They can use our defaults based on our experience from 50,000 parts we’ve finalized, or they can customize it to align with their manufacturing approach in general.” 

Synthesizing technical and economic data bridges gaps that often occur between the design team and the production team which can lead to delays and rising costs.

“When you show the financial benefit to the production engineer, procurement manager, VP of engineering and even supply chain managers—people that have to do with the business side of things—then you have a good case to convince a design engineer that maybe his design might either be suitable as is or with some changes to adopt a different manufacturing technique than he designed the part for,” says Blaier.

Decision support software such as Castor informs those discussions and decisions before a company embarks on a project to transform their manufacturing processes. Seeing the benefits and challenges of additive manufacturing tailored to the company’s available resources and short- and long-term objectives can be invaluable.

This type of decision-making value is not lost on the major software developers, According to Blaier, Castor is embedded as part of platforms such as Siemens’ Teamcenter PLM, Materialise’s CO-AM platform and Ultimaker’s Digital Factory. Blaier says companies understand that there is a need for this type of parts-and-process identification tool before you deep dive into additive manufacturing processes.

On The Right Track

Another sign that castor is one the right track in terms of helping solve these issues was its selection as a winner of Hexagon’s Sixth Sense challenge program, which helps startups bring their products to wider markets and scale their business. Hexagon identifies high-priority economic, environmental and consumer challenges and asks companies to submit applications to describe how their products and services respond to those challenges.

“Castor is a great example of a start-up we want to support,” says Milan Kocić, Head of Hexagon’s Sixth Sense. “The unique solution is helping companies scale additive manufacturing operations, become more sustainable, cut costs and improve supply chain resiliency. By combining expertise and resources, we’re aiming to help Castor scale and make a real impact on industry.”

Today, manufacturers understand additive manufacturing has the potential to transform their operations, make them more efficient and responsive to the market, innovate and remain competitive. However, the real challenge is in determining when and how to integrate emerging technologies such as 3D printing into the shop floor in the most effective way. Software such as Castor enables companies to tap into the disruptive potential of additive manufacturing in a manner that is tailored to their specific needs and objectives.

The post Mill, Mold, Cut or Print: How to Decide Which Process is Best appeared first on Engineering.com.

Industrial simulation is a transformative tool that can fundamentally change the way manufacturers do business—but manufacturers need to have clear goals in mind when incorporating the technology into their processes.

That was the principal message from three industry leaders who participated in a recent Association for Advancing Automation (A3) webinar.

“There are two reasons people get into advanced simulation and virtual commissioning,” said Phil Glennie, Director of Marketing and Sales at Longterm Technology Services, a manufacturing software reseller and integrator in London, Ont. “The first is that they want to. The second is that they’re forced to—and we’re finding that has really grown a lot recently.”

Virtual Commissioning

Virtual commissioning is, in essence, the practice of using virtual simulations to design, install and/or test control software in a virtual environment before implementing it in a real-world setting. In contrast, conventional commissioning involves testing directly on a physical machine or machines, a real programmable logic controller (PLC) and other physical components such as sensors and actuators.

One of the main advantages of virtual commissioning, however, is the ability to test and adjust much earlier in the development process with a digital twin—so that by the time it comes to real-world commissioning, many of the tweaks and adjustments have already been resolved.

“People are looking to technology and simulation to be able to address those earlier on in the process,” said Graham Wloch, Director if Business Development, Visual Components a developer of 3D manufacturing simulation software and solutions based in Finland. “It creates a better, more optimized situation…You can go from putting out fires and being reactive to being proactive. A lot of our customers are trying to do that, to get ahead of things and mitigate those potential issues whenever possible.”

Industrial automation is a field that moves slowly, mostly because implementing changes can be dangerous and costly. Companies are understandably conservative about making changes to their processes: the slightest misstep can result in a broken machine and potentially an injured employee.

In the past the operators of a manufacturing cell would have to determine what happens in each moment and manually program and synchronize each machine to move, or wait, at each moment—a process that can take weeks, particularly in the case of machines working closely together and moving within a fraction of an inch of each other. And while modern machines are more aware of their surroundings and adept at avoiding collisions, it’s still a laborious and expensive process.

This is how virtual simulation can be a real game-changer. A collision in a simulation won’t damage equipment and result in no lost machining time.

Simulation is Becoming the Norm for Project RFQs

Software and automation service providers are finding it increasingly common for manufacturers to include a “hard spec” request for a simulation or virtual commissioning for the job in question. While this means additional challenges for the suppliers, it brings about benefits for both the supplier and the client.

For the client, it provides a layer of control over the deliverables that they didn’t have before. In addition, the digital deliverable can be incorporated into the client’s existing digital library to update the simulation and can be repurposed in the future to reprogram lines and variables. “If you need to introduce a new product or program a line, and you have that digital twin established, you can have a person reprogram that line or re-test control logic from anywhere—they don’t have to be anywhere near the facility,” said Glennie. “It’s not just about the project being delivered, it’s about receiving the digital deliverable and weaving it into the existing digital twin of their facility.

