Application of artificial intelligence in fault diagnosis, quality improvement, and productivity enhancement in manufacturing and mechanical engineering industries

Mahdi Sadeghzadeh Tabrizi¹ Mir Ali Mousavi Orangi² Mostafa Farhadi³ Ali Bitag Arznaq⁴

1) Professor, East Azerbaijan University of Applied Sciences, Tabriz Worker s House Training Center,
2) Student, East Azerbaijan University of Applied Sciences, Tabriz Worker s House Training Center, Tabriz, Iran
3) Student, East Azerbaijan University of Applied Sciences, Tabriz Worker s House Training Center, Tabriz, Iran
4) Student, East Azerbaijan University of Applied Sciences, Tabriz Worker s House Training Center, Tabriz, Iran

IOI Code : AAHD-AAKCKH
Publication : National Conference on Information Technology, Nanotechnology, Artificial Intelligence and Technological Futures Studies(itnaf.ir)
Abstract :
    In the era of digital transformation, artificial intelligence (AI) is recognized as a strategic tool in improving the performance of manufacturing industries and the field of mechanical engineering. By utilizing machine learning algorithms, machine vision, and big data analysis, this technology enables rapid and accurate detection of defects in production processes, optimizing product quality, and increasing operational efficiency. In this article, the applications of artificial intelligence are examined in three main areas: defect detection in mechanical parts and equipment, improving quality through intelligent process control, and increasing efficiency through predictive analysis of production data. The research method includes a systematic review of scientific sources, analysis of successful industrial examples, and examination of applied algorithms in manufacturing environments. The findings show that implementing artificial intelligence in production lines reduces waste, increases accuracy in quality control, and improves managerial decision-making. This research aims to provide a practical framework for using artificial intelligence in mechanical industries, which can be used as a guide for engineers, production managers, and researchers in the field of advanced technologies.

Keywords : artificial intelligence quality improvement machine learning process control