Author(s): Juan Carlos Rodríguez, Maria Oliveira, Lars Hansen and Sofia Müller

Abstract: Automation in the automotive industry has proven to be a transformative force, particularly in the context of quality control systems. As automotive manufacturing continues to scale and become more complex, the need for precision in quality control has become more critical. Automated systems enhance the efficiency and reliability of quality control processes by integrating advanced technologies such as robotics, artificial intelligence (AI), and machine learning (ML). These technologies ensure consistent product standards, reduce human error, and enable real-time analysis, which is essential in the production of complex automotive parts. The use of automation in quality control systems also facilitates faster detection of defects, allowing for prompt corrective actions, which ultimately reduce waste and operational costs. In addition, automated systems can process large amounts of data, identify trends, and optimize processes, leading to improved overall product quality and operational efficiency. This paper explores the role of automation in enhancing the precision of automotive quality control systems, focusing on the integration of various automated technologies and their impacts on manufacturing processes. The research also examines the challenges faced by the automotive industry in adopting these technologies and the future trends in automated quality control. The hypothesis presented is that automation significantly improves the accuracy and consistency of quality control in automotive manufacturing, thereby contributing to higher-quality products and cost-effective production.

DOI: 10.22271/27078205.2026.v7.i1a.75

Pages: 35-38 | Views: 8 | Downloads: 4

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