Online quantitative monitoring of milling cutter health condition based on deep convolutional autoencoder

The health condition of milling cutters (HCOMC) could heavily affect workpiece quality. However, it is extremely difficult to be quantified online. To solve this problem, an online quantitative monitoring method (OQM) is proposed based on a deep convolutional autoencoder (CAE). In this method, a hea...

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Veröffentlicht in:	International journal of advanced manufacturing technology Jg. 125; H. 9-10; S. 4739 - 4752
Hauptverfasser:	Lei, Yuncong, Li, Changgen, Guo, Liang, Gao, Hongli, Liang, Junhua, Sun, Yi, He, Jigang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.04.2023 Springer Nature B.V
Schlagworte:	Adhesive wear Advanced manufacturing technologies CAE) and Design Classification Computer-Aided Engineering (CAD Decision trees Engineering Fault diagnosis Industrial and Production Engineering Manufacturing Mechanical Engineering Media Management Methods Milling cutters Original Article Representations Sensors Tool changing Wear Workpieces Milling cutters Online quantitative monitoring Convolutional autoencoder Fusion metric Health indicator
ISSN:	0268-3768, 1433-3015
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Abstract	The health condition of milling cutters (HCOMC) could heavily affect workpiece quality. However, it is extremely difficult to be quantified online. To solve this problem, an online quantitative monitoring method (OQM) is proposed based on a deep convolutional autoencoder (CAE). In this method, a health indicator (HI) is constructed for fast HCOMC monitoring. The OQM is composed of two parts, offline training and online monitoring. In the offline stage, the multi-sensor monitoring data that record in the cutter normal wear stage (named normal wear data, NWD) are selected from a subsampled life testing dataset to train a deep CAE. In the online stage, each monitoring data segment (MDS) is directly input into the trained CAE to obtain deep representations. Then, the HI is constructed by the mean square error (MSE) between the MDS and the deep representations to monitor the HCOMC. It is called convolutional-autoencoder-reconstruction-error-based health indicator (CARE-HI). In addition to the above-mentioned method, a new metric named isometric fusion metric (IFM) is also designed to assess HI. IFM is able to address the uneven problem of property contribution when using some widely used HI metrics. In the experiment, 28 milling cutters were subjected to cutting experiments under different working conditions. The experimental result demonstrates that the proposed OQM can efficiently improve feature quality and precisely monitor HCOMC. It also illustrates that the CARE-HI outperformed some existing ones in five metric dimensions. Therefore, the proposed CARE-HI can provide more accurate guidance for tool changing in machining.
AbstractList	The health condition of milling cutters (HCOMC) could heavily affect workpiece quality. However, it is extremely difficult to be quantified online. To solve this problem, an online quantitative monitoring method (OQM) is proposed based on a deep convolutional autoencoder (CAE). In this method, a health indicator (HI) is constructed for fast HCOMC monitoring. The OQM is composed of two parts, offline training and online monitoring. In the offline stage, the multi-sensor monitoring data that record in the cutter normal wear stage (named normal wear data, NWD) are selected from a subsampled life testing dataset to train a deep CAE. In the online stage, each monitoring data segment (MDS) is directly input into the trained CAE to obtain deep representations. Then, the HI is constructed by the mean square error (MSE) between the MDS and the deep representations to monitor the HCOMC. It is called convolutional-autoencoder-reconstruction-error-based health indicator (CARE-HI). In addition to the above-mentioned method, a new metric named isometric fusion metric (IFM) is also designed to assess HI. IFM is able to address the uneven problem of property contribution when using some widely used HI metrics. In the experiment, 28 milling cutters were subjected to cutting experiments under different working conditions. The experimental result demonstrates that the proposed OQM can efficiently improve feature quality and precisely monitor HCOMC. It also illustrates that the CARE-HI outperformed some existing ones in five metric dimensions. Therefore, the proposed CARE-HI can provide more accurate guidance for tool changing in machining.
Author	He, Jigang Gao, Hongli Liang, Junhua Lei, Yuncong Guo, Liang Sun, Yi Li, Changgen
Author_xml	– sequence: 1 givenname: Yuncong surname: Lei fullname: Lei, Yuncong organization: School of Mechanical Engineering, Southwest Jiaotong University, Engineering Research Center of Advanced Drive Energy saving Technologies, Ministry of Education, Southwest Jiaotong University – sequence: 2 givenname: Changgen surname: Li fullname: Li, Changgen organization: School of Mechanical Engineering, Southwest Jiaotong University, Engineering Research Center of Advanced Drive Energy saving Technologies, Ministry of Education, Southwest Jiaotong University – sequence: 3 givenname: Liang surname: Guo fullname: Guo, Liang email: guoliang@swjtu.edu.cn organization: School of Mechanical Engineering, Southwest Jiaotong University, Engineering Research Center of Advanced Drive Energy saving Technologies, Ministry of Education, Southwest Jiaotong University – sequence: 4 givenname: Hongli surname: Gao fullname: Gao, Hongli organization: School of Mechanical Engineering, Southwest Jiaotong University, Engineering Research Center of Advanced Drive Energy saving Technologies, Ministry of Education, Southwest Jiaotong University – sequence: 5 givenname: Junhua surname: Liang fullname: Liang, Junhua organization: School of Mechanical Engineering, Southwest Jiaotong University, Engineering Research Center of Advanced Drive Energy saving Technologies, Ministry of Education, Southwest Jiaotong University – sequence: 6 givenname: Yi surname: Sun fullname: Sun, Yi organization: School of Mechanical Engineering, Southwest Jiaotong University, Engineering Research Center of Advanced Drive Energy saving Technologies, Ministry of Education, Southwest Jiaotong University – sequence: 7 givenname: Jigang surname: He fullname: He, Jigang organization: School of Mechanical Engineering, Southwest Jiaotong University, Engineering Research Center of Advanced Drive Energy saving Technologies, Ministry of Education, Southwest Jiaotong University
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Snippet	The health condition of milling cutters (HCOMC) could heavily affect workpiece quality. However, it is extremely difficult to be quantified online. To solve...
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SubjectTerms	Adhesive wear Advanced manufacturing technologies CAE) and Design Classification Computer-Aided Engineering (CAD Decision trees Engineering Fault diagnosis Industrial and Production Engineering Manufacturing Mechanical Engineering Media Management Methods Milling cutters Original Article Representations Sensors Tool changing Wear Workpieces
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Title	Online quantitative monitoring of milling cutter health condition based on deep convolutional autoencoder
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