STRATEGIC MATERIAL SELECTION: A KEYSTONE FOR ENHANCED PERFORMANCE IN CONVENTIONAL AND MODERN METAL MACHINING PROCESSES
DOI:
https://doi.org/10.63996/njte.v25i1.58Keywords:
Material selection, metal machining, tool wear, surface finish, machinabikityAbstract
This paper explores the pivotal role of strategic material selection in enhancing performance within conventional and modern metal machining processes. By comparing material properties, machinability indices, and compatibility with various machining techniques, the study highlights the implications of material choice on productivity, tool life, surface finish, and overall manufacturing efficiency. The paper offers a framework for selecting materials that optimize performance in evolving machining environments, including CNC, additive-subtractive hybrid systems, and advanced manufacturing technologies.
References
Ajie, P., Osoh, O., & Thomas, I. (2024). Enhancing material selection skills in technical education: Integrating advanced tools for machinability optimization. Journal of Vocational Engineering, 15(1), 45–58.
Ajie, P. M., Osoh, M. N., & Thomas, C. G. (2022). Up-skilling metalwork technology in TVET institutions in Rivers State for relevance in the 21st-century workplace. Asian Journal of Education and Social Studies, 31(3), 1–7. https://doi.org/10.9734/ajess/2022/v31i330747
Ashby, M. F., Shercliff, H., & Cebon, D. (2018). Materials: Engineering, science, processing and design (4th ed.). Butterworth-Heinemann.
Chukwu, D. U., Omeje, H. O., Ojo, S. A., Okekpa, A. A., Vershima, M. N., & Kechere, I. C. (2024). Industry evolving needs and TVET institutional offerings: A qualitative response to the on-demand skill gap in Nigerian woodwork technology institutions. International Journal of Vocational Education and Training Research, 10(1), 1–7. https://doi.org/10.11648/j.ijvetr.20241001.11
Chukwu, M., Nwosu, E., & Okoro, T. (2024). Curriculum gaps in material science for machining: Bridging education and industry needs in Nigeria. International Journal of Technical Education, 12(2), 102–115.
Das, A., Das, S. R., Panda, J. P., Dey, A., Gajrani, K. K., Somani, N., & Gupta, N. (2022). Machine learning-based modelling and optimization in hard turning of AISI D6 steel with newly developed AlTiSiN coated carbide tool. arXiv. https://arxiv.org/abs/2202.00596
Das, S., Kumar, P., & Singh, A. (2023). Machine learning applications in material selection for metal machining processes. Materials Today: Proceedings, 58, 134–141. https://doi.org/10.1016/j.matpr.2022.12.095
Kalpakjian, S., & Schmid, S. R. (2022). Manufacturing engineering and technology (8th ed.). Pearson.
Kumar, A., Banga, H. K., & Singh, P. (2023). A review on sustainable machining of advanced materials using hybrid techniques. Journal of Cleaner Production, 397, Article 136611. https://doi.org/10.1016/j.jclepro.2023.136611
Ogunleye, A., & Shittu, A. (2021). Implementation of suitable metalwork technology workplace in tertiary institutions in Nigeria: Challenges and recommendations. In International Conference on Educational Research and Technology. https://icertpublication.com/index.php/eduphoria/eduphoria-vol-2-issue-3-2/implementation-of-suitable-metalwork-technology-workplace-in-tertiary-institutions-in-nigeria-challenges-and-recommendations/
Ogunleye, J., & Shittu, A. (2022). Modernizing material selection methods in Nigerian manufacturing: The role of digital tools and sustainable practices. Industrial Engineering Review, 28(4), 211–227.
Rahman, M. M., & Zitoune, R. (2022). Recent advancements in CNC machining technologies and their role in Industry 4.0. Procedia CIRP, 105, 245–250. https://doi.org/10.1016/j.procir.2022.02.041
Singh, A., Kumar, R., & Sharma, V. (2023). Effects of material properties on tool wear and surface integrity in metal machining: A review. Journal of Manufacturing Processes, 87, 112–125. https://doi.org/10.1016/j.jmapro.2023.03.007
Zhou, Y., Lin, J., & Wu, T. (2022). Thermal effects in machining titanium alloys: A study on chip formation and tool wear. International Journal of Advanced Manufacturing Technology, 119(1), 645–659. https://doi.org/10.1007/s00170-022-09123-6
