Version 1
: Received: 26 December 2023 / Approved: 27 December 2023 / Online: 27 December 2023 (10:08:22 CET)
How to cite:
Nugrahanto, I.; Gunawan, H.; Chen, H.-Y. Innovative Approaches to Sustainable CNC Machining: A Machine Learning Perspective on Energy Optimization. Preprints2023, 2023122036. https://doi.org/10.20944/preprints202312.2036.v1
Nugrahanto, I.; Gunawan, H.; Chen, H.-Y. Innovative Approaches to Sustainable CNC Machining: A Machine Learning Perspective on Energy Optimization. Preprints 2023, 2023122036. https://doi.org/10.20944/preprints202312.2036.v1
Nugrahanto, I.; Gunawan, H.; Chen, H.-Y. Innovative Approaches to Sustainable CNC Machining: A Machine Learning Perspective on Energy Optimization. Preprints2023, 2023122036. https://doi.org/10.20944/preprints202312.2036.v1
APA Style
Nugrahanto, I., Gunawan, H., & Chen, H. Y. (2023). Innovative Approaches to Sustainable CNC Machining: A Machine Learning Perspective on Energy Optimization. Preprints. https://doi.org/10.20944/preprints202312.2036.v1
Chicago/Turabian Style
Nugrahanto, I., Hariyanto Gunawan and Hsing-yu Chen. 2023 "Innovative Approaches to Sustainable CNC Machining: A Machine Learning Perspective on Energy Optimization" Preprints. https://doi.org/10.20944/preprints202312.2036.v1
Abstract
Industry 4.0 brings advanced technologies and automation, enabling greater productivity and efficiency. However, it also increases energy consumption and carbon emissions. Sustainable manufacturing in the era of Industry 4.0 is vital for addressing the environmental impact by minimizing resource consumption, optimizing energy efficiency, and adopting eco-friendly practices. CNC (Computer Numeric Control) 5-axis milling plays a significant role in the machining of precision molds and dies, aerospace, consumer electronics, etc. This research aims to explore the potential of the artificial intelligence (AI) technique in improving energy efficiency on CNC 5-axis milling process for sustainable manufacturing. The experiments were carried out using a CNC 5-axis milling machine with various machining parameters, toolpath planning, and dry cutting condition. Data on energy consumption of CNC 5-axis milling machine were collected, then the relationship between the machining parameters, toolpath planning, and energy consumption was analyzed and built. Furthermore, an AI-based optimization algorithm was developed that uses machine learning and optimization techniques to identify energy-efficient machining strategies. The developed algorithm was implemented within a simulation framework, the algorithm was rigorously assessed using machine learning analysis to effectively reduce energy consumption, all while ensuring the accuracy of machining result. Finally, verification experiments were carried out to evaluate the applicability and performance of the AI-based energy optimization approach on a CNC 5-axis milling machine. By integrating AI into the CNC machining process, manufacturers can achieve significant energy savings, leading to reduced carbon emissions and improved sustainability.
Keywords
Energy efficiency; sustainable manufacturing; CNC milling process; power consumption
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
