The permanent filter module is designed to trap soot in a sintered plate filter, coating the waste material with an inhibitor for dry disposal. According to the company, this increases machine uptime, stabilizes gas flow, reduces consumable costs and increases machine safety.

Alongside the redesigned SLM machines comes the launch of the Picture Perfect Pro Plan, an alliterative sales package for new machines in North America and Europe. Existing Nikon SLM Solutions customers can also opt into the new plan by renewing their service contracts for five years.

According to the company, Pro Plan benefits include:

• 85% Machine Uptime Commitment: Nikon SLM Solutions commits to a minimum of 85% uptime for mid-size and larger systems, including SLM 280, SLM 280 PS, SLM 500, and NXG series models. Beyond this minimum commitment, the company also claims success rates exceeding 90% uptime among installed base customers.
• Essential Production Items: All systems come with a handling device designed for safe and fast part handling. SLM 500 buyers receive an additional set of build cylinders.
• Five-Year Software Access: Continued access to SLM.Link, SLM.Quality, and Free Float software.

Additionally, the Pro Plan includes a tailored powder supply contract and a five-year service package. Nikon SLM Solutions claims that the first uptime commitment for mid-size and larger machines in the additive manufacturing industry.

The post Nikon SLM Solutions introduces new plan for new and existing machines appeared first on Engineering.com.

With many CIOs reporting to CFOs, the emphasis has largely shifted to cost efficiency over proactive innovation. This gap opens the door for digitally minded, innovation-driven executives like CDOs to bridge IT, engineering and manufacturing, enhancing data integration, accessibility and actionable insights. This coordination across Product Lifecycle Management (PLM), Enterprise Resource Planning (ERP), Material Requirements Planning (MRP) and other core systems supports a holistic approach to data-driven innovation.

A 2023 Deloitte Insights report highlights three factors driving CDOs’ effectiveness in transforming business performance: aligning their vision with business strategy, controlling data management practices and cultivating influential relationships to extend their reach. Approximately 67% of Fortune 500 companies now have a CDO role, underscoring the priority of data leadership across industries.

This article examines how the CDO elevates IT’s mandate, aligning data strategies with engineering and operational needs to make data a driving force behind manufacturing and product innovation.

The CDO drives end-to-end data integration

In manufacturing, the integration of PLM and ERP is not just a technical requirement; it is an essential component of strategic growth. The end-to-end flow of data between product design and production (facilitated by PDM and PLM) and operational processes (led by ERP, MRP and MES systems) boosts collaboration, shortens time-to-market and improves product quality —ultimately unlocking greater innovation potential through real-time, data-backed insights across the product lifecycle.

The CDO approaches this integration challenge with a strategic vision. By dismantling data silos and enabling seamless information flow between PDM, ERP, MRP, MES and CRM repositories, the CDO empowers cross-functional teams with real-time insights that support timely, informed decision-making. This effort involves more than just integrating systems; it requires fostering a culture of continuous collaboration and improvement. The CDO mandate is therefore more about people and stakeholder alignment than about data analytics and AI governance.

Addressing some key questions can drive value from cross-functional alignment, including:

• Are design and engineering teams optimizing products for customer needs and regulatory standards?
• How effectively are production teams implementing efficient manufacturing strategies?
• Are procurement teams securing cost-effective and sustainable supplier partnerships?
• Is the sales strategy aligned with product capabilities and customer feedback?
• How efficiently are we responding to market changes based on real-time data?

The CDO role goes beyond data integration and technical alignment, fostering a culture where data serves as the foundation for innovation and responsiveness in product development. With well-managed and accessible data, innovation flourishes, enabling organizations to meet market demands proactively and competitively.

