Artificial Intelligence in Industrial Production: A Survey of concepts, technologies & Practical Application in an Intelligent Production Environment

Abstract

The increasing digitalization of industrial production systems in the context of Industry 4.0 is leading to a growing use of data-driven and intelligent technologies in manufacturing and assembly environments. In particular, Artificial Intelligence (AI), machine learning, and deep learning methods based on neural networks open up new possibilities for the automation of complex decision-making and inspection processes. One of the central application areas in this context is AI-based image processing for visual inspection and quality control. This paper provides a structured and comprehensive overview of the fundamental concepts, technologies, and methods of Artificial Intelligence in the context of industrial production. Among other aspects, relevant neural network architectures with a focus on industrial image processing, typical training and optimization procedures, data preprocessing, and key challenges are addressed. In addition, common application areas of AI in Smart Factory environments are systematically presented. Finally, the paper presents current research at Hochschule Bochum, in which the practical implementation of these technologies using developed neural networks for automated visual quality control in a gearbox assembly process is demonstrated. Lastly, a comparative approach between the AI-based image processing solution and a conventional rule-based machine vision system is presented.