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Research Papers

Audio-Based Tool Condition Monitoring in Milling of the Workpiece Material With the Hardness Variation Using Support Vector Machines and Convolutional Neural Networks

[+] Author and Article Information
Achyuth Kothuru

Rochester Institute of Technology,
One Lomb Memorial Dr,
Rochester, NY 14623
e-mail: axk5592@rit.edu

Sai Prasad Nooka

Rochester Institute of Technology,
One Lomb Memorial Dr,
Rochester, NY 14623
e-mail: nooka.saiprasad@gmail.com

Rui Liu

Rochester Institute of Technology,
One Lomb Memorial Dr,
Rochester, NY 14623
e-mail: rleme@rit.edu

1Corresponding author.

Manuscript received June 7, 2018; final manuscript received July 10, 2018; published online August 3, 2018. Editor: Y. Lawrence Yao.

J. Manuf. Sci. Eng 140(11), 111006 (Aug 03, 2018) (9 pages) Paper No: MANU-18-1416; doi: 10.1115/1.4040874 History: Received June 07, 2018; Revised July 10, 2018

Machining industry has been evolving toward implementation of automation into the processes for higher productivity and efficiency. Although many studies have been conducted in the past to develop intelligent monitoring systems in various application scenarios of machining processes, most of them just focused on cutting tools without considering the influence of the nonuniform hardness of workpiece material. This study develops a compact, reliable, and cost-effective tool condition monitoring (TCM) system to detect the cutting tool wear in machining of the workpiece material with hardness variation. The generated audible sound signals during the machining process are analyzed by state-of-the-art artificial intelligent techniques, support vector machine (SVM) and convolutional neural network (CNN), to predict the tool wear and the hardness variation of the workpiece. A four-level classification model is developed for the system to detect the tool wear condition based on the width of the flank wear land and the hardness variation of the workpiece. This study also involves the comparative analysis between two employed artificial intelligent techniques to evaluate the performance of the model in prediction. The proposed TCM system has shown a high prediction accuracy in detecting the tool wear from the audible sound into the proposed multiclassification wear level in end milling of the nonuniform hardened workpiece.

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Figures

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Fig. 1

Types of flank wear: VB 1—uniform flank wear; VB 2—nonuniform flank wear; and VB 3—localized flank wear (ISO 8688)

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Fig. 2

Cutting zone of end-milling tool

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Fig. 3

Hardness variation of workpiece

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Fig. 4

Experimental setup of data acquisition

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Fig. 5

Schematic diagram of experimental setup

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Fig. 6

Left illustrates a regular three-layer multilayer perceptron and right illustrates a CNN arrangement of neurons

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Fig. 7

CNN architecture flow-chart

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Fig. 8

Variable window length analysis for SVM: (a) tool wear prediction analysis and (b) workpiece hardness variation prediction analysis

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Fig. 9

Training data size variation of SVM and CNN for tool wear prediction analysis

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Fig. 10

Training data size variation of SVM and CNN for workpiece hardness variation prediction analysis

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