The CDO bridge the engineering-IT divide

The CDO role in bridging engineering and IT is central to achieving seamless collaboration across all stages of product development and manufacturing. Engineering and manufacturing teams often rely on specialized tools and platforms for design, simulation and testing, which may not always be compatible with traditional IT infrastructure. This disconnection can isolate engineering efforts from broader strategic objectives, slowing down innovation and creating operational silos. Here, the CDO brings a strategic approach to data integration, aligning specialized engineering tools with enterprise systems and ensuring that engineering efforts contribute directly to overarching business goals.

By establishing data pathways between engineering and IT, the CDO makes real-time insights available across departments, enhancing collaboration and ensuring engineering data supports PLM and ERP solutions. This integration goes beyond technical alignment. It fosters a shared understanding and sense of purpose across functions, allowing engineering teams to innovate with a clear view of how their efforts impact end-to-end product strategy. When engineers can seamlessly access critical information and align their objectives with broader business needs, the organization can drive faster time-to-market, improve product quality and make more informed decisions at every stage of the product lifecycle.

A 2023 survey published by the Harvard Business School reported that “The CDO role is poorly understood and incumbents of the job have often met with diffuse expectations and short tenures. There is a clear need for CDOs to focus on adding visible value to their organizations.” Furthermore, the authors, Davenport et al., highlighted that “Of the CDOs surveyed, 41% said they define success by achieving business objectives —significantly more than those who measured success in terms of change management or culture shift (19%), technical accomplishments (5%), prevention of serious data problems (2%), or an equal combination of these factors (32%).”

As the CDO aligns data strategies to break down engineering silos, they create a connected ecosystem where engineering insights actively inform operations, design and customer needs. By prioritizing and advocating for the distinct data needs of engineering, the CDO elevates this function from an isolated operation to a core, strategic component of the organization. This alignment allows the business to respond more rapidly to customer demands, ensuring that innovation efforts stay on track and deliver tangible value across departments.

Leveraging technology adoption to create value

While CIOs are often tasked with “sweating the assets” and managing technical debt, the CDO can advocate for engineering’s unique data requirements, highlighting their role in broader strategic goals. An effective CDO can reshape the traditional IT role, transforming it from a cost center focused on operational maintenance into a proactive, strategic partner in product innovation and manufacturing excellence. By aligning IT functions with product development and manufacturing priorities, the CDO facilitates a shift from reactive data management to data-driven growth and continuous innovation.

The CDO fosters digital transformation by building a data governance framework that prioritizes data quality, accessibility and security—empowering IT to act as an enabler of data-driven insights rather than just a gatekeeper of digital resources. Through advanced analytics, machine learning and cloud-based solutions, the CDO enables IT to leverage data assets for predictive maintenance, supply chain efficiency and agile product design. As a result, IT contributes actively to achieving the organization’s strategic goals, moving beyond maintenance to deliver predictive insights that inform every stage of the product lifecycle.

Collaboration between the CDO and CIO is also critical in identifying and prioritizing technology investments that drive the highest business impact. This synergy ensures that while the CDO focuses on strategic, data-driven growth, the CIO maintains a stable and secure IT foundation—balancing innovation with operational resilience. By harmonizing their objectives, the CDO and CIO can allocate IT resources more effectively, emphasizing data initiatives that deliver substantial returns while safeguarding the organization’s core infrastructure.

In times where agility and innovation determine market leadership, the CDO stands as a visionary force, aligning data, technology and people to redefine business potential. By embedding data-driven strategies at every level, the CDO not only bridges operational silos but also empowers organizations to drive sustainable growth, anticipate change and maintain a competitive edge. In the digital age, the CDO leadership is not just valuable—it is essential for turning data into the organization’s most powerful asset.

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“The TruPrint 3000 is designed for volume production of high-quality parts,” said Roland Spiegelhalder in a press release. Spiegelhadler is product manager for additive manufacturing (AM) at TRUMPF. “Users from all industries, such as the automotive and aerospace industries, will benefit from it.”

According to the company, the integrated cooling system monitors the process area and maintains a constant temperature. This is intended to allow the 3D printer to use the full 700 watts of laser power without overheating the material during the printing process, making it suitable for aluminum alloys, among other materials.

