Artificial intelligence is fundamentally changing the manufacturing industry and driving it towards a smarter and more efficient future. Against this backdrop, academic Li Peigen, a member of the Chinese Academy of Engineering, has highlighted several key trends for “AI+manufacturing” and provided valuable recommendations for companies in the manufacturing sector.
Digital twins: the key to process optimization
Academician Li emphasized the central importance of digital twin technology in the manufacturing process. He emphasized that companies should not focus solely on products, but rather need to understand the entire process.
Real-time optimization and error detection
Li explained that even identical machine tools can exhibit varying operating states at different moments. By using sensors and digital twins, companies can analyze machine data in real time – comparable to an electrocardiogram. In this way, potential problems can be identified at an early stage.
Improving product development
The technology of digital twins not only offers real-time optimization, but also enables the analysis of individual process data. This opens up new insights into complex relationships and universal laws, which in turn can drive product development forward.
Recognizing complex data correlations: AI enriches manufacturing intelligence
In manufacturing, there are many data relationships that have received little attention to date – from material properties such as thermal, mechanical and chemical attributes to equipment and environmental factors. Li Peigen made it clear that conventional methods are often not sufficient to recognize these relationships. Artificial intelligence closes this gap.
Example: From apple properties to material characteristics
Li illustrated the potential of AI with a comparison: a system like ChatGPT can analyze the characteristics of hundreds of apple varieties based on different varieties and growing regions – far beyond what human intuition can do. Similarly, AI can analyse material, equipment and process characteristics in manufacturing to open up new optimization opportunities.
Data analysis in forming technology
In forming technology, material, equipment and environmental variables interact in complex ways. With the help of AI and big data, companies can better understand these interactions and make informed decisions for process optimization and quality assurance.
Robots and intelligent agents: Applications in manufacturing
Li pointed out that industrial robots should not only be thought of as humanoid systems. Non-humanoid robots such as transport robots can be more efficient and practical in many scenarios, for example in warehousing and production.
Intelligent agents: Multi-purpose helpers of the future
Li also predicted that intelligent agents that take on specific tasks will become increasingly important in companies. These systems could be used in areas such as design, process optimization, quality analysis, maintenance and marketing. Personalized AI agents could be developed for engineers and managers that not only replicate their expertise, but also extend it to support decision-making.
Preparing for the future: recommendations for companies
Li Peigen emphasized that many of the concepts presented cannot yet be implemented across the board. Nevertheless, companies should keep an eye on these trends and – where possible – launch pilot projects, for example to introduce digital twins or AI-supported analysis systems. This would pave the way for the successful integration of “AI+manufacturing”.
Conclusion
Academic Li Peigen’s perspectives offer companies in the manufacturing sector valuable insights into intelligent transformation. From real-time optimization through digital twins and the recognition of complex data relationships to the versatile application of robots and intelligent agents, “AI+manufacturing” is increasingly becoming a reality. Companies that adopt these technologies at an early stage can gain a decisive competitive advantage in a changing industry.