E3S Web Conf.
Volume 309, 20213rd International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2021)
|Number of page(s)||7|
|Published online||07 October 2021|
Grey integrated Multiobjective-Particle Swarm Optimization (MOPSO) for Machining assessment and predictive modeling of Cutting Forces generated during Polymer nanocomposite Drilling
1,2 Materials and Morphology Laboratory,
Department of Mechanical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India -273010
* Corresponding author: email@example.com
Carbon nanomaterials reinforced composite materials have been broadly utilized in manufacturing engineering due to improved thermal resistivity, reduced weight, and other improved mechanical properties. This article highlights the drilling experimentation of zero-dimensional (0-D) Carbon nano onion (CNO) reinforced polymer composite. For this, three drilling constraints was considered viz., spindle speed, feed rate, and weight % of nanomaterial reinforced. The objective is to achieve the desired value of generated drilling forces such as Torque (Nm) and Thrust Force (N) during the machining procedure of developed composite samples. The Multiobjective-Particle Swarm Optimization (MOPSO) is utilized to achieve optimal results from the multi-decision criterion for the Machining performance. Exploiting this optimization process, non-dominated solutions were obtained, and the Pareto front was identified. Practical applications for the discovered relationships include using Grey relation analysis (GRA) to extract the most relevant finding from the Pareto Front space of optimal solutions. Using the GRA, the optimum solution was found: Spindle Speed of 1000 RPM, Feed Rate of 100 mm/min, and CNO weight percentage of 0.5. After this, a confirmation test was performed, the expected effects have been confirmed. The findings reveal that the proposed optimization module can be recommended for online quality and productivity control.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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