Int. J. Simul. Multidisci. Des. Optim.
Volume 12, 2021
Computation Challenges for engineering problems
|Number of page(s)||13|
|Published online||21 September 2021|
Experimental investigations and finite element analysis of milling of Inconel 718 alloy
Department of Mechanical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam 603 110, Tamil Nadu, India
* e-mail: email@example.com
Accepted: 10 August 2021
Super-alloys encompass great challenges in machinability. One such alloy of much interest in applications is Inconel 718. Its increased hardness, low thermal diffusivity and high temperature strength make it desirable for applications, at the same time rendering its machining a demanding task. Extensive studies have been performed on machinability of Inconel 718, from the turning process stand-point. However, there is found to be a comparative dearth of work on the milling process. Taking into account the versatility of end-milling within the family of milling processes and the research gap, we found that a parametric optimization (aimed at minimum machining forces) of end-milling would be a meaningful effort. An experiment was conducted to study conditions that would help us achieve the same. In our further quest for optimization, chip morphology studies using SEM occupied a special place. Bearing in mind immense prediction capabilities of computer simulations based on FEA available today, we attempted process replication of the experimental work. The significant cutting forces were chosen as the benchmark factor for this purpose and proper attention was given to validation of the FEM created. Such FEM holds promise of being resourceful to drive up efficiency, with consequent spill-over to the production line.
Key words: Inconel 718 / machinability / process optimization / milling / end-milling / chip morphology / prediction capabilities of FEA / validation of FEM
© A.C. Kuriakose et al., Published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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