This paper provides a structured and comprehensive overview of the fundamental concepts, technologies, and methods of Artificial Intelligence in the context of industrial production and presents current research at Hochschule Bochum.
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.
Abstract. The proliferation of interlinked industrial devices at an exponential rate in the Industry 4.0 paradigm has produced volumes of real-time manufacturing information not seen before, creating a need and opportunity to establish intelligent process control. This article suggests a new hybrid artificial intelligence system of smart manufacturing, combining Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN), ensemble tree-based classifiers, and a Proximal Policy Optimization (PPO)-based Reinforcement Learning (RL) agent in a four-layer system that includes data acquisition, AI processing, decision control, and feedback actuation. The framework is tested on the SECOM semiconductor manufacturing data with added synthetic CNC machining data, where a fault detection accuracy of 96.7, an Overall Equipment Effectiveness (OEE) of 91.8 and a defect rate is reduced by 6.8 to 1.1 compared to traditional Statistical Process Control (SPC) baselines. Latency of inference 44 ms meets hard real-time requirements in manufacturing. The superiority and generalizability of the proposed approach are supported by the results of comparative analysis against six state-of-the-art methods. The findings indicate that predictive modeling, computer vision and adaptive closed-loop control in a synergistic combination is a viable scalable route to intelligent manufacturing excellence.
C. T· Materials Research Proceedin...· 0 citations
Artificial Intelligence (AI) is a multidisciplinary field focused on designing and developing intelligent systems capable of performing tasks that typically require human intelligence. It encompasses the creation of smart computer programs that can perceive, reason, learn, and make decisions. Although AI draws inspiration from the study of human intelligence, it is not restricted to biologically inspired methods or processes. Despite the absence of a universally accepted definition, AI is generally recognized as the study of computational techniques that enable machines to perceive their environment, reason logically, and take appropriate actions. In the modern digital era, the volume of data generated by humans and machines has increased dramatically, surpassing the capacity of individuals to efficiently process, analyze, and make informed decisions based on such information. AI addresses this challenge by enabling computers to learn from data, identify patterns, and support intelligent decision-making. As a result, artificial intelligence has become a fundamental technology driving automation, data analysis, and advanced computing applications across numerous industries. This paper presents an overview of artificial intelligence by discussing its fundamental concepts, definitions, historical evolution, key characteristics, major applications, recent advancements, and notable achievements.
Mahendra Manilal Patel, Brijesh J. Gameti, Darshanaben D. Pandya· International Journal of Sci...· 0 citations
This article examines the application of machine vision systems for automated quality control at manufacturing enterprises. It analyzes the current state of the market, the hardware structure of visual inspection systems, and the technological capabilities of 2D, 3D, hyperspectral, and intelligent machine vision for defect detection, dimensional measurement, assembly verification, marking inspection, and packaging control. A classification of production tasks based on the feasibility of machine vision implementation is proposed. Special attention is paid to the selection of cameras, lenses, lighting, image processing algorithms, and integration with programmable logic controllers, MES, and ERP systems. The article presents accuracy indicators, methods for evaluating algorithm performance, and recommendations for reducing false rejects and missed defects. Based on the analysis of practical experience from Russian and international enterprises, recommendations are formulated for choosing an optimal machine vision architecture for different types of production, and promising directions for further development of the technology in the context of industrial digital transformation are identified.
Ya.A. Gaidukov, M. Naumov· Glavnyj mekhanik (Chief Mech...· 0 citations
This review highlights the role of machine learning techniques, deep learning models, and their applications in various areas such as smart grid, renewable energy prediction, fault detection, energy prediction, and predictive maintenance, and covers benchmark datasets, evaluation platforms, and the performance of AI algorithms.
Fawad Khan· Global Trends in Science and...· 0 citations
Artificial intelligence (AI) is transforming smart manufacturing by enabling intelligent automation, data-driven decisions, and stronger collaboration between humans and manufacturing systems. The widespread adoption of collaborative robots, the industrial internet of things, and cyber-physical systems is driving demand for manufacturing environments that are safer, more flexible, and more efficient. Despite AI’s broad application in manufacturing, few studies have combined adaptive safety and intelligent task allocation within a single human-centered framework. This review offers a comprehensive look at AI applications that support these two complementary functions. Literature from Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and the ACM Digital Library was systematically reviewed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and analysed thematically. The findings show that machine learning, deep learning, computer vision, reinforcement learning, knowledge-driven approaches, digital twins, and explainable AI contribute to improvements in predictive maintenance, quality inspection, production scheduling, adaptive safety, and collaborative decision-making. That said, challenges remain - such as interoperability, explainability, limited access to high-quality manufacturing data, industrial validation, and integrating multiple AI technologies. This review gives researchers and practitioners a holistic perspective and highlights integrated, human-centered AI frameworks as key enablers of resilient, efficient, and sustainable Industry 5.0 manufacturing systems.
Zaliha Baso, N. Yadav, P. Faujdar· Cureus Journal of Computer S...· 0 citations
Aims: This research aims to advance industrial glass manufacturing by implementing a next-generation autonomous control architecture. The study focuses on maximising energy efficiency and product quality by integrating Digital Twin technology, Deep Reinforcement Learning (DRL), and Explainable AI (XAI) to overcome the limitations of legacy control systems.
Study Design: This is an analytical and simulation-based research study focused on the cognitive optimisation of industrial float glass production processes.
Place and Duration of Study: The study was conducted in the Department of "Instrumentation Engineering" (Intellectual Measurement and Control Systems), Azerbaijan State Oil and Industry University (ASOIU), between September 2025 and June 2027.
Methodology: A high-fidelity Digital Twin of a float glass furnace was developed to simulate production dynamics. A DRL-based agent was implemented for real-time furnace regulation, allowing for continuous self-optimisation of thermal zones. The system integrated a Convolutional Neural Network (CNN)-based visual inspection layer for defect detection, complemented by an XAI module that provides transparent, logic-based justifications for autonomous operational adjustments. Theoretical evaluations of hydrogen-natural-gas blending were also conducted to assess sustainability impacts.
Results: The proposed DRL-driven architecture achieved improved thermal regulation, maintaining stability within ±0.5°C compared with traditional Fuzzy-PID models. The combustion strategy, optimised via reinforcement learning, produced a reported reduction in fuel consumption of more than 6% (P < 0.05). The XAI module provided real-time interpretability of system decisions, while the integration of hydrogen-enriched combustion pathways indicated a potential decrease in carbon emissions of approximately 8-10% and maintained high combustion efficiency compared with standard natural gas use.
Conclusion: The transition from localised control to an AI-driven autonomous framework, supported by Digital Twins and Explainable AI, provides a transparent approach to modernising glass manufacturing. These systems may reduce operational risks and environmental impacts, thereby supporting intelligent and sustainable industrial automation.
Omar Musazade, Stanislav Aghamatov· Journal of Engineering Resea...· 0 citations
