Application of neural networks in the steel industry
DOI:
https://doi.org/10.33448/rsd-v14i4.48712Keywords:
Six Sigma; Neural Network; Optimization; Foundation; Lamination.Abstract
The objective of this article is to present a case study of applied research on the implementation of deep neural networks in the rolling process of a steel mill. The study is developed within the context of Industry 4.0 and is based on the Lean Six Sigma methodology to address a real-life problem related to defects in products derived from the rolling process. The methodology includes the application of various quality tools to identify the root cause and propose methods that offer an innovative solution. Through the design, training, and evaluation of a multi-layer neural network, an improvement in the classification of conforming (prime) and non-conforming (scrap) products was achieved, reaching an efficiency close to 85%. The study demonstrates how artificial intelligence can be a viable solution for complex industrial processes involving multiple variables, providing significant improvements in quality, efficiency, and decision-making.
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Copyright (c) 2025 José de Jesús Calderón-Sánchez; Rafael Granillo-Macías; Francisca Santana Robles; Héctor Rivera-Gómez
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