For suppliers, it motivates—some might say forces—them to build a library of commonly used components and the schematics associated with them. It also makes it easier to check their work, including work that has been outsourced, and enables them to be more responsive to the client’s requests. It’s a particular benefit for small automation companies getting into the market; armed with simulation experts and a library they could compete on equal footing with larger, more established companies.

Implementing Industrial Automation, the Right Way

It may be tempting for a manufacturer to jump into industrial automation with both feet—after all, the benefits are clear to see. But it should be approached with caution.

“First, understand what you currently have, and then you can use it to make all sorts of changes down the line,” said Wloch. “You get your house in order, you get everything fixed and running efficiently, you figure out your bottlenecks. You might not even need automation if you just fix what you have—and if you use automation to improve it, it’s a win-win.”

Simulation software could be used to gain that understanding. Uploading any assets that were installed pre-simulation could help a company determine what works well and what needs to be improved. This could provide manufacturers with the data they need to make an informed decision about implementing the right automation tools in the right spots. “Customers who want to automate because ‘robots are cool’ don’t have the right vision and may not get to where they want to be,” said Wloch.

When examining a manufacturer’s existing assets and processes, it’s important to have good coaching and a skilled partner who can help guide it towards the right purchase and help the manufacturer build in-house capability to operate and maximize the technology’s effectiveness. Some software provides comprehensive services that includes factors beyond just automation, and others provide specific solutions such as robot motion software—and it’s vital for a company to choose the right fit.

In addition, while industrial robots are all very similar in how they function, they are very different in how they are actually programmed. Each vendor has a different way of programming their machines, which means a company needs to rely on people with expertise in multiple robot brands. Having different experts work in the same simulated virtual space enables them to work together to resolve those differences and align their products to work with each other efficiently before installing the machines next to each other in the real world. As a result, the manufacturer can spend less time worrying about which robots they have, and more time focusing on using the technologies to meet the company’s broader objectives.

The benefits of simulation are often scalable as well. The efficiencies gained from simulating one particular variable can be used again the next time that process is updated or enhanced, and potentially applied to other processes and manufacturing cells—it’s not a one-time benefit.

Making Manufacturers More Agile

Industrial simulation can enable manufacturers to respond to the increased demand for flexibility in their operations. It can also help build supply chain resiliency.

Manufacturing processes have conventionally been built with eye on simplicity: a manufacturing cell was built to work for many years, with incremental changes being implemented along the way. But today’s market requires more flexibility, and manufacturers are responding by implementing complex cells that offer more flexibility, that allow for smaller batch production and even allow for producing multiple products in the same cell. While this results in a more agile process, it increases the complexity of the program. “If you’re trying to be more flexible and agile with traditional ways you’ll quickly reach a wall you can’t get past,” said Alejandro Soler Fenoll, Senior Manager of Applications Engineering, Realtime Robotics, a Boston-based automation control software developer. “Customers are hungry for flexibility, they want very dynamic cells that they can repurposed in shorter periods [of operation].”

Industrial simulation has significant potential to transform the way manufacturers do business. “You’re still looking at a change that is on the level of [the transition from] paper drawings to CAD,” said Glennie.

The technology can help make companies more efficient in how they develop products and bring them to market, and it can help build resilience and agility to deal with new challenges and plan for future growth. The key is to approach this transformation with a clear vision of what the company has and needs, and a clear end result in mind.

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Digital transformation is becoming increasingly predominant—and increasingly important—across a large cross-section of industries thanks to the growth of digital tools and technologies such as the Industrial Internet of Things (IIoT) and artificial intelligence (AI).

In the past, manufacturers have relied on methodologies such as benchmarking and Kaizen to refine and improve their processes. While useful, these methodologies—which can often be manual processes—have been unable to access, process and derive insights from the massive amounts of data that can be generated on the factory floor.

New digital technologies such as machine learning, AI and IIoT sensors can both capture and generate extensive amounts of data. Analyzing and deriving actionable insights from it requires a digital solution. That’s the problem digital transformation means to solve.

Being able to do so would be of clear benefit for manufacturers. Digital transformation can be challenging to implement, though—and many manufacturers are falling behind on the digital transformation curve. Technologies such as high-performance computing or supercomputing could be key components that enable those businesses to catch up with the competition.

What is digital transformation?

You’ve heard the buzzword, but what does it actually mean?

Digital transformation—as the term implies—is the process of using digital technologies to improve and enhance a company’s business and production processes. But it’s more than just putting sensors on a legacy machine—though that in itself is a valuable part of the process.

Rather, it’s about intentionally and strategically implementing comprehensive changes to the business to reach its goals (improving efficiency, boosting customer and shareholder value, becoming greener, etc.)—and carefully selecting and deploying digital technologies to make that change happen. The consensus among manufacturers across industries is that digital transformation is a powerful tool that will help companies improve performance, respond to market challenges, meet the demands of their customers and become more resilient and adaptable to the future.

