Indirect cutting tool wear classification using deep learning and chip colour analysis
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ORIGINAL ARTICLE
Indirect cutting tool wear classification using deep learning and chip colour analysis Luca Pagani1 · Paolo Parenti2 · Salvatore Cataldo2 · Paul J. Scott1 · Massimiliano Annoni2 Received: 6 May 2020 / Accepted: 7 September 2020 / Published online: 7 October 2020 © The Author(s) 2020
Abstract In the growing Industry 4.0 market, there is strong need to implement automatic inspection methods to support manufacturing processes. Tool wear in turning is one of the biggest concerns that most expert operators are able to indirectly infer through the analysis of the removed chips. Automatising this operation would enable developing more efficient cutting processes that turns in easier process planning management toward the Zero Defect Manufacturing paradigm. This paper presents a deep learning approach, based on image processing applied to turning chips for indirectly identifying tool wear levels. The procedure extracts different indicators from the RGB and HSV image channels and instructs a neural network for classifying the chips, based on tool state conditions. Images were collected with a high-resolution digital camera during an experimental cutting campaign involving tool wear analysis with direct microscope imaging. The sensitivity analysis confirmed that the most sensible image channels are the hue value H that were used to teach the network, leading to performances in the range of 95 of proper classification. The feasibility of the deep learning approach for indirectly understanding the tool wear from the chip colour characterisation is confirmed. However, due to the big effects on chip colours of variables as the workpiece material and cutting process parameters, the applicability is limited to stable production flows. An industrial implementation can be foreseen by populating proper large databases and by implementing real-time chip segmentation analysis. Keywords Image processing · Vision inspection · Deep learning · Tool wear · Chip analysis · Monitoring
1 Highlights • • • •
A deep learning network is implemented to classify turning tool wear The procedure is based on chip colour analysis The method can be implemented online The approach supports automatic management of tool wear
2 Introduction Minimising the number of defects in manufactured products is fundamental for production nowadays, toward the implementation of the Zero Defect Manufacturing (ZDM)
Paolo Parenti
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Extended author information available on the last page of the article.
paradigm [1]. A quick and real-time monitoring of turning tool wear becomes therefore extremely important to keep the production under control, even during unmanned operations [2]. Since the olden times, one of the biggest issues in machining industry is tool wear. Keeping tools in good conditions is a fundamental practice to enable optimal production. The implementation of tool wear identification and monitoring strategies is therefore a key aspect. The most reliable, but less efficient, methods are based on the direct observatio
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