Milling Tool Wear Prediction by Feed Motor Current Signal using MLPs and ANFIS

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Nasernia e.; NOURI KHAJAVI M.; REZAEE M.

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Journal Paper

Paper Information

Journal: Journal of Aerospace Mechanics Year:2019 | Volume:15 | Issue:1 (55) Page(s): 51-62

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Title

Milling Tool Wear Prediction by Feed Motor Current Signal using MLPs and ANFIS

Author(s)

Nasernia e. | NOURI KHAJAVI M. | REZAEE M. | Issue Writer Certificate

Keywords

Tool Condition MonitoringQ2

Tool WearQ2

Motor CurrentQ1

Multi-Layer Neural NetworksQ1

Adaptive Neuro-Fuzzy Inference SystemQ1

Abstract

The cutting Tool Wear degrades the quality, reliability and productivity of the product in the manufacturing process. Accordingly, an on-line monitoring of the cutting Tool Wear level is essential to prevent any deterioration. Unfortunately, there is no direct method to measure the cutting Tool Wear on-line. Consequently, an indirect method can be adopted where wear will be estimated from the measurement of one or more physical parameters appearing during the machining process such as vibrations, electrical current, cutting force, etc. In this paper, two techniques namely Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multi-Layer Perceptron (MLP) have been used for prediction of Tool Wear in face milling. For this purpose, a series of experiment is carried out on a milling machine. It is observed that there was an increase in the current amplitude with increasing the Tool Wear. Besides, the effects of Tool Wear, feed, and depth of cut on the current are analyzed. Comparison of the Tool Wear detection techniques shows 92% of correct Tool Wear detection for ANFIS and 84% for MLP. As a result, ANFIS can be proposed as proper technique for intelligent fault detection of the Tool Wear and breakage due to its high efficiency in diagnosing wear and tool breakage.

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APA: Copy

Nasernia, e., NOURI KHAJAVI, M., & REZAEE, M.. (2019). Milling Tool Wear Prediction by Feed Motor Current Signal using MLPs and ANFIS. AEROSPACE MECHANICS JOURNAL, 15(1 (55) ), 51-62. SID. https://sid.ir/paper/102064/en

Vancouver: Copy

Nasernia e., NOURI KHAJAVI M., REZAEE M.. Milling Tool Wear Prediction by Feed Motor Current Signal using MLPs and ANFIS. AEROSPACE MECHANICS JOURNAL[Internet]. 2019;15(1 (55) ):51-62. Available from: https://sid.ir/paper/102064/en

IEEE: Copy

e. Nasernia, M. NOURI KHAJAVI, and M. REZAEE, “Milling Tool Wear Prediction by Feed Motor Current Signal using MLPs and ANFIS,” AEROSPACE MECHANICS JOURNAL, vol. 15, no. 1 (55) , pp. 51–62, 2019, [Online]. Available: https://sid.ir/paper/102064/en

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