How Digital Transformation Helps Manufacturers

Manufacturers can gain a lot of actionable insight from data and analytics, AI and digitally enabled machinery and processes—resulting in opportunities to add significant value to their companies and give them a competitive edge. Research and advisory firm McKinsey estimates that companies that successfully navigate this transformation could see forecasting accuracy increases of up to 85 per cent, machine downtime reductions of 30 to 50 per cent, throughput increases of 10 to 30 per cent and labor productivity improvements ranging from 15 to 30 per cent.

Digital transformation is “the only way to respond to sustainability, changing customer requirements, competitors who are in the process of transforming, increased operating resilience, supply chain volatility, cost control, and increasing product and packaging complexity,” said Greg Gorbach, vice president of digitalization and Internet of Things at ARC Advisory Group, a leading technology market research firm for industry and manufacturing.

This transformation can benefit manufacturers in a variety of ways:

• Higher productivity: automated systems can accomplish repetitive processes that humans aren’t particularly suited for, reducing errors and inefficiency and freeing up employees to focus on more value-added work.
• Reduced operating costs: automation, data analytics and IoT sensors can enable operations to be adjusted automatically to save time and money through measures such as predictive maintenance to avoid machine breakdowns and optimizing energy use to save utility costs.
• Resolve labor challenges: automation on factory floors and in the office can help reduce manual workloads, enabling manufacturers to conduct business with smaller workforces—and helping maximize the work of skilled laborers through the use of technologies such as collaborative robots.
• Create resilient supply chains: the transparency enabled by real-time data analytics allows manufacturers to become more flexible and responsive to supply chain challenges.
• Enabling ongoing improvements: data generated from digital transformation can be used by businesses to recognize and address opportunities to further optimize their operations.

How Are Manufacturers Doing?

In a recent Gartner report, large operations are spending twice as much and taking twice as long to implement their digital transformation plans than they originally expected to. In addition, 53 per cent of the organizations the firm surveyed haven’t been tested by a digital challenge yet, undermining their readiness for that transformation.

According to McKinsey, there are five recurring reasons for the failure of manufacturers to see their digital transformation through.

First, implementation tends to be siloed. Companies often set up digital transformation delivery teams that are not plugged in to executive leadership, site operations or enterprise-wide IT departments. Other firms try to replicate the success of a single site transformation across the enterprise without taking into account broader network complexities.

Second, companies fail to adapt to challenges along their transformation trajectory. They pursue a one-size-fits-all solution (such as the single site experience applied to all sites in the previous point) that is not resilient or adaptable enough to take advantage of unique circumstances or respond to particular challenges in separate manufacturing sites.

Third, companies could find themselves paralyzed by overanalysis. It may not always be ideal for a company to perform a deep and exhausting analysis of its current operations, leaving it with no further stamina to implement that transformation. Instead of going all-in on a top-to-bottom review, companies could be better served with a limited, strategic analysis from which to extract actionable insights.

Fourth, companies may become enamored with implementing a particular technology rather than putting their values and vision first. A technology-first approach means solutions are implemented without sufficient focus on actual business challenges or capabilities—which can undermine the plan’s adoption by the very people tasked with implementing it.

Lastly, companies could be making “perfect” the enemy of the good. Waiting on the ideal digital architecture and complete amount of data before implementing any solutions could mean manufacturers miss the opportunities to add value that come from a pragmatic and viable architecture. This flaw is somewhat related to the third point about analysis paralysis.

Companies that have successfully implemented a robust digital transformation plan haven’t jumped headlong into acquiring and deploying particular technologies. Instead, they have invested time in properly identifying how Industry 4.0 can meet the needs of their operations first—and planning and deploying a digital transformation strategy focused on meeting those needs.

HPC and Supercomputing

As you can imagine, digital transformation can generate massive amounts of information. This presents a unique challenge to the operation that’s undergoing that transformation: how do you capture all that data and turn it into insights that you can use to grow your business?

Increasingly, the answer could be supercomputing and high-performance computing (HPC).

Supercomputing is the use of a powerful computer to perform tasks that require a lot of computational power or handle a lot of data—more than a shop floor manager’s computer can handle. In contrast, high-performance computing is a broader term that covers all the skills and tools needed to build supercomputers—and link them together into a cluster to work as one. Since most of today’s supercomputers are HPCs that operate in clusters, the two terms have become interchangeable.

Imagine if you had tens of thousands of laptops, all connected together, running one piece of software that needs all of the laptops running it at the same time, to solve one single problem. That’s what supercomputing does.

But while supercomputing offers great potential, the costs of owning a supercomputer can be prohibitive—and it requires specialized staff with thorough knowledge of advanced modeling and simulation techniques. The U.S. Department of Energy does provide American businesses with access to its HPC resources. And increasing demand could put market pressure on the sector to reduce costs.

But with or without a supercomputer, digital transformation is not only necessary, but achievable. Oracle estimates that 82 per cent of manufacturers either already have, or intend to develop, a digital transformation strategy—and that nearly a third of those operations already report having a competitive advantage. It’s a process rather than a destination—and is one that can be initiated right now.

This story is one in a series underwritten by AMD and produced independently by the editors of engineering.com. Subscribe here to receive informative infographics, handy fact sheets, technology recommendations and more in AMD’s data center insights newsletter. 

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