Predicting tool wear with multi-sensor data using deep belief networks

doi:10.1007/s00170-018-2571-z

CIOMP OpenIR

	Predicting tool wear with multi-sensor data using deep belief networks
	Chen, Y. X.; Jin, Y.; Jiri, G.
	2018
发表期刊	International Journal of Advanced Manufacturing Technology
ISSN	0268-3768
卷号	99 期号:2019-05-08 页码:1917-1926
摘要	Tool wear is a crucial factor influencing the quality of workpieces in the machining industry. The efficient and accurate prediction of tool wear can enable the tool to be changed in a timely manner to avoid unnecessary costs. Various parameters, such as cutting force, vibration, and acoustic emission (AE), impact tool wear. Signals are collected by different sensors and then constitute the raw data. There are two main types of methods used to make predictions, namely model-based and data-driven methods. Data-driven methods are typically preferred when a mathematical model is not available. In such a situation, artificial intelligent methods, such as support vector regression (SVR) and artificial neural networks (ANNs), are applied. Recently, deep learning algorithms have been widely used because of their accuracy, computing speed, and excellent performance in solving nonlinear problems. In this study, a deep learning network called deep belief network (DBN) is applied to predict the flank wear of a cutting tool. To confirm the superiority of the DBN in predicting tool wear, the performance of the DBN is compared with the performances obtained using ANNs and SVR in terms of the mean-squared error (MSE) and the coefficient of determination (R-2), considering data from more than 900 experiments.
关键词	Tool wear prediction Deep belief network Support vector regression Artificial neural network neural-networks diagnosis state model optimization prognostics machinery algorithm filter Automation & Control Systems Engineering
DOI	10.1007/s00170-018-2571-z
收录类别	SCI ; EI
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/61113
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	Chen, Y. X.,Jin, Y.,Jiri, G.. Predicting tool wear with multi-sensor data using deep belief networks[J]. International Journal of Advanced Manufacturing Technology,2018,99(2019-05-08):1917-1926.
APA	Chen, Y. X.,Jin, Y.,&Jiri, G..(2018).Predicting tool wear with multi-sensor data using deep belief networks.International Journal of Advanced Manufacturing Technology,99(2019-05-08),1917-1926.
MLA	Chen, Y. X.,et al."Predicting tool wear with multi-sensor data using deep belief networks".International Journal of Advanced Manufacturing Technology 99.2019-05-08(2018):1917-1926.

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