Towards Material-Batch-Aware Tool Condition Monitoring

Journal of Manufacturing and Materials Processing

Philip Howell¹  Daniel Regulin¹  Benjamin Lutz¹  Bastian Engelmann²  Jörg Franke³ 

[1] Functional Materials and Manufacturing Processes, Technology Department, Siemens AG, 81739 Munich, Germany;Institute Digital Engineering (IDEE), University of Applied Sciences Würzburg-Schweinfurt, 97421 Schweinfurt, Germany;Institute for Factory Automation and Production Systems (FAPS), Friedrich-Alexander University Erlangen-Nuremberg (FAU), 90429 Nuremberg, Germany;

关键词: material identification;    tool condition monitoring;    machining;    turning;    unsupervised learning;    material batches;   

DOI  :  10.3390/jmmp5040103

来源: DOAJ

【 摘 要 】

In subtractive manufacturing, process monitoring systems are used to observe the manufacturing process, to predict maintenance actions and to suggest process optimizations. One challenge, however, is that the observable signals are influenced not only by the degradation of the cutting tool, but also by deviations in machinability among material batches. Thus it is necessary to first predict the respective material batch before making maintenance decisions. In this study, an approach is shown for batch-aware tool condition monitoring using feature extraction and unsupervised learning to analyze high-frequency control data in order to detect clusters of materials with different machinability, and subsequently optimize the respective manufacturing process. This approach is validated using cutting experiments and implemented as an edge framework.

【 授权许可】

Unknown