Tool wear classification using time series imaging and deep learning

• Published in: International journal of advanced manufacturing technology 2019-10, Vol.104 (9-12), p.3647-3662
• Main Authors: Martínez-Arellano, Giovanna, Terrazas, German, Ratchev, Svetan
• Format: Article
• Language: English
• Subjects: CAE) and Design; Computer-Aided Engineering (CAD; Condition monitoring; Cutting tools; Deep learning; Engineering; Imaging; Industrial and Production Engineering; Machine learning; Machining; Mechanical Engineering; Media Management; Original Article; Signal classification; Time series; Tool wear
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-019-04090-6

Tool condition monitoring (TCM) has become essential to achieve high-quality machining as well as cost-effective production. Identification of the cutting tool state during machining before it reaches its failure stage is critical. This paper presents a novel big data approach for tool wear classification based on signal imaging and deep learning. By combining these two techniques, the approach is able to work with the raw data directly, avoiding the use of statistical pre-processing or filter methods. This aspect is fundamental when dealing with large amounts of data that hold complex evolving features. The imaging process serves as an encoding procedure of the sensor data, meaning that the original time series can be re-created from the image without loss of information. By using an off-the-shelf deep learning implementation, the manual selection of features is avoided, thus making this novel approach more general and suitable when dealing with large datasets. The experimental results have revealed that deep learning is able to identify intrinsic features of sensory raw data, achieving in some cases a classification accuracy above 90%.