Fraunhofer Institute for Manufacturing Engineering and Automation IPA
AI-supported parameter optimization for machines and systems
For high product quality and productivity of industrial systems, the optimal parameter setting often involves complex and time-consuming manual effort. Different orders, materials or environmental conditions require regular adjustments. Traditional optimization approaches for machine parameters are characterized by high manual effort, long machine downtimes and a lack of scalability. In addition, domain experts and specially trained employees are usually not available around the clock.
AI-supported parameter optimization offers decisive advantages here: faster adaptation through optimized test loops, autonomous parameter settings based on learned patterns and high efficiency even with small amounts of data. This significantly shortens optimization cycles and reduces set-up times and rejects. Our AI-supported parameter optimization helps both machine manufacturers and plant operators.
Value proposition
Our solution enables fast and precise optimization of machine parameters with minimal use of training data. Machine manufacturers can offer their customers significant added value by enabling faster commissioning and improved process stability. Plant operators benefit from reduced downtimes, fewer rejections and optimized production output. The ML models are flexibly transferable to different machines, materials or geometries.
Investment and scope of services
Depending on the individual requirements, our solution can range from the sole support of model development to the integrated provision of a supporting assistance system for parameter optimization. You receive the following services from 55,000 euros:
- Analysis and needs assessment: Identification of relevant process parameters, evaluation of the existing database, development of an individual optimization strategy.
- ML model development: training of models with minimal data input.
- Piloting and validation: Test run and improvement of the ML models in a few iterations in real production environments.