Mathematical Modelling and Optimization of Tool Geometry to Machine Hard Metals Using PCBN Inserts

DOI

10.2991/978-94-6463-252-1_103How to use a DOI?

Keywords

Hard metals; Tool Geometry; Machining Force; Mathematical Modelling; Artificial Neural Networks; Optimization; Genetic Algorithm; Experimental Validation

Abstract

Hard metals are highly preferred in critical applications to withstand severe stresses and deformations. But they pose difficulties while machining in the form of rapid tool-wear and poor dimensional accuracy of the work-pieces. In conventional practice, hard metals are annealed to facilitate machining and hardness is restored back after machining. This is followed by grinding operation to finish the components. Hence, each component undergoes two stages of heat treatment and grinding operation additionally, which increases production time and cost apart from higher process rejections. This study and experimental work are carried out to facilitate direct machining of hard metals without the need of heat treatment and grinding operations. As any machining operation is highly influenced by the tool geometry, in the current experiments, the tool geometry is varied and corresponding machining forces are measured. Using experimental data, mathematical model is formulated with Artificial Neural Networks to relate the machining force with tool-geometry. Optimum tool geometry for minimum of the machining force is identified using Genetic Algorithm and the same is validated experimentally. The result shows that the machining forces are least at the optimum tool geometry to facilitate direct machining of hard metals.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Syed Adil
AU  - A. Krishnaiah
AU  - D. Srinivas Rao
PY  - 2023
DA  - 2023/11/09
TI  - Mathematical Modelling and Optimization of Tool Geometry to Machine Hard Metals Using PCBN Inserts
BT  - Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023)
PB  - Atlantis Press
SP  - 1022
EP  - 1030
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-252-1_103
DO  - 10.2991/978-94-6463-252-1_103
ID  - Adil2023
ER  -