Improving Quality Control in the Industrial Sector Using Artificial Intelligence Applications

Abstract

The increasing complexity of industrial production systems and the growing demand for high product quality have intensified the need for advanced quality control (QC) solutions beyond traditional inspection-based approaches. Artificial intelligence (AI) has emerged as a powerful enabler for transforming industrial quality control into a proactive, predictive, and data-driven function. This article presents a comprehensive review and conceptual analysis of improving quality control in the industrial sector through AI applications. It first establishes a conceptual framework that links classical QC principles with AI-driven quality management, highlighting the evolution from manual and statistical inspection methods to intelligent and adaptive systems. The study then examines key AI techniques and models, including machine learning, deep learning, computer vision, and expert systems, and their applications in defect detection, process monitoring, predictive quality assessment, and automated inspection. The role of industrial data and digital infrastructure is analyzed, emphasizing data acquisition, integration, and real-time analytics enabled by Industrial Internet of Things (IIoT) and big data platforms. Furthermore, the performance and impact of AI-driven QC systems are evaluated in terms of technical accuracy, operational efficiency, and economic benefits compared with conventional QC approaches. Finally, the article discusses major implementation challenges, ethical considerations, and future research directions for sustainable and intelligent quality control. The findings indicate that AI-enabled quality control can significantly enhance product quality, process reliability, and industrial competitiveness when supported by robust data governance, ethical frameworks, and organizational readiness.