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Benchmark Issue on Artificial Intelligence and Machine Learning
Home » Allgemein » Benchmark Issue on Artificial Intelligence and Machine Learning
2. January 2019 18:46

The third issue of the magazine “Benchmark”, the international magazine for engineering designers and analysts from NAFEMS, in 2018 is entitled “Artificial Intelligence & Machine Learning”. NAFEMS is the international association for the engineering modelling, analysis and simulation community, a not-for-profit organization established in 1983. Dr.-Ing. Volker Gravemeier and Dr.-Ing. Jonas Biehler of AdCo EngineeringGW are active members of the NAFEMS Uncertainty Quantification Working Group, which topics are tightly related to artificial intelligence and machine learning.

In his article on “The Applicability of Artificial Intelligence in Design and Manufacturing”, Phil Cartwright argues that the ability to harness data generated along the product lifecycle and feed this information back into the design and development process will lead to significant benefits. In the remainder of the article, an example for machine learning techniques in combination with state-of-the-art simulation approaches is provided, to reduce rework, scrap, and repair in a liquid composite moulding process. In this context, the machine learning approach results in improved tuning of the manufacturing process parameters and ultimately reduced manufacturing cost for the production company.

Combining techniques from the machine learning community with state-of-the-art simulation approaches is without a doubt a very promising way to go for improving efficiency and speed, which will eventually result in reduced costs. In our view, this does not only hold true for manufacturing processes, but also for the complete product development cycle in general.   This is why AdCo EngineeringGW develops novel approaches to fuse information from simulation models with experimental data through so-called multi-fidelity approaches. These approaches are powerful tools to identify, e.g., those production parameters with the highest impact on the reliability of a product or a component thereof, respectively, via sensitivity analysis.  Further product or system attributes can be assessed as well, and both production and design parameters may be investigated to substantially improve the results in the end.

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