Artificial Intelligence Approaches for Predictive Power Consumption Modeling in Machining-Short Review

¹ Assistant Professor, Maharishi School of Engineering & Technology, Maharishi University of Information Technology, Uttar Pradesh, India
² Assistant Professor, Mechanical Engineering, Vivekananda Global University, Jaipur, India
³ Assistant Professor, Department of Computer Science and Information Technology, Jain (Deemed to be University), Bangalore, India
⁴ Professor, Civil Engineering, Vivekananda Global University, Jaipur, India

^* Corresponding Author: shwetasingh580@gmail.com
^** singh.satendra@vgu.ac.in
^*** rahul.pawar@jainuniversity.ac.in
^**** k.singh@vgu.ac.in

Abstract

This article focuses on the crucial role of predictive modeling, particularly powered by artificial intelligence (AI), in optimizing power consumption in machining, a vital facet of modern manufacturing. Highlighting the growing significance of power utilization in machining operations due to economic, environmental, and equipment-related implications, the article underscores the importance of this area. It proceeds to discuss the contributions of predictive modelling , elucidating its capacity to predict and manage variability, optimize tool selection and cutting parameters, reduce downtime, enable energy-efficient scheduling, and enhance sustainability, all while reducing costs. AI, with its data-driven capabilities, is presented as a transformative force, providing real-time adaptability, predictive maintenance, and energy-efficient scheduling, aligning with sustainability and cost-efficiency goals. While acknowledging the current limitations of AI models, the article outlines future opportunities such as advanced machine learning, IoT integration, sensor monitoring, digital twins, hybrid models, industry standards, and the growing emphasis on explainable AI. These advancements are poised to shape a more sustainable, efficient, and data-informed future for the manufacturing industry.