The integrated cooling is also designed to allow the material to cool more quickly. TRUMPF claims this makes components printed on the TruPrint 3000 more durable with a high level of reproducibility, meaning that the machine can print the same part repeatedly with the same quality standard.

By increasing laser power to 700 watts, TRUMPF’s engineers have been able to increase the size of the machine’s two laser spots from 80 to 200 µm, allowing the lasers to process a larger area on the build plate. “This increases the productivity of the system while maintaining the same high part quality,” Spiegelhalder said. 

TRUMPF is presenting the 3D printer at Formnext in Frankfurt, Germany this week.

The post TRUMPF turns up the power for Formnext 2024 appeared first on Engineering.com.

The power of artificial intelligence (AI) to enhance digital transformation initiatives has become increasingly evident to engineers as they seek to improve operational efficiencies, scale innovation and gain a competitive edge.

While digital transformation is hardly new, AI and large language models (LLMs) have emerged as a formidable accelerator by changing business processes, reshaping products and services and sometimes upending entire industries.

AI’s ability to learn and improve over time, coupled with digital transformation, means that organizations can realize faster processes, reduced costs and more efficient operations.

These AI benefits contribute to an environment of continuous improvement and innovation that is often key to success in a competitive environment.

Enhancing data-driven decision-making

Engineers know that data-driven organizations use digital insights to shape strategies, optimize processes and respond rapidly to market changes. However, harnessing the potential of integrated digital data at scale for decision-making requires far more than traditional data analytics.

AI’s capability adds unprecedented speed and precision to decision-making by:

• Sifting through structured data, identifying patterns and generating predictive insights.
• Analyzing vast volumes of unstructured data more effectively than search engines, specialized databases or software developers to generate predictive insights.
• Avoiding the cost and elapsed time associated with custom data integration of diverse data sources using software developers.
• Autonomously detecting trends and forecasting outcomes.

Adding AI and LLM capability to data-driven decision-making helps engineers optimize operational and strategic decisions while reducing the need to base decisions on history, experience, in-vogue ideas or hunches.

Examples of adding AI capability to data-driven decision-making for engineering include:

• Monitoring large volumes of IIoT data from production equipment to identify performance anomalies to avoid unscheduled downtime.
• Sifting through the external media for general and industry audiences to identify competitor initiatives that may require a response.
• Summarizing patent data, trademark data and research journals maintained in multiple languages to identify potentially relevant technology developments.

Automating processes and workflows

Automation is a fundamental aspect of digital transformation. AI-powered tools like robotic process automation (RPA), machine learning and cognitive computing, a type of AI that simulates human thought processes, have taken digital transformation to new heights.

While valuable, previous generations of automation that engineers implemented were limited to well-defined, repetitive tasks and detailed, rule-based decisions. AI expands automation to more complex decision-making processes, pattern recognition and more generalized problem-solving.

Examples of adding AI capability to automating processes and workflows include:

• Adding more accuracy and sophistication to simulations. For example, engineers can refine and enhance their designs through successive simulations to reduce limitations, which leads to more innovative solutions.
• Enhancing supply chain management for better product demand forecasting, logistics optimization, order fulfillment and risk assessments for component shortages. Achieving these improvements requires the integration of disparate data sources maintained by partners.
• Adding more intelligence to RPA transaction workflows such as invoice and shipment receipt processing. Examples include identifying potentially duplicate invoices, assessing the materiality of discrepancies and identifying likely fraud.

Improving data quality

Engineers are painfully aware that insufficient data quality is the number one reason for the failure of digital transformation initiatives. Asking data analysts to identify and correct data quality issues is slow, tedious, expensive and subject to further errors.

AI can automate data quality improvement work using pattern recognition. AI can achieve more speed and consistency at a lower cost than human analysts.

