Upgrading the versatility of conventional machine tools using the mechatronic approach

References

• Abdullah, R., Mahrous, A., & Barakat, A. (2016). Surface quality of marble machined by abrasive water jet. Cogent Engineering, 3(1).
   Google Scholar
• Al-Maeeni, S. S. H., Kuhnhen, C., Engel, B., & Schiller, M. (2020). Smart retrofitting of machine tools in the context of industry 4.0. Procedia CIRP, 88, 369–374. https://doi.org/10.1016/j.procir.2020.05.064
   Google Scholar
• Alqoud, A., Schaefer, D., & Milisavljevic-Syed, J. (2022). Industry 4.0: A review of digital retrofitting solutions for legacy manufacturing systems. Advances in Manufacturing Technology XXXV, 25, 3–9.
   Google Scholar
• Ashmawy, M., Elsheikh, A., & Elkassas, A. (2023). A review of cooling and lubrication techniques for machining difficult-to-cut material. Journal of Engineering Research, 7(1), 4. https://doi.org/10.21608/erjeng.2023.183248.1134
   Google Scholar
• Babalola, S. A., Mishra, D., Dutta, S., & Murmu, N. C. (2023). In-situ workpiece perception: A key to zero-defect manufacturing in Industry 4.0 compliant job shops. Computers in Industry, 148, 103891.
   Web of Science ®Google Scholar
• Baur, M., Albertelli, P., & Monno, M. (2020). A review of prognostics and health management of machine tools. International Journal of Advanced Manufacturing Technology, 107, 2843–2863.
   Web of Science ®Google Scholar
• Bleicher, F., Biermann, D., Drossel, W.-G., Moehring, H.-C., & Altintas, Y. (2023). Sensor and actuator integrated tooling systems. CIRP Annals, 72(2), 673–696. https://doi.org/10.1016/j.cirp.2023.05.009
   Web of Science ®Google Scholar
• Bolton, W. (2015). Mechatronics: Electronic control systems in mechanical and electrical engineering. Pearson Education, p. 3.
   Google Scholar
• Budak, E., Matsubara, A., Donmez, A., & Munoa, J. (2022). Mechanical interfaces in machine tools. CIRP Annals, 71(2), 647–670. https://doi.org/10.1016/j.cirp.2022.05.005
   Web of Science ®Google Scholar
• Cunha, J., Batista, N., Cardeira, C., & Melicio, R. (2021). Upgrading a legacy manufacturing cell to IoT. Journal of Sensor and Actuator Networks, 10(4), 65. https://doi.org/10.3390/jsan10040065
   Web of Science ®Google Scholar
• de Sá Só Martins, D. H. C., Viana, D. P., de Lima, A. A., Pinto, M. F., Tarrataca, L., e Silva, F. L., Gutiérrez, R. H. R., de Moura Prego, T., Monteiro, U. A. B. V., & Haddad, D. B.(2021). Diagnostic and severity analysis of combined failures composed by imbalance and misalignment in rotating machines. International Journal of Advanced Manufacturing Technology, 114, 3077–3092.
   Web of Science ®Google Scholar
• Dias, A. L., Turcato, A. C., Sestito, G. S., Brandao, D., & Nicoletti, R. (2021). A cloud-based condition monitoring system for fault detection in rotating machines using PROFINET process data. Computers in Industry, 126, 103394.
   Web of Science ®Google Scholar
• Feng, C., & Huang, S. (2020). The analysis of key technologies for sustainable machine tools design. Applied Sciences (Switzerland), 10(3), 731. https://doi.org/10.3390/app10030731
   Web of Science ®Google Scholar
• García-Martínez, E., Miguel, V., Martínez-Martínez, A., Manjabacas, M. C., & Coello, J. (2019). Sustainable lubrication methods for the machining of titanium alloys: An overview. Materials, 12(23), 3852. https://doi.org/10.3390/ma12233852
   PubMed Web of Science ®Google Scholar
• Goman, V. V. (2020). Prospects of upgrade of the main drives of CNC machine tools using mechatronic modules. KnE Engineering, 5(3), 36–42. https://doi.org/10.18502/keg.v5i3.6755
   Google Scholar
• Goodall, P., Pantazis, D., & West, A. (2020). A cyber physical system for tool condition monitoring using electrical power and a mechanistic model. Computers in Industry, 118, 103223.
