AI-Assisted Manufacturing Higher Efficiency is Just A Few Data Points Away!

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Siemens Digital Industries Software GmbH

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With increasing digitalization in the industry, the implementation of AI in manufacturing is just a small step away—but one that offers great potential. In combination with the digital twin, Siemens sees this as both a tool for quality improvement and more efficient work preparation.

As digitization continues to gain momentum across all industries, data is becoming the lifeline of modern manufacturing. Today, on the verge of the AI revolution, this is even more true. Enormous amounts of data are generated in many parts of the manufacturing process, already being utilized in various ways. However, the sheer volume of such data also means that many optimization opportunities and important insights remain unused.

It is often feared that artificial intelligence (AI) could make workers obsolete, but its use in part manufacturing does not mean that humans and processes are "automated away." On the contrary, AI acts more as an "amplifier" that complements existing systems, thereby improving efficiency and productivity. One example of this is the co-pilot in a computer-aided manufacturing (CAM) system. It can automatically suggest tool paths by analyzing the 3D model of a component, using the capabilities of advanced software. The combination of traditional manufacturing processes with intelligent data collection, AI, and the comprehensive digital twin is a crucial step toward the next generation of data-driven manufacturing.

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The Need for Industrial-Grade AI

The use of AI in parts manufacturing offers many advantages but should be approached with caution. In consumer applications, an occasional error or hallucination by artificial intelligence may be acceptable—but in industry, where vast sums of money and even human lives may be at stake, a production error can have catastrophic consequences.

If the industry wants to benefit from the advantages of AI, it must deploy industrial-grade AI. The answers provided by a model must therefore be robust, reliable, and repeatable; users should not have to question every result. Building industrial-grade AI encompasses many individual aspects, such as

• a testing framework for continuously verifying whether the models are still delivering plausible results,
• automatic testing routines to ensure correctness, as well as
• software that involves humans in critical tasks within the process.

When a solid foundation for implementation is established, industrial-grade AI can be used in three ways to improve parts manufacturing: AI to optimize manufacturing processes, AI to analyze manufacturing data and workflows, and AI to increase production profitability.

AI Optimizes Manufacturing

Even today, AI can accelerate many processes in the machine shop or any manufacturing environment, save labor time and materials, and increase efficiency in production. It is now used in many areas, including:

• Natural language processing (NLP) for handling maintenance manuals, manufacturing data, and other tools such as the Industrial Copilot;
• Optimization of energy consumption by using existing data to examine savings potential across all production processes;
• AI-driven processing of CAM workflows for faster order completion.

These are just a few examples of how AI is already helping to make production more efficient. With further investments from companies in digitalization, the benefits of AI will also continue to increase.

Data Analysis for Greater Benefit

The combination of AI with data from manufacturing, design, and production enables optimizations through powerful analyses in all areas—from workflows to ergonomics. When all this information converges in solutions like the Siemens Insights Hub, AI can be applied to virtually anything: quality control reports, production plans in manufacturing, and more. A more thorough analysis thereby unlocks entirely new possibilities for streamlining processes.

AI makes a significant contribution to increasing production efficiency through predictive quality assurance. By analyzing defect data and correlating it with the manufacturing and performance data provided by intelligent machines, an AI model can be developed that detects signs of manufacturing errors at an early stage. Detecting such issues early enough saves time and materials, as the risk of defects in finished parts is reduced.

Increase Quality And Assess Risks

For example, so-called chatter during machining leads to substandard surfaces and reduces tool life. The result is chatter marks on the machined surface, often appearing as wave-like patterns or regular traces. Prolonged chatter can also lead to uneven wear or even tool breakage. AI algorithms can detect the occurrence of chatter by analyzing data from various sensors (vibrations, sound emissions, force, current) in real time. The machining parameters can then be adjusted immediately before the chatter becomes strong enough to affect the quality of the parts.

AI can not only process vast amounts of data but also significantly speed up the time-consuming analysis of data for specific use cases, such as improving ergonomics for human workers. Frequently performed movements can pose a physical strain, especially bending or gripping motions in awkward positions. While every person has a certain sense of the physical strain caused by repetitive movements, the long-term effects may be more difficult to assess. With an AI model trained on data about ergonomics and the human musculoskeletal system, the ergonomics of a specific motion can be evaluated based on a single image. AI-driven simulations of the human body allow effective analysis of high-risk scenarios. This information can be utilized in the comprehensive digital twin to more easily design a workplace that promotes health and enables efficient work by optimally arranging parts and tools.

Achieve Progress in Production

One of the newest and most well-known forms of artificial intelligence is generative AI, with its ability to communicate almost like a human. In the industry, it acts as a bridge between humans and technology, simplifying the use of complex tools. In the future, generative AI will be a central component of no- and low-code platforms, capable of programming complex machines using NLP.

An AI-powered co-pilot can also significantly accelerate the creation of CNC programs, the calculation of speeds and feeds, as well as the validation of tool paths. Currently, with CAM software, generating usable G-code from a 3D model is a complex and time-consuming process that requires extensive expertise in both CNC machining and the respective software.

It does not appear that the demand for CNC experts will decrease anytime soon. However, AI can speed up the work in the form of a CAM co-pilot by making tools more accessible and automating many labor-intensive manual steps. The CAM co-pilot can help automate the definition of machining processes for CNC machines, reducing programming time from hours to just minutes.

Once a feature in the 3D model is selected, the CAM co-pilot offers several combinations of work steps, tools, feed rates, etc., as suggestions. After the user approves one, it automatically transfers all the values of the chosen proposal into the software. At the same time, a co-pilot can learn to understand the characteristics of a manufacturing machine and immediately check whether a machine is suitable for a specific design and tool path and can produce the product safely.

Such generative AI tools can also serve as a kind of knowledge base that builds on the experience of qualified users and the data from previous workflows, providing best practices for the manufacturing environment. Through robust AI suitable for industrial use, a company also protects its own valuable expertise. This knowledge is just as easily accessible to new employees as it is to experienced staff and cannot be lost if someone changes jobs or retires.

Analyze, Optimize, And Generate With AI for Industrial Use

With the ongoing digitalization of manufacturing, it is becoming increasingly important for companies, regardless of their size, to use their data to achieve quality, sustainability, and efficiency goals. One thing is already clear today and will be even more so in the future: AI is becoming an increasingly essential tool for analyses, optimizations, and advancements in manufacturing. It offers everything—from simple analyses to fully integrated assistance functions—and will play a central role in building data-driven manufacturing. AI transforms dormant data, consisting of zeros and ones, into a goldmine for increasing efficiency across the entire company.

Rahul Garg is Vice President of Industrial Machinery at Siemens Digital Industries Software

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