Examples of using AI capability to automate data quality improvement include:

• Recognizing that existing equipment can’t manufacture the designs due to dimensions, lack of accessibility and unachievable tolerances.
• Identifying and correcting instances where numeric values are associated with different units of measure or measurement systems creates errors and confusion.
• Sharply reducing the number of duplicate and incomplete inventory master records.
• Generating synthetic data to augment existing datasets to improve AI models.

Persisting knowledge

Organizations lose surprising amounts of essential knowledge and intellectual property (IP). Too often, engineers reinvestigate problems or wrestle again with design refinements because of a lack of awareness of prior work. Loss of knowledge and expertise typically occurs due to:

• Staff turnover and transfers.
• Reluctance to share knowledge.
• Lack of management support for knowledge management.
• Lack of time to document work.
• No repository in which to store work products.
• No easy ability to search and retrieve documents.
• Organization restructuring, acquisitions and mergers.
• Confusion caused by inaccurate, outdated or redundant versions of information.

Addressing these issues without digital transformation is impossible. Including digital knowledge management to the scope of digital transformation initiatives can significantly increase the value organizations achieve from the knowledge they have accumulated, often at considerable effort and cost.

Adding AI agents to knowledge repositories can add another increment of value. AI agents are intelligent software that use an LLM to perform query tasks, make decisions and learn from their experiences like humans. AI agents are a significant advance on the more familiar chatbots.

Examples of using AI agents to enhance digital knowledge management include enabling engineers to:

• Query “tribal knowledge” to improve production performance.
• Discover best practices.
• Better troubleshoot production equipment problems based on records of historical incidents.
• Query IP such as patent records, test results, research reports, development studies and licensing agreements in support of current work.

Challenges and considerations

While AI and LLMs add potential to digital transformation, engineers must acknowledge the challenges and ethical considerations associated with its deployment. These include:

• Ensuring data privacy to maintain customer and employee confidence.
• Addressing biases in AI algorithms and training data to maintain trust and inclusivity.
• Recognizing that LLMs may be incomplete or misleading.
• Training a skilled workforce that can effectively manage AI-driven processes.
• Fostering a culture that embraces innovation to ensure the smooth integration of AI and LLM technologies.

Engineers can establish robust data governance, prioritize transparency in communication and continuously monitor AI systems to mitigate unintended consequences.

Artificial intelligence is a powerful accelerator of digital transformation. Its impact spans most industries and functions, enhancing efficiency, agility and resilience. By embracing AI’s transformative potential, businesses can achieve a sustainable competitive advantage and drive long-term growth.

The post Where AI can accelerate digital transformation appeared first on Engineering.com.

The biggest recent development on that last one is probably the 2022 experiment at Lawrence Livermore National Laboratory (LLNL) which saw a successful fusion ignition, but the scientists and engineers working at LLNL aren’t content to stop there.

“Now that we have achieved and repeated fusion ignition,” said Tammy Ma, lead for LLNL’s inertial fusion energy institutional initiative, in a press release, “the Lab is rapidly applying our decades of know-how into solving the core physics and engineering challenges that come with the monumental task of building the fusion ecosystem necessary for a laser fusion power plant. The mass production of ignition-grade targets is one of these, and cutting-edge 3D printing could help get us there.”

The ignition targets Ma refers to are nearly perfect spheres of hollow diamond encasing deuterium and tritium (DT) fusion fuel. These are suspended inside a hohlraum: a cavity with walls in radiative equilibrium with the radiant energy within the cavity. Under exposure to intense laser energy, these hydrogen isotopes fuse and, ideally, produce more energy than needed to start the reaction.

Unfortunately, these targets take months to manufacture, while a functioning fusion energy power plant would require nearly one million targets per day, igniting at a rate of ten times a second. The physical reaction would be similar to the ignition already achieved at LLNL, but the production of targets requires a fundamentally new approach that can work at scale.

