Morales Matamoros Oswaldo, Takeo Nava José Guillermo, Moreno Escobar Jesús Jaime, Ceballos Chávez Blanca Alhely
Centro de Investigación en Computación, Instituto Politécnico Nacional, Ciudad de México 07700, Mexico.
Escuela Superior de Ingeniería Mecánica y Eléctrica, Instituto Politécnico Nacional, Unidad Zacatenco, Ciudad de México 07738, Mexico.
Sensors (Basel). 2025 Feb 20;25(5):1288. doi: 10.3390/s25051288.
Artificial intelligence (AI) has become a revolutionary tool in the automotive sector, specifically in quality management and issue identification. This article presents a systematic review of AI implementations whose target is to enhance production processes within Industry 4.0 and 5.0. The main methods analyzed are deep learning, artificial neural networks, and principal component analysis, which improve defect detection, process automation, and predictive maintenance. The manuscript emphasizes AI's role in live auto part tracking, decreasing dependance on manual inspections, and boosting zero-defect manufacturing strategies. The findings indicate that AI quality control tools, like convolutional neural networks for computer vision inspections, considerably strengthen fault identification precision while reducing material scrap. Furthermore, AI allows proactive maintenance by predicting machine defects before they happen. The study points out the importance of incorporating AI solutions in actual manufacturing methods to ensure consistent adaptation to Industry 5.0 requirements. Future investigations should prioritize transparent AI approaches, cyber-physical system consolidation, and AI material enhancement for sustainable production. In general terms, AI is changing quality assurance in the automotive industry, improving efficiency, consistency, and long-term results.
人工智能（AI）已成为汽车领域的一项革命性工具，特别是在质量管理和问题识别方面。本文对旨在提升工业4.0和5.0生产流程的人工智能应用进行了系统综述。所分析的主要方法包括深度学习、人工神经网络和主成分分析，这些方法可改善缺陷检测、过程自动化和预测性维护。该论文强调了人工智能在实时汽车零部件跟踪中的作用，减少对人工检查的依赖，并推动零缺陷制造策略。研究结果表明，人工智能质量控制工具，如用于计算机视觉检测的卷积神经网络，在降低材料报废率的同时，显著提高了故障识别精度。此外，人工智能能够通过在机器故障发生前进行预测来实现预防性维护。该研究指出了将人工智能解决方案纳入实际制造方法以确保持续适应工业5.0要求的重要性。未来的研究应优先考虑透明的人工智能方法、网络物理系统整合以及用于可持续生产的人工智能材料增强。总体而言，人工智能正在改变汽车行业的质量保证，提高效率、一致性和长期成果。
