AI-based optimisation of total machining performance: A review

Katrin Ullrich, Magnus von Elling, Kevin Gutzeit, Martin Dix, Matthias Weigold, Jan C. Aurich, Rafael Wertheim, I. S. Jawahir, Hassan Ghadbeigi

Research output: Contribution to journalArticlepeer-review

Abstract

Advanced modelling and optimisation techniques have been widely used in recent years to enable intelligent manufacturing and digitalisation of manufacturing processes. In this context, the integration of artificial intelligence in machining provides a great opportunity to enhance the efficiency of operations and the quality of produced components. Machine learning methods have already been applied to optimise various individual objectives concerning process characteristics, tool wear, or product quality in machining. However, the overall improvement of the machining process requires multi-objective optimisation approaches, which are rarely considered and implemented. The state-of-the-art in application of various optimisation and artificial intelligence methods for process optimisation in machining operations, including milling, turning, drilling, and grinding, is presented in this paper. The Milling process and deep learning are found to be the most widely researched operation and implemented machine learning technique, respectively. The surface roughness turns out to be the most critical quality measure considered. The different optimisation targets in artificial intelligence applications are elaborated and analysed to highlight the need for a holistic approach that covers all critical aspects of the machining operations. As a result, the key factors for a successful total machining performance improvement are identified and discussed in this paper. The AI methods were investigated and analysed in the frame of the IMPACT project initiated by the CIRP.

Original languageEnglish
Pages (from-to)40-54
Number of pages15
JournalCIRP Journal of Manufacturing Science and Technology
Volume50
DOIs
StatePublished - Jun 2024

Bibliographical note

Publisher Copyright:
© 2024 The Authors

Keywords

  • Artificial intelligence
  • Machining performance
  • Multi-objective optimisation

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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