OPTIMIZATION OF ENERGY CONSUMPTION IN CNC MARBLE PROCESSING: STATISTICAL AND MACHINE LEARNING APPROACHES

Yıl 2024, Cilt: 8 Sayı: 3, 437 - 450, 30.12.2024

Gencay Sarıışık , Ahmet Sabri Öğütlü

https://doi.org/10.46519/ij3dptdi.1564924

Öz

This study aims to evaluate the specific energy consumption during marble processing on CNC machines both by traditional statistical methods and machine learning models. It presents an analytical framework that examines the effects of process parameters to improve energy efficiency in CNC machining processes. In the experimental part, a data set of 5400 obrervations was obtained considering different machining types, depths of cut and feed rates. Analysis of Variance (ANOVA) and regression models confirmed the decisive role of material removal rate (MRR) on specific energy consumption. The study comprehensively analyzed the performance of four different machine learning models (Gradient Boosting, Random Forest, XGBoost, LightGBM) to predict the specific energy consumption during marble processing on CNC machines. The findings show that specific energy consumption is an important parameter for energy efficiency and cost reduction. The accuracy of the models was evaluated with metrics such as R2, RMSE and MAE, and as a result, it was found that Gradient Boosting and XGBoost models outperformed the others in the Spiral machining type. These findings provide a solid basis for developing strategies to improve energy efficiency in marble processing on CNC machines. The study provides important information that can help make strategic decisions to save energy and improve environmental sustainability. Providing valuable guidance for future research, this study demonstrates the potential use of machine learning models to improve energy efficiency in the natural stone industry.

Anahtar Kelimeler

Marble, Energy Consumption, Specific Energy, CNC Machines, Machine Learning.

