Leveraging AI for energy-efficient manufacturing systems: Review and future prospectives

Energy poses a significant challenge in the industrial sector, and the abundance of data generated by Industry 4.0 technologies offers the opportunity to leverage Artificial Intelligence (AI) for enhancing energy efficiency (EE) in manufacturing processes, particularly within manufacturing systems. However, fully realizing AI's potential in addressing energy challenges requires a comprehensive review of AI methodologies aimed at overcoming obstacles in energy-efficient manufacturing systems. This article provides a systematic review that combines both quantitative and qualitative analyses of literature from the past ten years, focusing on mitigating prevalent energy efficiency challenges in manufacturing systems through AI-related methodologies. These challenges include Monitoring and Prediction, Real-Time Control, Scheduling, and Parameters Optimization. The AI-related solutions proposed in the reviewed research articles utilize Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL) techniques, either individually or in combination with other methods. A total of 67 journal papers on manufacturing systems, addressing the mentioned energy challenges through AI-related approaches, have been identified and thoroughly reviewed. As a result of this review, an Energy Efficient-Digital Twin (EE-DT) framework is proposed, demonstrating how a DT, equipped with AI techniques, can be applied to solve energy issues in manufacturing systems. This study provides scholars with a comprehensive guideline for selecting various types of AI methods to address common challenges in energy-efficient manufacturing systems, while also highlighting some promising future research directions. • A systematic literature review on AI-driven energy solutions for manufacturing systems. • Investigated common energy challenges in manufacturing systems and proposed AI-based solutions.. • Introduced a novel conceptual Energy Efficiency-Digital Twin (EE-DT) framework. • Discussed challenges and future directions for AI-driven energy-efficient manufacturing systems.