   Web of Science ®Google Scholar
• Guan, K., Sun, Y., Yang, G., & Yang, X. (2023). Hybrid decision-making-method-based intelligent system for integrated bogie welding manufacturing. Applied System Innovation, 6(1), 29. https://doi.org/10.3390/asi6010029
   Google Scholar
• Ilari, S., Carlo, F. D., Ciarapica, F. E., & Bevilacqua, M. (2021). Machine tool transition from industry 3.0 to 4.0: A comparison between old machine retrofitting and the purchase of new machines from a triple bottom line perspective. Sustainability (Switzerland), 13(18), 10441. https://doi.org/10.3390/su131810441
   Web of Science ®Google Scholar
• Jedrzejewski, J., & Kwasny, W. (2017). Development of machine tools design and operational properties. International Journal of Advanced Manufacturing Technology, 93, 1051–1068.
   Web of Science ®Google Scholar
• Jeerapongudom, T., Anwar, S., & Che, B. (2022). Design and development of mini CNC machine for high performance machining. EasyChair Preprint No. 9104. EasyChair.
   Google Scholar
• Jiang, Y., Drescher, B., Wittstamm, M., Hu, C., Clemens, F., Wang, W., & Stich, V. (2022, May 17–20). Tool wear prediction upgrade kit for legacy CNC milling machines in the shop floor [Paper presentation]. 3rd Conference on Production Systems and Logistics (CSPL 2022), Vancouver, BC, Canada (pp. 131–140).
   Google Scholar
• Keshav Kolla, S. S. V., Lourenço, D. M., Kumar, A. A., & Plapper, P. (2022). Retrofitting of legacy machines in the context of Industrial Internet of Things (IIoT). Procedia Computer Science, 200, 62–70.
   Google Scholar
• Keshava, K. G., & Sree Rajendra, C. K. K. R. (2021). Retrofitting of CNC bench milling machine with raspberry Pi. International Research Journal of Engineering and Technology (IRJET), 8, 3937–3941.
   Google Scholar
• Kröll, M., & Cseh, C. (2023). Implementation model for digital retrofit for sustainable production. Procedia Computer Science, 217, 486–494.
   Google Scholar
• Li, K. Y., Luo, W. J., & Wei, S. J. (2020). Machining accuracy enhancement of a machine tool by a cooling channel design for a built-in spindle. Applied Sciences (Switzerland), 10(11), 3991. https://doi.org/10.3390/app10113991
   Web of Science ®Google Scholar
• Li, Z., Wang, Y., & Wang, K. (2019). A deep learning driven method for fault classification and degradation assessment in mechanical equipment. Computers in Industry, 104, 1–10.
   Web of Science ®Google Scholar
• Lindfield, G., Penny, J., & Analyzing, D. (2019). Numerical methods using Matlab, Fourth Edition, Academic Press, Chapter 7, Elsevier (pp. 329–381). https://linkinghub.elsevier.com/retrieve/pii/B9780128122563000166
   Google Scholar
• López de Calle, K., Ferreiro, S., Arnaiz, A., & Sierra, B. (2019). Dynamic condition monitoring method based on dimensionality reduction techniques for data-limited industrial environments. Computers in Industry, 112. (November Issue).
   Web of Science ®Google Scholar
• Mohamed, A., Hassan, M., M’Saoubi, R., & Attia, H. (2022). Tool condition monitoring for high-performance machining systems – A review. Sensors, 22(6), 2206. https://doi.org/10.3390/s22062206
   PubMed Web of Science ®Google Scholar
• Neugebauer, R., Denkena, B., & Wegener, K. (2007). Mechatronic systems for machine tools. CIRP Annals – Manufacturing Technology, 56, 657–686.
   Web of Science ®Google Scholar
• Oezkaya, E., Michel, S., & Biermann, D. (2022). Experimental and computational analysis of the coolant distribution considering the viscosity of the cutting fluid during machining with helical deep hole drills. Advances in Manufacturing, 10, 235–249.
