Young Researcher Paper Award 2021
🥇Winners

Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

Instructions to authors
English    日本語

Instructions for manuscript preparation
English    日本語

Template
English

Publisher
 MYU K.K.
 Sensors and Materials
 1-23-3-303 Sendagi,
 Bunkyo-ku, Tokyo 113-0022, Japan
 Tel: 81-3-3827-8549
 Fax: 81-3-3827-8547

MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.


MYU Research

(proofreading and recording)


MYU K.K.
(translation service)


The Art of Writing Scientific Papers

(How to write scientific papers)
(Japanese Only)

Sensors and Materials, Volume 34, Number 2(3) (2022)
Copyright(C) MYU K.K.
pp. 803-817
S&M2854 Research Paper of Special Issue
https://doi.org/10.18494/SAM3642
Published: February 28, 2022

Using Neural Networks for Tool Wear Prediction in Computer Numerical Control End Milling [PDF]

Cheng-Hung Chen, Shiou-Yun Jeng, and Cheng-Jian Lin

(Received September 6, 2021; Accepted January 4, 2022)

Keywords: backpropagation neural network, tool wear prediction, linear regression, machine tool, milling

The precision of the machining tool in computer numerical control (CNC) machining is affected by several factors. For example, cutting parameters considerably affect machining accuracy and tool wear. Tool wear results in the manufacture of substandard products. Therefore, predicting tool wear is crucial in CNC machining. In this study, we proposed a backpropagation neural network (BPNN) to predict tool wear. In machine learning, backpropagation is a widely used algorithm for training artificial neural networks. The proposed BPNN considered the variation of tool wear with different cutting parameters, such as the spindle speed, feed, cutting depth, and cutting time. The experimental results revealed that the root mean square error of the BPNN prediction model was less than that of the linear regression prediction model. Furthermore, the proposed model achieved a coefficient of determination (R2) of 0.9964, which indicated that the BPNN model can accurately predict tool wear.

Corresponding author: Cheng-Jian Lin


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Cheng-Hung Chen, Shiou-Yun Jeng, and Cheng-Jian Lin, Using Neural Networks for Tool Wear Prediction in Computer Numerical Control End Milling, Sens. Mater., Vol. 34, No. 2, 2022, p. 803-817.



Forthcoming Regular Issues


Forthcoming Special Issues

Special Issue on Collection, Processing, and Applications of Measured Sensor Signals
Guest editor, Hsiung-Cheng Lin (National Chin-Yi University of Technology)


Special Issue on Advanced Materials and Sensing Technologies on IoT Applications: Part 4-3
Guest editor, Teen-Hang Meen (National Formosa University), Wenbing Zhao (Cleveland State University), and Cheng-Fu Yang (National University of Kaohsiung)


Special Issue on IoT Wireless Networked Sensing for Life and Safety
Guest editor, Toshihiro Itoh (The University of Tokyo) and Jian Lu (National Institute of Advanced Industrial Science and Technology)
Call for paper


Special Issue on the International Multi-Conference on Engineering and Technology Innovation 2021 (IMETI2021)
Guest editor, Wen-Hsiang Hsieh (National Formosa University)
Conference website


Special Issue on Biosensors and Biofuel Cells for Smart Community and Smart Life
Guest editor, Seiya Tsujimura (University of Tsukuba), Isao Shitanda (Tokyo University of Science), and Hiroaki Sakamoto (University of Fukui)
Call for paper


Special Issue on Novel Sensors and Related Technologies on IoT Applications: Part 1
Guest editor, Teen-Hang Meen (National Formosa University), Wenbing Zhao (Cleveland State University), and Cheng-Fu Yang (National University of Kaohsiung)
Call for paper


Copyright(C) MYU K.K. All Rights Reserved.