Artificial Intelligence for Quality Defects in the Automotive Industry: A Systemic Review

Area of Science:

• Manufacturing Engineering
• Computer Science
• Industrial Engineering

Background:

• Artificial intelligence (AI) is transforming the automotive sector, particularly in quality management and issue identification.
• Industry 4.0 and 5.0 emphasize intelligent automation and human-centric manufacturing.

Purpose of the Study:

• To systematically review AI implementations for enhancing automotive production processes within Industry 4.0 and 5.0.
• To analyze AI's role in quality management, defect detection, and predictive maintenance.

Main Methods:

• Deep learning
• Artificial neural networks
• Principal Component Analysis
• Convolutional Neural Networks for computer vision

Main Results:

• AI significantly improves defect detection precision and reduces material scrap.
• AI enables automated tracking of auto parts, decreasing reliance on manual inspections.
• AI facilitates predictive maintenance by forecasting machine failures.

Conclusions:

• Integrating AI solutions is crucial for adapting automotive manufacturing to Industry 5.0 requirements.
• AI enhances efficiency, consistency, and sustainability in automotive quality assurance.
• Future research should focus on transparent AI, cyber-physical systems, and sustainable AI materials.