S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

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Sensors and Materials, Volume 33, Number 6(1) (2021)
Copyright(C) MYU K.K.
pp. 1859-1867
S&M2579 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2021.3243
Published: June 1, 2021

Estimation of Screw’s Physical Properties Using Neural Network [PDF]

Nan Hua Lu, Huang-Chu Huang, Shan-Jun Wu, and Rey-Chue Hwang

(Received December 30, 2020; Accepted March 19, 2021)

Keywords: estimation, screw’s physical properties, neural network, heat treatment, spheroidization

In this paper, the estimation of a screw’s physical properties using a neural network (NN) technique is presented. The aim of this research is to study the effects of various control parameters of heat treatment and spheroidization on the physical properties of an alloy steel wire in its manufacturing process. The NN model is used to analyze the data collected by the image sensor and temperature sensor for heating treatments of alloy steel wire. It is expected that an advanced screw manufacturing system with intelligent analysis ability can be developed. Then, this smart system will be able to provide the optimal control parameters in real time to produce an alloy steel wire with ideal physical properties so that high-quality screws can be produced in the later manufacturing process. The results of this study show that the NN model can indeed achieve a fairly accurate estimation of the physical properties of a steel wire after the spheroidization, quenching, and tempering heat treatments. This shows that the development of an artificial-intelligence-based screw process optimization mechanism is very feasible.

Corresponding author: Rey-Chue Hwang


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Cite this article
Nan Hua Lu, Huang-Chu Huang, Shan-Jun Wu, and Rey-Chue Hwang, Estimation of Screw’s Physical Properties Using Neural Network, Sens. Mater., Vol. 33, No. 6, 2021, p. 1859-1867.



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