Author(s)

Kenan Peker ¹, Mahmut Yıldız ², Safiye Turgay ²

Affiliation(s)

¹ Gedik İleri Döküm Teknolojileri, Sakarya 2.OSB, 11 No'lu Yanyol, No.6, 54300 Hendek, Sakarya, Turkey
² Department of Industrial Engineering, Sakarya University, Sakarya, Turkey

Corresponding Author

Safiye Turgay

ABSTRACT

This study delves into the pivotal role of artificial intelligence (AI) and image processing techniques in revolutionizing quality control processes across diverse industries. The modern landscape of manufacturing and quality assurance demands advanced methodologies that go beyond human capability, and AI-powered image analysis has emerged as a transformative solution. In this research, we explore the integration of machine learning, computer vision, and image processing algorithms to enhance the accuracy, efficiency, and comprehensiveness of quality control procedures. The study begins by elucidating the theoretical foundations of image processing and AI, highlighting their synergy and applicability in quality control. It examines the pivotal components of this technology, including deep learning, convolutional neural networks (CNNs), and object recognition algorithms. We also investigate the practical challenges and considerations associated with implementing AI-driven image processing systems in industrial settings, such as data acquisition, hardware, and real-time processing. A central focus of this research is the evaluation of AI's capacity to inspect and analyze images, patterns, and anomalies across various sectors, from automotive and electronics to pharmaceuticals and food processing. We present case studies and empirical results demonstrating the significant improvements in defect detection, yield enhancement, and production efficiency achieved by integrating AI-powered image analysis. In conclusion, this study underscores the transformative potential of AI and image processing techniques in quality control, offering a glimpse into the future of manufacturing and production. The integration of AI and image analysis not only accelerates defect detection but also ensures consistent product quality, ultimately strengthening competitiveness and customer satisfaction.

KEYWORDS

Artificial Intelligence, Image Processing, Quality Control, Machine Learning, Computer Vision, Defect Detection, Industrial Automation

CITE THIS PAPER

Kenan Peker, Mahmut Yıldız, Safiye Turgay, Improving Quality Inspections with Image Analysis and Artificial Intelligence. Accounting and Corporate Management (2023) Vol. 5: 135-146. DOI: http://dx.doi.org/10.23977/acccm.2023.051120.

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