A tool wear monitoring and prediction system based on multiscale deep learning models and fog computing

• Published in: International journal of advanced manufacturing technology Vol. 108; no. 7-8; pp. 2367 - 2384
• Main Authors: Qiao, Huihui, Wang, Taiyong, Wang, Peng
• Format: Journal Article
• Language: English
• Published: London Springer London 01.06.2020; Springer Nature B.V
• Subjects: Accuracy; CAE) and Design; Cloud computing; Computer architecture; Computer-Aided Engineering (CAD; Condition monitoring; Data storage; Deep learning; Edge computing; Engineering; Industrial and Production Engineering; Intelligent manufacturing systems; Machine learning; Mechanical Engineering; Media Management; Network latency; Original Article; Real time; Response time; Software; Tool wear; Twist drills; Deep learning; Fog computing; Long short-term memory network (LSTM); Multiscale feature learning; Convolutional neural network (CNN); Tool condition monitoring
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-020-05548-8

Tool condition monitoring (TCM) during the manufacturing process is of great significance for ensuring product quality and plays an important role in intelligent manufacturing. Current TCM systems deployed in the local device or cloud computing environment unable meet the requirements of low response latency and high accuracy at the same time. The emerging fog computing provides new solutions for the above problem. This paper presents a tool wear monitoring and prediction (TWMP) system based on deep learning models and fog computing. In order to improve monitoring and prediction accuracy, we propose a multiscale convolutional long short-term memory model (MCLSTM) to complete the tool wear monitoring task and a bi-directional LSTM model (BiLSTM) to complete the tool wear prediction task. To reduce the response latency of the TWMP system, we deploy the MCLSTM model and the BiLSTM model in a fog computing architecture. The fog computing architecture consists of an edge computing layer, a fog computing layer, and a cloud computing layer. The edge computing layer undertakes real-time signal collection task. The fog computing layer undertakes real-time tool wear monitoring task. The cloud computing layer with powerful computing resources undertakes intensive computing and latency-insensitive tasks such as data storage, tool wear prediction, and model training. A twist drill wear monitoring and prediction experiment is conducted to test the performance of the proposed system in terms of accuracy, response time, and network bandwidth consumption.