Kaynakça

• 1. Sarıışık, G., “Investigation on cutting parameter models and processability index in 3D marble products with milled tools,” Arabian Journal of Geosciences, Vol. 14, Issue 18, Pages 1854, 2021.
• 2. Sarıışık, G. and Özkan, E., “Effects of natural rock properties on cutting forces, specific energy and specific cutting energy by four-axis machine,” Arabian Journal of Geosciences, Vol. 11, Pages 1-19, 2018.
• 3.Turchetta, S., “Cutting force and diamond tool wear in stone machining,” The International Journal of Advanced Manufacturing Technology, Vol. 61, Pages 441-448, 2012.
• 4. Mishra, V.K. and Salonitis, K., “Empirical estimation of grinding specific forces and energy based on a modified Werner grinding model,” Procedia CIRP, Vol. 8, Issue 1, Pages 287-292, 2013.
• 5. Salonitis, K., Stavropoulos, P., and Kolios, A., “External grind-hardening forces modelling and experimentation,” International Journal of Advanced Manufacturing Technology, Vol. 70, Issues 1-4, Pages 523-530, 2014.
• 6. Yurdakul, M., Gopalakrishnan, K., and Akdas, H., “Prediction of specific cutting energy in natural stone cutting processes using the neuro-fuzzy methodology,” International Journal of Rock Mechanics and Mining Sciences, Vol. 67, Pages 127–135, 2014.
• 7. Zhang, H., Zhang, J., Wang, Z., Sun, Q., and Fang, J., “A new frame saw machine by diamond segmented blade for cutting granite,” Diamond and Related Materials, Vol. 69, Pages 40–48, 2016.
• 8. Polini, W. and Corrado, A., “Digital twin of stone sawing processes,” The International Journal of Advanced Manufacturing Technology, Vol. 112, Issue 1, Pages 121-131, 2021.
• 9. Breidenstein, B., Denkena, B., Bergmann, B., Picker, T., and Wolters, P., “Tool wear when using natural rocks as cutting material for the turning of aluminum alloys and plastics,” Production Engineering, Vol. 17, Issue 3, Pages 425-435, 2023.
• 10. Wang, K., Zhang, J., and Zhang, H., “An analytical dynamic force model for sawing force prediction considering the material removal mode,” Journal of Manufacturing Processes, Vol. 106, Pages 494-505, 2023.
• 11. Dong, P., Zhang, J., and Zhang, H., “Wear performance of diamond tools during sawing with reciprocating swing frame saw,” Diamond and Related Materials, Pages 110890, 2024.
• 12. Turchetta, S. and Sorrentino, L., “Forces and wear in high-speed machining of granite by circular sawing,” Diamond and Related Materials, Vol. 100, Page 107579, 2019.
• 13. Zhu, Z., Buck, D., Guo, X., Xiong, X., Xu, W., and Cao, P., “Energy efficiency optimization for machining of wood plastic composite,” Machines, Vol. 10, Issue 2, Page 104, 2022.
• 14. Inal, S., Erkan, I., and Aydiner, K., “Determination of the wear performance of diamond saw blades using inductively coupled plasma,” Sādhanā, Vol. 44, Issue 5, Page 127, 2019.
• 15. Wang, F., Liu, S., Guo, Z., and Cao, L., “Analysis of cutting forces and chip formation in milling of marble,” International Journal of Advanced Manufacturing Technology, Vol. 108, Issue 9, Pages 2907–2916, 2020.
• 16. Wang, K., Zhang, J., and Zhang, H., “An analytical dynamic force model for sawing force prediction considering the material removal mode,” Journal of Manufacturing Processes, Vol. 106, Pages 494-505, 2023.
• 17. Breidenstein, B., Denkena, B., Bergmann, B., Picker, T., and Wolters, P., “Tool wear when using natural rocks as cutting material for the turning of aluminum alloys and plastics,” Production Engineering, Vol. 17, Issue 3, Pages 425-435, 2023.
• 18. Zhu, Z., Buck, D., Guo, X., Ekevad, M., and Cao, P., “Effect of cutting speed on machinability of stone–plastic composite material,” Science of Advanced Materials, Vol. 11, Issue 6, Pages 884-892, 2019.
• 19. Polini, W. and Turchetta, S., “Force and specific energy in stone cutting by diamond mill,” International Journal of Machine Tools and Manufacture, Vol. 44, Issue 11, Pages 1189-1196, 2004.
• 20. Sarıışık, G. and Özkan, E., “Mermerlerin CNC makinesi ile işlenmesinde kesme kuvvetleri ve spesifik kesme enerjisinin istatistiksel analizi,” Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, Vol. 19, Issue 55, Pages 178-193, 2017.
• 21. Sariisik, G. “Force and Specific Energy in Natural Rocks Cutting by Four-Axis Machine.” In Earth Crust. IntechOpen, 2019.
• 22. Klaić, M., Brezak, D., Staroveški, T., and Murat, Z. “On-line Workpiece Hardness Monitoring in Stone Machining.” Transactions of FAMENA, Vol. 43, Issue 4, Pages 43-53, 2019.
• 23. Bai, S., Qiu, H., Rabe, L., Jia, S., Elwert, T., Wang, Q. Y., and Sun, Y. “A Study on Energy-saving Optimization Strategy for the Stone Processing Industry—An Improved Method for Modeling Cutting Power and Energy Consumption: A Case Study of Block Sawing Process.” Journal of Cleaner Production, Vol. 300, Page 126922, 2021.
• 24. Gupta, R. K., and Pratap, B. “Diamond Tools Processing for Marble and Granite: Cutting & Wear.” Materials Today: Proceedings, Vol. 46, Pages 2135-2140, 2021.
• 25. Özkan, E., and Öz, O. “Determination of Appropriate Cutting Parameters Depending on Surface Roughness by Taguchi Method in Milling of Marbles.” Arabian Journal of Geosciences, Vol. 13, Issue 13, Pages 532, 2020.
