Regardless of the industry or technology, rarely is any system optimized at startup.
Experienced design engineers can certainly estimate cycle times, throughput, quality and uptime. However, the complexity of processes and associated controls leave plenty of room for fine-tuning during engineering and after installation. It should be noted that optimization is not the same as continuous improvement. Optimization is refinement conducted on a current process where continuous improvement generally refers to changes to the process or systems. These can be done concurrently but it is best to optimize first and then concentrate on continuous improvement.
Simulation and modeling
System simulation after initial design can be used quite effectively before the final design is complete. Simulation time and costs should be worked into a project whenever possible as the payback can be quite significant. These tools can be used to test and validated designs. Identifying potential issues at the design stage can reduce the risk of costly mistakes that could delay commissioning or cause problems afterwards. Simulation can be used to evaluated machining and automation processes and serve as a tool to facilitate conversations through the project.
Process Analysis and Mapping: Some simulation can be detailed enough to be considered a “Digital Twin” of the system. Digital Twins allow for even more detailed simulations to take place. System behaviour can be evaluated under very specific conditions and inputs creating a map that enables continuous optimization and testing without disrupting actual operations. As well, a properly designed and updated Digital Twin can then operate simultaneously with an actual system providing some future predictability.
Current State Analysis: Models encourage designers to more thoroughly understand existing processes. Creating an accurate model involves documenting every step, input, output, and resource used within the system. The goal is to have a clear and comprehensive overview of how the system operates. This in turn sets the foundation for identifying areas of improvement.
Once the process is mapped out and simulated, designers can identify points in the process where delays or inefficiencies occur. These could be due to machine limitations, inadequate supply of materials, or other factors that slow down the process. By visualizing the flow of materials and information through the system, designers can enhance and streamline processes and eliminate waste.
Data monitoring and collection
An often-neglected step in the improvement process is proper and accurate data monitoring and collection. A logical and systematic approach is required with emphasis on using the proper tools for the job. If good data is not collected, the subsequent analysis will be flawed. It is extremely important to try and understand what data is needed and how accurate that data must be. A camera system, for example, intended for use as image collection or shape recognition may not be able to measure dimensional attributes for quality purposes.
Sensor Integration: Integrating sensors into machinery and processes allows for real-time data collection of various parameters such as temperature, pressure, speed, and more. This data is crucial for determining system performance and possibly identifying areas for improvement. Forward planning will help reduce costs by designing in connections and associated hardware during equipment build.
Analysis: Once the data is collected, advanced analytics can be used to help identify patterns, trends, and anomalies. Deviations from expected performance metrics may indicate issues that need addressing. Analysis can be performed on-site or even remotely by a third parts that specializes in big data collection and analysis. Modern AI learning algorithms can now proactively predict potential equipment failures before they occur minimizing downtime and even extending the lifespan of machinery. Monitoring robot joint motor performance, for example, can be used to trigger a preventative maintenance activity before a problem leads to a significant breakdown.
PLC systems
Coding: Writing code is not necessarily difficult, however, writing efficient, modular, and well-documented PLC code takes time, planning and experience. Good coding techniques is crucial to make the code easier to maintain and modify, reducing the likelihood of errors and enhancing system reliability.
Error Handling and Debugging: Robust error handling routines are essential for quickly identifying and resolving issues by operator and maintenance personal. This must be specified early as a great deal of time and effort is required. The payback is reduced downtime and smooth systems operation.
Human-Machine Interface (HMI): Designing intuitive and user-friendly interfaces makes it easier for operators to control and monitor systems. This can reduce the likelihood of operator errors and improve overall system efficiency. Providing real-time feedback and alerts to operators allows for quick responses to issues. This can include notifications about performance deviations, maintenance needs, or system faults.
Automation and robotics optimization
Path Optimization: It is important that path creation is done by experts. However, in many robotic systems, there will still be room for improvement. Optimizing the movement paths can significantly reduce cycle times and energy consumption. This involves programming robots to take the most efficient routes. Using joint moves can be faster then calculated linear or curved routes. However, creating intermediate points can sometimes force a robot to behave less erratically.
Cycle Time Reduction: Streamlining operations to reduce the time taken for each cycle of operation increases overall throughput. This can involve optimizing tool changes, reducing setup times, and eliminating redundant steps. The goal is to minimize unnecessary movements and dwells times to reduce non-value added motion.
Continuous improvement and lean methodologies
Many companies follow specific techniques to refine processes. Regardless of the methodology employed, most can be used for both optimization and continuous improvement. It should be noted that these are only tool to effectively create positive change but specific expertise in the specific method is necessary. A culture of improvement is a tremendous benefit and should not be discounted. Some examples are:
Kaizen: Implementing a culture of continuous improvement, known as Kaizen, encourages regular evaluation and enhancement of processes. This approach focuses on making small, incremental changes that collectively lead to significant improvements. The process is allowed to stabilize before moving onto the next development opportunity. Since this approach represents a culture, it does not matter if the target is quality, maintenance, cycle time, operation or some other enhancement.
Six Sigma: Utilizing Six Sigma methodologies helps reduce process variation and eliminate defects. This is a data driven focused process using a statistical approach in decision-making to improve process quality and efficiency. Although this method is mainly targeted to process improvements that effect quality, a thorough analysis of data can lead to discoveries in many areas that can be a benefit.
By advance planning and the careful implementation of some of these strategies, organizations can achieve significant improvements in the performance, efficiency, and reliability of their automation systems and associated processes. A holistic approach, involuting multiple tools and a variety of personnel can enhance productivity and minimize waste.
