Jun 2026· Computing· Vol 108· 0 citations· 45 references
Computer Science
TL;DR
This systematic review synthesizes advancements in ML-based process optimization from 2015 to 2025, analyzing 185 peer-reviewed articles from Scopus, Web of Science, and IEEE Xplore to provide a comprehensive foundation for advancing ML applications in modern manufacturing systems.
Artificial Intelligence (AI) and Machine Learning (ML) are increasingly driving the digital transformation of manufacturing systems, enabling the transition from conventional process operation toward intelligent, adaptive, and data-centric production environments. This work presents the development of AI-enabled advisory systems for casting processes, integrating singular value decomposition (SVD)-based reduced-order models with a Variational Autoencoder with Arbitrary Conditioning (AC-VAE) and hybrid simulation frameworks to support real-time process prediction and optimization. The proposed approach leverages manufacturing data to establish predictive models capable of rapidly evaluating process conditions, optimizing operating parameters, and enhancing product quality while reducing material waste, energy consumption, and production costs. By combining physics-based understanding with AI-driven analytics, the framework facilitates real-time decision support, adaptive process control, and continuous performance improvement within modern manufacturing ecosystems. These capabilities contribute to the broader objectives of Industry 4.0 and emerging Industry 5.0 paradigms, including automation, connectivity, operational resilience, sustainability, and human-centered manufacturing. A representative Horizontal Direct Chill (HDC) continuous casting case study is presented to demonstrate the practical implementation of the framework, encompassing database generation, model training, validation, and deployment of predictive advisory tools for real-time manufacturing applications.
Sofija Milicic, A. Horr, S. Elgeti et al.· Processes· 0 citations
Industry 4.0 technologies are transforming manufacturing into intelligent, interconnected, and data-driven systems. Among these innovations, Digital Twin (DT) technology creates virtual replicas of physical assets, processes, and systems to enable real-time monitoring and optimization. When integrated with Artificial Intelligence (AI), Digital Twins provide a powerful framework for predictive maintenance, anomaly detection, process optimization, and autonomous control. By leveraging IoT sensors, edge computing, cloud platforms, and machine learning algorithms, AI-driven Digital Twins continuously synchronize physical and virtual environments to support intelligent decision-making. The proposed framework includes data acquisition, preprocessing, digital modeling, AI analytics, simulation, optimization, and decision support layers. Techniques such as Artificial Neural Networks (ANNs), Deep Learning (DL), Reinforcement Learning (RL), and predictive analytics are used to identify operational patterns and optimize industrial performance. Key performance indicators, including production efficiency, resource utilization, product quality, energy efficiency, downtime reduction, and system reliability, demonstrate the effectiveness of the framework. Experimental results indicate that AI-enabled Digital Twins can improve industrial productivity by over 20%, reduce maintenance costs, and enhance decision-making accuracy. The study highlights the transformative potential of AI-driven Digital Twins in enabling autonomous industrial operations, sustainable manufacturing practices, and the development of future smart factories.
Suresh Babu Reddy· International Journal of App...· 0 citations
Industry 4.0 integrates Artificial Intelligence (AI), Industrial Internet of Things (IIoT), cloud computing, edge computing, and cyber-physical systems to enable intelligent and automated manufacturing. Unlike traditional rule-based automation, AI-driven process optimization enables predictive decision-making, adaptive control, and continuous learning in dynamic production environments. This paper proposes an AI-enabled process optimization framework that combines real-time sensor data, predictive analytics, machine learning, reinforcement learning, optimization algorithms, and closed-loop feedback to improve manufacturing performance. The framework predicts equipment failures, detects process anomalies, optimizes production schedules, enhances resource utilization, and reduces energy consumption. Performance is evaluated using metrics such as production efficiency, cycle time, defect rate, machine utilization, predictive maintenance accuracy, throughput, energy efficiency, and operational cost. The proposed framework provides a scalable and intelligent solution for Industry 4.0 and Industry 5.0 manufacturing, improving productivity, sustainability, operational resilience, and decision-making in automated production systems.
N. Wirth· International Journal of Int...· 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
It is argued that first-principles models (FPMs) remain essential for mission- and business-critical workflows in industrial automation, both in process design and operations and hybrid intelligence integrates mechanistic rigor with data-driven insights to deliver smarter design, safer operations, and more sustainable processes.
Hongzhi Zhao, Shu Wang, Salvador I. Pérez-Uresti et al.· Industrial & Engineering...· 0 citations
Abstract. Sustainable manufacturing involves being able to optimize productivity, energy efficiency, and environmental impact simultaneously given dynamic and uncertain operating conditions. The traditional optimization methods are unadaptable and cannot easily reflect the real-time changes in the system. This paper provides a sophisticated reinforcement learning (RL)-based decision-making model of sustainable manufacturing process. The manufacturing system is modelled as a Markov Decision Process (MDP) and a Deep Q-Network (DQN) is used to learn about the optimal control policies by interacting with the environment continuously. Multi-objective reward function is created to include production rate, energy usage, machine usage and minimization of waste. The suggested framework is tested in a virtualized smart factory setting, where the demand is stochastic and machines have variability. Comparative analysis shows that the RL-based approach outperforms the rule-based and heuristic strategies and reports remarkable energy efficiency and operational sustainability. The findings prove RL as a potential solution to adaptive and intelligent manufacturing control.
A. Jain· Materials Research Proceedin...· 0 citations