   Web of Science ®Google Scholar
• Patole, P. B., & Kulkarni, V. V. (2017). Experimental investigation and optimization of cutting parameters with multi response characteristics in MQL turning of AISI 4340 using nano fluid. Cogent Engineering, 4(1).
   Google Scholar
• Pelayo, G. U., Olvera-Trejo, D., Budak, E., & Wan, M. (2023). Special issue on machining systems and signal processing: Advancing machining processes through algorithms, sensors and devices. Mechanical Systems and Signal Processing, 182, 109575.
   Web of Science ®Google Scholar
• Peng, J., Yin, M., Cao, L., Liao, Q., Wang, L., & Yin, G. (2023). Study on the thermally induced spindle angular errors of a five-axis CNC machine tool. Advances in Manufacturing, 11, 75–92.
   Web of Science ®Google Scholar
• Pimenov, D. Y., Mia, M., Gupta, M. K., Machado, A. R., Tomaz, Í. V., Sarikaya, M., Wojciechowski, S., Mikolajczyk, T., & Kapłonek, W. (2021). Improvement of machinability of Ti and its alloys using cooling-lubrication techniques: A review and future prospect. Journal of Materials Research and Technology, 11, 719–753. https://doi.org/10.1016/j.jmrt.2021.01.031
   Google Scholar
• Sparham, M., Sarhan, A. A. D., Mardi, N. A., & Hamdi, M. (2014). Designing and manufacturing an automated lubrication control system in CNC machine tool guideways for more precise machining and less oil consumption. International Journal of Advanced Manufacturing Technology, 70, 1081–1090.
   Web of Science ®Google Scholar
• Sutopo, S., Setiadi, B. R., & Hanzla, M. (2020). Upgrading manual turning machine towards IoT-based manufacturing. Jurnal Pendidikan Teknologi Dan Kejuruan, 26(2), 155–161. https://doi.org/10.21831/jptk.v26i2.27334
   Google Scholar
• Tsai, P. C., Cheng, C. C., Chen, W. J., & Su, S. J. (2020). Sensor placement methodology for spindle thermal compensation of machine tools. International Journal of Advanced Manufacturing Technology, 106, 5429–5440.
   Web of Science ®Google Scholar
• Uhlmann, E., Lang, K.-D., Prasol, L., Thom, S., & Peukert, B. (2017). Sustainable solutions for machine tools. In R. Stark, G. Seliger, & J. Bonvoisin (Eds.), Sustainable manufacturing. Sustainable production, life cycle engineering and management (pp. 47–69). Springer.
   Google Scholar
• Uhlmann, E., Thom, S., & Ehricke, M. (2017). Application of spindle speed increaser as sustainable solution to upgrade machine tools. Procedia Manufacturing, 8, 680–685. https://doi.org/10.1016/j.promfg.2017.02.087
   Google Scholar
• Wan, S., Li, D., Gao, J., Roy, R., & Tong, Y. (2017). Process and knowledge management in a collaborative maintenance planning system for high value machine tools. Computers in Industry, 84, 14–24.
   Web of Science ®Google Scholar
• Wu, C., Wang, Q., Fan, J., & Pan, R. (2020). A novel prediction method of machining accuracy for machine tools based on tolerance. The International Journal of Advanced Manufacturing Technology, 110, 629–653. https://doi.org/10.1007/s00170-020-05762-4
   Web of Science ®Google Scholar
• Zhao, Y. (2020). The application research of mechatronics technology based on computer in construction machinery. Journal of Physics: Conference Series, 1574, 012060. https://doi.org/10.1088/1742-6596/1574/1/012060
   Google Scholar
• Zhao, Z., Lin, P., Shen, L., Zhang, M., & Huang, G. Q. (2020). IoT edge computing-enabled collaborative tracking system for manufacturing resources in industrial park. Advanced Engineering Informatics, 43, 101044. https://doi.org/10.1016/j.aei.2020.101044
   Web of Science ®Google Scholar
• Zheng, C., Le Duigou, J., Bricogne, M., & Eynard, B. (2016). Multidisciplinary interface model for design of mechatronic systems. Computers in Industry, 76, 24–37.
   Web of Science ®Google Scholar