Enter 3D printing, with a new LLNL project focusing on constructing a workflow to design, fabricate, characterize and field fully 3D-printed fuel capsules. The project is also developing a first-of-its-kind dual-wavelength, two-photon polymerization (DW-2PP) approach to meet the stringent engineering demands of ignition targets.

“We are focusing on a specific type of wetted-foam capsule, in which liquid DT can be wicked into a uniform foam layer on the inside of the spherical capsule by capillary action,” said Xiaoxing Xia, co-principal investigator and a staff scientist at LLNL. “The current DT ice layering process takes up to a week to complete with extreme meticulousness. It’s possible that 3D printing is the only tool for this kind of complex geometry at scale.”

If successful, this project could address critical bottlenecks towards 3D printing ignition capsules in their entirety.

“Our DW-2PP printer uses two light sources with different wavelengths to selectively print different materials with sub-micron resolution,” explained co-principal investigator James Oakdale in the same press release. “This novel capability gives us exquisite control over the spatial chemistry and densities within both the capsule and inner foam material, which allows us to respond quickly to bespoke or one-off capsule designs.”

According to LLNL, the work is already showing promise, with 3D printed targets successfully used during two fusion experiments in 2024 and more expected in the year ahead.

Could 2025 finally be the year we achieve fusion power?

Probably not, but at least we’re still making progress.

The post Why additive manufacturing could be the catalyst to harnessing fusion appeared first on Engineering.com.

If you’ve bought a phone or tablet in the last few years, there’s a good chance it includes a lidar sensor to support AR applications. You may have partaken in some AR games—Pokémon Go is the classic example—or used AR to see what some desired product, such as a piece of furniture, would look like in your living room.

Just as AR is augmenting our everyday lives, it’s also augmenting the lives of engineers and manufacturing professionals. Here’s a primer on the technology and how it’s being used in engineering.

What is augmented reality (AR)?

Augmented reality is a type of spatial computing that adds a graphical overlay onto an image of the real world. AR goes beyond a heads-up display, or HUD, in which static info is presented to the user overtop their surroundings. A hallmark of AR, and of spatial computing more broadly, is that the graphical output depends on the user’s position in space.

For instance, an AR application that places a virtual sofa in your living room would respond to your position in that room. Whether looking through a phone, tablet or head-mounted display (HMD), you could move around the virtual sofa and see it from different angles while it remained fixed in place.

If you were using the highest-end AR hardware with an exceptionally photorealistic 3D model, you could be fooled into thinking you really had a new sofa—at least until you tried to sit on it. For engineers using AR, that level of photorealism is often desired.

How are engineers and manufacturers using AR?

One popular way that engineers are using AR is to visualize their designs as they would exist in the real world. Automotive engineers, for example, could use an AR headset to see a life-size model of their latest vehicle right before their eyes.

With the proper hardware and software, they could even map the lighting and reflections of the real world onto the AR model—heightening the illusion of a real, physical vehicle. In this way, AR can provide a level of design insight not easily achieved without laborious prototyping.

Another emerging use of AR is as a tool for factory commissioning, maintenance and training. AR applications can present information about a piece of equipment on top of that equipment, such as visual instructions for how to operate it safely or change out a part. Similarly, AR could help plan or optimize a factory layout by revealing how a piece of equipment would physically fit in a space.

Once products are designed and built, AR is becoming an increasingly useful tool for selling them. Online product configurators that support AR allow users to see exactly what they’re buying, and in exactly the environment they’re buying them for. Nothing makes you want to buy a real car more than the tantalizing simulacrum of a virtual car in your driveway.

What types of hardware support AR?

Unlike virtual reality, which requires the user to don an opaque head-mounted display, augmented reality can be achieved with a more versatile range of computing hardware. Phones, tablets, low-profile smart glasses and HMDs can all be used for AR. This flexibility makes AR more accessible than VR, especially in the field or on the factory floor.

