CUTTING TOOLS AND THEIR DESIGN FEATURES: FROM THE POINT OF VIEW OF ARTIFICIAL INTELLIGENCE

Authors

  • Aydın Heydarov Azerbaijan Technical University, Baku, Azerbaijan

DOI:

https://doi.org/10.69471/gsd-15

Keywords:

Cutting Tools, Design Features, AI, Engineering Technology

Abstract

Cutting tools are fundamental components in manufacturing and engineering processes which are essential for shaping, cutting, and decided materials like metal, wood and composites. The design of these tools calls for a delicate balance between choice of materials, geometry and performance criteria whose parameters are tailored. The emergence of artificial intelligence (AI) has turned this world all upside down. AI first uses machine learning algorithms to analyze large quantities of data about material properties, wear resistance, or cutting conditions leading to new tool designs for a given material. In addition, AI also allows real-time monitoring during machining and with adaptive control. It can improve precision as well as reduce tool wear by selecting the right speed when cutting, while slow down results in prevention of damage. Furthermore, AI-driven simulations have enabled engineers to simulate and optimize tool design in virtual environments prior to physical production. AI will continue to advance in the future holds out the promise of decreasing still further the cost and improving the performance characteristics of cutting tools. It is the combination of AI and cutting tool design to gain maximum benefits of this innovation while minimizing costs for now in the future.

References

Al-khaleeli, W. A., & Al-wswasi, M. (2024, June). The use of feature technology in selecting cutting tools and generating tool paths. Salud, Ciencia y Tecnología - Serie de Conferencias, 3(3). https://doi.org/10.56294/sctconf2024856

Al-wswasi, M., Ivanov, A., & Makatsoris, H. (2018). A survey on smart automated computer-aided process planning (ACAPP) techniques. International Journal of Advanced Manufacturing Technology, 97(1-4), 809-832. https://doi.org/10.1007/s00170-018-1742-2

Hashmi, A. W., Mali, H. S., Meena, A., Khilji, I. A., Hashmi, M. F., & Saffe, S. N. M. (2022). Artificial intelligence techniques for implementation of intelligent machining. Materials Today: Proceedings, 56(4), 1947-1955. https://doi.org/10.1016/j.matpr.2021.11.277

Ismail, N., (2005). Recognition of cylindrical and conical features using edge boundary classification. International Journal of Machine Tools and Manufacture, 45(5), 649-655. https://doi.org/10.1016/j.ijmachtools.2004.08.004

LeewayHertz. (2024, August 29). AI in predictive maintenance: Use cases, technologies, benefits, solution and implementation 2024. LeewayHertz. https://www.leewayhertz.com/ai-in-predictive-maintenance/

Neural Concept. (2024, August 26). Generative design & the role of AI engineering - Applied use cases 2024. Neural Concept. https://www.neuralconcept.com/post/generative-design-the-role-of-ai-engineering-applied-use-cases

Octavio, A. (2024, October 29). AI in the entire product development cycle: Design, simulation, and manufacturing for time-to-market success. LinkedIn. https://www.linkedin.com/pulse/ai-entire-product-development-cycle-design-simulation-octavio

Rampur, V. V., (2017). Computer aided process planning using STEP neutral file for automotive parts. International Journal of Engineering Research & Technology (IJERT), 6(4). Retrieved from http://www.ijert.org

Xiao, W., Huang, J., Wang, B., & Ji, H. (2022). A systematic review of artificial intelligence in the detection of cutting tool breakage in machining operations. Measurement, 190, Article 110748. https://doi.org/10.1016/j.measurement.2022.110748

Yusof, S., & Latif, K. (2014). Survey on computer-aided process planning. International Journal of Advanced Manufacturing Technology, 75(1-4), 77-89. https://doi.org/10.1007/s00170-014-5644-4

Downloads

Published

2024-08-30

How to Cite

Heydarov, A. (2024). CUTTING TOOLS AND THEIR DESIGN FEATURES: FROM THE POINT OF VIEW OF ARTIFICIAL INTELLIGENCE. Global Sustainable Development, 2(2), 63–70. https://doi.org/10.69471/gsd-15

Issue

Section

Articles