• 26. Nallusamy, S. “Enhancement of Productivity and Efficiency of CNC Machines in a Small Scale Industry Using Total Productive Maintenance.” International Journal of Engineering Research in Africa, Vol. 25, Pages 119-126, 2016.
• 27. Moreira, L. C., Li, W. D., Lu, X., and Fitzpatrick, M. E. “Supervision Controller for Real-time Surface Quality Assurance in CNC Machining Using Artificial Intelligence.” Computers & Industrial Engineering, Vol. 127, Pages 158-168, 2019.
• 28. Fertig, A., Weigold, M., and Chen, Y. “Machine Learning Based Quality Prediction for Milling Processes Using Internal Machine Tool Data.” Advances in Industrial and Manufacturing Engineering, Vol. 4, Page 100074, 2022.
• 29. Yang, Y., Hu, T., Ye, Y., Gao, W., and Zhang, C. “A Knowledge Generation Mechanism of Machining Process Planning Using Cloud Technology.” Journal of Ambient Intelligence and Humanized Computing, Vol. 10, Pages 1081-1092, 2019.
• 30. Jamwal, A., Agrawal, R., Sharma, M., Kumar, A., Kumar, V., and Garza-Reyes, J. A. A. “Machine Learning Applications for Sustainable Manufacturing: A Bibliometric-based Review for Future Research.” Journal of Enterprise Information Management, Vol. 35, Issue 2, Pages 566-596, 2021.
• 31. Rajesh, A. S., Prabhuswamy, M. S., and Satish, H. S. “Smart Manufacturing Through Machine Learning: A Review, Perspective and Future Directions to Machining Industry.” In AIP Conference Proceedings, Vol. 2399, Issue 1, 2023.
• 32. Soori, M., Jough, F. K. G., Dastres, R., and Arezoo, B. “Sustainable CNC Machining Operations, A Review.” Sustainable Operations and Computers, Vol. 5, Pages 73-87, 2024.
• 33.Teale, R. “The Concept of Specific Energy in Rock Drilling.” International Journal of Rock Mechanics and Mining Sciences, Vol. 2, Issue 1, Pages 57-73, 1965.
• 34. Özdemir, M. H., Aylak, B. L., İnce, M., and Oral, O. “Predicting world electricity generation by sources using different machine learning algorithms.” International Journal of Oil, Gas and Coal Technology, Vol. 35, Issue 1, Pages 98-115, 2024.
• 35. Aylak, B. L., İnce, M., Oral, O., Süer, G., Almasarwah, N., Singh, M., and Salah, B. “Application of machine learning methods for pallet loading problem.” Applied Sciences, Vol. 11, Issue 18, Pages 8304, 2021.
• 36. Li, C., Tang, Y., Cui, L., Li, P., "A quantitative approach to analyze carbon emissions of CNC-based machining systems," Journal of Intelligent Manufacturing, Vol. 26, 2013.
• 37. Zhang, C., Jiang, P., "Sustainability Evaluation of Process Planning for Single CNC Machine Tool under the Consideration of Energy-Efficient Control Strategies Using Random Forests," Sustainability, Vol. 11, 2019.
• 38. Feng, Z., Zhang, H., Li, W., Yu, Y., Guan, Y., Ding, X., "Exergy Loss Assessment Method for CNC Milling System Considering the Energy Consumption of the Operator," Processes, Vol. 11, Issue 2702, 2023.
• 39. Soori, M., Arezoo, B., and Dastres, R. “Machine Learning and Artificial Intelligence in CNC Machine Tools, A Review.” Sustainable Manufacturing and Service Economics, Vol. 2, Page 100009, 2023.
• 40. Martinov, G. M., Ljubimov, A. B., and Martinova, L. I., "From classic CNC systems to cloud-based technology and back," Robotics and Computer-Integrated Manufacturing, Vol. 63, 101927, 2020.
• 41. Soori, M., Jough, F. K. G., Arezoo, B., and Dastres, R., "Robotical automation in CNC machine tools: a review," Acta Mechanica et Automatica, Vol. 18, Pages 434-450, 2024.
• 42. Aebersold, S. A., Akinsolu, M. O., Monir, S., and Jones, M. L., "Ubiquitous control of a CNC machine: Proof of concept for industrial IoT applications," Information, Vol. 12, Issue 12, 529, 2021.
• 43. Re, M., and Valentini, G. “Ensemble Methods: A Review in Advances in Machine Learning and Data Mining for Astronomy.” In Kumar, V. (Ed.), Pages 563-594, 2012. 44. Xu, P., Ji, X., Li, M., and Lu, W. “Small Data Machine Learning in Materials Science.” npj Computational Materials, Vol. 9, Issue 1, Page 42, 2023.
• 45. Sharma, S., Gupta, V., Mudgal, D., and Srivastava, V. “Machine Learning for Forecasting the Biomechanical Behavior of Orthopedic Bone Plates Fabricated by Fused Deposition Modeling.” Rapid Prototyping Journal, Vol. 30, Issue 3, Pages 441-459, 2024.
• 46. Chen, T., and Guestrin, C. “Xgboost: A Scalable Tree Boosting System.” In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Pages 785-794, 2016.
• 47. Friedman, J. H. “Greedy Function Approximation: A Gradient Boosting Machine.” Annals of Statistics, Pages 1189-1232, 2001.
• 48. Sharma, S., Gupta, V., and Mudgal, D. “Response Surface Methodology and Machine Learning Based Tensile Strength Prediction in Ultrasonic Assisted Coating of Poly Lactic Acid Bone Plates Manufactured Using Fused Deposition Modeling.” Ultrasonics, Vol. 137, Page 107204, 2024.
• 49. Breiman, L. “Random Forests.” Machine Learning, Vol. 45, Pages 5-32, 2001.
• 50. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., and Liu, T. Y. “Lightgbm: A Highly Efficient Gradient Boosting Decision Tree.” Advances in Neural Information Processing Systems, Vol. 30, 2017.
• 51. Zhou, Y., Wang, W., Wang, K., and Song, J. “Application of LightGBM Algorithm in the Initial Design of a Library in the Cold Area of China Based on Comprehensive Performance.” Buildings, Vol. 12, Page 1309, 2022.