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

S&M Young Researcher Paper Award 2021
Award Criteria
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.

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Metallographic Analysis of Spheroidization Using Deep Learning Neural Network

Rey-Chue Hwang, I-Chun Chen, and Huang-Chu Huang

(Received June 22, 2021; Accepted September 9, 2021)

Keywords: metallographic analysis, steel, spheroidization, deep learning, neural network

Spheroidization is a process that uses a high temperature to change the properties of metals and it is often used in physical metallurgy. Metallographic inspection is an important method of inspecting the quality of metal materials after spheroidization. In the process of metallographic inspection, a high-power optical microscope combined with a digital camera is usually used to obtain an image of the spheroidized metal. A light sensor, which is a charge-coupled device in the camera, is used to convert the image observed by the microscope into an electronic image signal. In this paper we present an image recognition method with a deep learning neural network (NN) to inspect the metallographic grade of spheroidized metal. Three different transfer learning models are incorporated in the NN structure for feature extraction for comparison. The overall aim of our study is to reduce the shortcomings and inconvenience of traditional manual inspection and increase the judgment accuracy of metallographic analysis. In experiments, 203 metallographic images of size 1536 × 2048 were used for the learning and testing of the NN. The metallographic grade of the spheroidized metal was evaluated by the deep learning NN model.

Corresponding author: Huang-Chu Huang




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