Similarity-search and Prediction Based Process Parameter Adaptation for Quality Improvement in Interlinked Manufacturing Processes
- Publication Type:
- Conference Proceeding
- IEEE International Conference on Industrial Engineering and Engineering Management, 2019, 2019-December, pp. 700-704
- Issue Date:
|Schmitt Deuse 2018 - Similarity-search and Prediction Based Process Par.._.pdf||Published version||103.92 kB|
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© 2018 IEEE. Due to the steadily increasing global competition, manufacturing companies are forced to constantly improve their products and processes. In this context, real-time process adaptation based on inline quality monitoring using predictive data mining techniques presents a promising approach to sustainably increase manufacturing process efficiency and improve product quality. This paper presents an approach to improve process and product quality in manufacturing through process parameter adaptations utilizing quality prediction models and similarity search algorithms. The approach enables a data-driven decision support for process control in interlinked manufacturing processes.
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