Condition monitoring and life prediction of the turning tool based on extreme learning machine and transfer learning

• Published in: Neural computing & applications Vol. 34; no. 5; pp. 3399 - 3410
• Main Authors: Gao, Zhan, Hu, Qiguo, Xu, Xiangyang
• Format: Journal Article
• Language: English
• Published: London Springer London 01.03.2022; Springer Nature B.V
• Subjects: Accuracy; Artificial Intelligence; Artificial neural networks; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Condition monitoring; Costs; Data Mining and Knowledge Discovery; Domains; Efficiency; Failure; Feature extraction; Image Processing and Computer Vision; Life prediction; Machine learning; Machine tools; Neural networks; Prediction models; Probability and Statistics in Computer Science; Special Issue on Multi-modal Information Learning and Analytics on Big Data; Tool wear; Useful life; Workpieces; Condition monitoring; Life prediction; Turning tool; Extreme learning machine; Transfer learning

When the turning tool has worn and failed but the failure is not found, if it continues to be used for processing, it will break, and cause the workpiece to be scrapped, and even damage the machine tool. In order to avoid the loss caused by turning tool wear, the remaining useful life (RUL) predicti...