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Abstract <p>This article presents an application of the YOLOv8 model to the automated detection of surface defects in mechanical engineering products during industrial research using additive manufacturing of metals. The article discusses the dataset formation, the model training process, and the results of applying the proposed solution to automated quality control of mechanical engineering products manufactured using additive manufacturing.</p>

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Application of Neural Network Methods for Quality Control of Mechanical Engineering Products during Their Selective Laser Melting

  • 期刊论文

  • 发表日期：

    2026年3月1日

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  • P. Yu. Peretyagin,
  • N. V. Khodanovich,
  • P. A. Podrabinnik,
  • G. G. Aslanyan,
  • N. S. Grigoriev

摘要

Abstract

This article presents an application of the YOLOv8 model to the automated detection of surface defects in mechanical engineering products during industrial research using additive manufacturing of metals. The article discusses the dataset formation, the model training process, and the results of applying the proposed solution to automated quality control of mechanical engineering products manufactured using additive manufacturing.