Not all hardware makes for an equal AR experience. Using a phone or tablet for AR may be convenient, but the quality, spatial stability and responsiveness of the AR models won’t be as good as with a high end headset. For engineers hoping to use AR to evaluate their designs, an HMD will provide the best experience. However, HMDs also range in quality and features from one manufacturer to the next. Additionally, for many users, headsets are uncomfortable to wear for long periods of time.

Ultimately, the best hardware for your AR needs will be dependent on your application, your software and your budget. As more devices emerge to support augmented reality and other types of spatial computing, more engineering applications of this spicy technology will be close behind.

The post The fundamentals of augmented reality (AR) for engineering appeared first on Engineering.com.

In the study, waste powder from selective laser sintering (SLS) systems was recycled by Dressler Group and 3D printed by Fraunhofer IPA at the University of Bayreuth using a VX200 HSS platform from voxeljet. According to voxeljet, the initial results demonstrate that reconditioned PA12 waste powder can be processed effectively using ink- and printhead-based high speed sintering (HSS) technology. Moreover, the company reports that the preliminary test results indicate the material properties of the test units are equal to or may even exceed those of comparable prints with fresh powder.

Normally, unprinted PA12 powder loses its ability to be reused due to high temperature exposure in the build area, which causes the polyamide chains to lengthen after condensation and negatively affects powder flowability and melt viscosity. This makes the material difficult to process again via laser-based technologies, since the energy input from lasers is too short to process the longer molecule chains.

The study aimed to reclaim this used powder by processing it through voxeljet’s VX200 HSS platform, which uses an inkjet-based printhead and infrared heating to allow the polyamide to sinter gradually, enabling its reuse.

“Recycling used PA12 powder can effectively reduce costs and support sustainability efforts in AM.” said Holger Leonards, head of R&D at Dressler Group in a voxeljet press release. “Our expertise in regenerating powder properties and handling of large powder volumes enables companies to reclaim this valuable material.”

“The VX200 HSS technology is an open-source system, allowing us to quickly change and adapt process parameters to any powder,” said Jan Kemnitzer, research team lead at Fraunhofer IPA, in the same release. “We were therefore able to quickly adapt the 3D printer to the material with consistent or improved results in part properties.”

“The results of this study are especially interesting for ink- and printhead-based technologies such as the HSS technology,” said Tobias Grün, global product management at voxeljet. “The future possibility of processing this recycled powder on production platforms like the VX1000HSS will bring immense cost savings. Typically, 50% of running costs are attributable to powder. Thus, this development provides a huge effect on cost effectiveness while boosting a circular material flow and reducing waste.”

voxeljet and Fraunhofer will be demonstrating the results of this study at their respective booths at Formnext 2024 in Frankfurt from November 19^th to 22^nd.

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ABB has been selected by OHB System AG, a German space technology company, to design and build thermal infrared payloads for the European Space Agency’s (ESA) Earth Explorer Harmony satellites, set to launch in 2029. The advanced payloads will enable the satellites to collect critical environmental data, including sea surface temperatures, cloud motion and the positioning of clouds, with a level of accuracy that will significantly aid in climate science and weather forecasting.

The Harmony mission aims to enhance our understanding of the Earth’s climate and environmental dynamics. ABB’s multispectral thermal infrared (TIR) instruments, including its multiview TIR technology, will measure radiometric precision—the accuracy of infrared temperature readings compared to the true surface temperature. These measurements will play a key role in improving climate models and forecasting extreme weather events, such as hurricanes. Additionally, the data collected will help assess geohazards, providing valuable insights into seismic and volcanic activity by detecting subtle shifts in the land surface.

The satellites will also help researchers understand how upper-ocean heat exchanges influence weather patterns and contribute to long-term climate changes. One of the mission’s unique contributions will be its ability to track ice loss from glaciers, which has implications for rising sea levels. The thermal infrared payloads, combined with synthetic aperture radar (SAR) data from the Sentinel-1 satellite, will give a comprehensive view of Earth’s surface and atmosphere, offering more accurate information about ocean temperature, cloud movements, and climate change drivers.

