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The Method for Classifying Stainless Steel Grades in Products Using Portable NIR Spectrometer and CNN

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(10), pp.97-104
  • DOI : 10.9708/jksci.2024.29.10.097
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : August 20, 2024
  • Accepted : September 25, 2024
  • Published : October 31, 2024

Ju-Hoon Jang 1 In-Yeop Choi 1

1강남대학교

Accredited

ABSTRACT

This paper proposes a method for classifying the grade of stainless steel using a portable NIR(Near Infrared Ray) spectrometer and a CNN(Convolutional Neural Network) deep learning model. Traditionally, methods for classifying stainless steel grades have included chemical analysis, magnetic testing, molybdenum spot tests, and portable XRF devices. In addition, a classification method using a machine learning model with element concentration and heat treatment temperature as parameters was presented in the paper. However, these methods are limited in their application to everyday products, such as kitchenware and cookware, due to the need for reagents, specialized equipment, or reliance on professional services. To address these limitations, this paper proposes a simple method for classifying the grade of stainless steel using a NIR spectrometer and a CNN model. If the method presented in this paper is installed on a portable device as an on-device in the future, it will be possible to determine the grade of stainless steel used in the product, and to determine on-site whether a product made of low-cost material has been disguised as a high-cost product.

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