Industry and Production
The research area Industry and Production deals with the research and integration of Machine Learning (ML) and Artificial Intelligence (AI) in production technology.
In addition to the production of components in the specified quality as an objective, the focus in the production technology environment is on various resources that need to be minimized. These include, for example, the reduction of machine times and costs for tools, workpieces, and consumption of energy. Traditionally, technological investigations or simulation-based approaches are often pursued in order to optimize process development and operations in a resource-oriented manner.
Machine Learning for Economic and Sustainable Production Processes
ML is a rapidly evolving field that is transforming the way complex systems are analyzed and understood by automating the recognition of significant patterns and relationships from large data sets. Therefore, incorporating ML into manufacturing engineering can significantly improve the competitiveness and sustainability of production processes. Specifically, by integrating ML methods, it is possible to achieve predictions with a high degree of generalization that could not be achieved through technological or simulative approaches.
Within the Industry and Production research area, the focus is therefore particularly on the combination of process data, simulations and ML methods in the context of hybrid learning using the example of different production processes in order to minimize the experimental effort required to analyze and model process characteristics. In addition to simulations, generative modeling methods are also being investigated for augmenting the data sets with additional synthetic data. This is particularly advantageous to reduce the manual annotation effort required to create the labels, which is necessary in order to be able to carry out supervised learning.
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In-process Optimization Based on Data, Knowledge and Context
The research area is based on triangular AI as the overarching strategic direction of research at the Lamarr Institute by combining data-based observations with knowledge from physical and expert-based domain knowledge in a specific production engineering context, such as wear prediction for machines or tools. This also addresses tangential challenges, such as concept drift, which result from changing process conditions and data.
The scientific investigations in the Industry and Production research area are intended to make a significant contribution to a vision in which process characteristics and states in the production technology landscape are automatically analyzed in order to derive process-related optimization recommendations and continuously improve learned models with new data obtained during operation.