Cutting Forces, Surface Roughness and Tool Wear Quality Assessment Using ANN and PSO Approach During Machining of MDN431 with TiN/AlN-Coated Cutting Tool-Reference-Cited by-同舟云学术

Cutting Forces, Surface Roughness and Tool Wear Quality Assessment Using ANN and PSO Approach During Machining of MDN431 with TiN/AlN-Coated Cutting Tool

Published:2019-03-06 Issue:9 Volume:44 Page:7465-7477
ISSN:2193-567X
Container-title:Arabian Journal for Science and Engineering
language:en
Short-container-title:Arab J Sci Eng

Author:

Badiger Pradeep V.^ORCID,Desai Vijay,Ramesh M. R.,Prajwala B. K.,Raveendra K.

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Link

http://link.springer.com/content/pdf/10.1007/s13369-019-03783-0.pdf

Reference40 articles.

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2. Musfirah, A.H.; Ghani, J.A.; Haron, C.H.C.: Tool wear and surface integrity of Inconel 718 in dry and cryogenic coolant at high cutting speed. Wear 376–377, 125–133 (2017). https://doi.org/10.1016/j.wear.2017.01.031

3. Chabbi, A.; Yallese, M.A.; Meddour, I.; Nouioua, M.; Mabrouki, T.; Girardin, F.: Predictive modeling and multi-response optimization of technological parameters in turning of Polyoxymethylene polymer (POM C) using RSM and desirability function. Meas. J. Int. Meas. Confed. 95, 99–115 (2017). https://doi.org/10.1016/j.measurement.2016.09.043

4. Hanief, M.; Wani, M.F.; Charoo, M.S.: Modeling and prediction of cutting forces during the turning of red brass (C23000) using ANN and regression analysis. Eng. Sci. Technol. Int. J. 20, 1220–1226 (2017). https://doi.org/10.1016/j.jestch.2016.10.019

5. Mia, M.; Dhar, N.R.: Response surface and neural network based predictive models of cutting temperature in hard turning. J. Adv. Res. 7, 1035–1044 (2016). https://doi.org/10.1016/j.jare.2016.05.004

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