Estimation of Screw’s Physical Properties Using Neural Network

• Published in: Sensors and materials Vol. 33; no. 6; p. 1859
• Main Authors: Lu, Nan Hua, Huang, Huang-Chu, Wu, Shan-Jun, Hwang, Rey-Chue
• Format: Journal Article
• Language: English
• Published: Tokyo MYU Scientific Publishing Division 01.06.2021
• Subjects: Alloy steels; Heat treatment; Manufacturing; Mathematical models; Neural networks; Optimal control; Optimization; Parameters; Physical properties; Spheroidizing; Stability; Steel wire; Temperature sensors
• ISSN: 0914-4935; 2435-0869
• DOI: 10.18494/SAM.2021.3243

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.