“Harmony will provide critical data that will help advance Earth system science and climate research,” said Florence Hélière, Harmony Project Manager at the European Space Agency. “The expertise and reliability of industrial partners like ABB are crucial for ensuring the success of this mission and its ability to deliver valuable insights on time.”

ABB’s selection for this high-profile mission highlights the company’s long-standing expertise in infrared sensor technologies. “We’ve worked with OHB on multiple space programs and know ABB’s capabilities are second to none,” said Agustina Alvarez Toledo, Harmony Project Manager at OHB System AG. “We’re excited to collaborate again on this project to help support the global scientific community.”

The Harmony satellites will operate in tandem with the Copernicus Sentinel-1 satellite, with their configurations changing throughout the mission. The combination of radar and thermal infrared imagery from the satellites will provide a more detailed and dynamic understanding of Earth’s climate system. ABB’s advanced sensor technologies will play a critical role in achieving the mission’s goal of supporting sustainable and resource-efficient solutions for understanding and mitigating climate change.

The post ABB to develop thermal infrared payloads for ESA appeared first on Engineering.com.

Some simulation software platforms include post-processors with various options to streamline workflows and make analyses easier. However, engineers can also export data from a solver and import it into separate post-processing or data analysis software. Some third-party software, including open-source tools, are optimized for specific techniques and datasets. Therefore, engineers should consider their simulation technique, data size and complexity when selecting a post-processor.

Although post-processing software can generate cool-looking pictures and impressive animations, the focus is less on creating appealing visuals and more on assessing results and gaining knowledge.

During post-processing, engineers must leverage their experience and mathematical and scientific training to evaluate the entire study and determine whether the results make sense. Recall that a simulation study starts with pre-processing a model under defined conditions, then discretizes the model’s governing PDEs into algebraic equations and solves the system of equations as an approximation of real-world phenomena. Pre-processing inaccuracies directly affect post-processing results and can give inaccurate representations of the model’s behavior.

Things to keep in mind:

• While planning a simulation study, clearly define the problem and what you want to achieve during post-processing.
• Select the right post-processing software that satisfies the study’s goals.
• Analyze the data for accuracy, validity and reliability to ensure the dataset makes sense and to evaluate the entire study.
• Consider who will use your results and ensure visuals are easy for such audiences to understand.

The post What is post-processing in engineering simulation? appeared first on Engineering.com.

More specifically, ArcelorMittal has selected HP’s Metal Jet S100 as the basis of its research into new steel powders. The announcement comes almost exactly one year after ArcelorMittal entered the AM market as a steel powder supplier with the construction of an industrial-scale inert gas atomizer in Aviles, Spain.

According to ArcelorMittal, the collaboration will focus on two key pillars:

1. Lowering the cost-per-part of AM, with an eye toward the automotive sector
2. Extending material options by developing new steels

The two companies have committed to bringing new steel solutions to a sufficient Technology Readiness Level before using ArcelorMittal’s research center as an incubator for new applications.

“We are thrilled to collaborate with HP in advancing steel additive manufacturing,” said Aubin Defer, Chief Marketing Officer for ArcelorMittal Powders in a press release. “This collaboration leverages our combined expertise to develop innovative solutions to drive the industry forward. The promising results of our steel powders with HP’s binder jetting technology are a testament to the potential of this partnership.”

“We are excited to join forces with ArcelorMittal to push the boundaries of steel additive manufacturing,” said Alexandre Tartas, global leader of metals sales at HP, in the same release. “This collaboration will enable us to leverage our technical expertise and ArcelorMittal’s leadership in sustainable steel solutions to create groundbreaking advancements in the industry. Combining the steel expertise of ArcelorMittal and HP Additive Manufacturing positioning in high volume production offers a unique value proposition for the manufacturing industry.”

Both companies will be exhibiting at this year’s Formnext, from November 19^th to November 22^nd in Frankfurt, Germany.

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