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Experimental Investigations and Multi-Objective Optimization of Selective Inhibition Sintering Process Using the Dragonfly Algorithm

Experimental Investigations and Multi-Objective Optimization of Selective Inhibition Sintering Process Using the Dragonfly Algorithm

Siva Kumar M., Rajamani D., Balsubramanian E.
Copyright: © 2022 |Pages: 18
ISBN13: 9781799885160|ISBN10: 179988516X|ISBN13 Softcover: 9781799885177|EISBN13: 9781799885184
DOI: 10.4018/978-1-7998-8516-0.ch005
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MLA

M., Siva Kumar, et al. "Experimental Investigations and Multi-Objective Optimization of Selective Inhibition Sintering Process Using the Dragonfly Algorithm." Applications of Artificial Intelligence in Additive Manufacturing, edited by Sachin Salunkhe, et al., IGI Global, 2022, pp. 96-113. https://doi.org/10.4018/978-1-7998-8516-0.ch005

APA

M., S. K., D., R., & E., B. (2022). Experimental Investigations and Multi-Objective Optimization of Selective Inhibition Sintering Process Using the Dragonfly Algorithm. In S. Salunkhe, H. Hussein, & J. Davim (Eds.), Applications of Artificial Intelligence in Additive Manufacturing (pp. 96-113). IGI Global. https://doi.org/10.4018/978-1-7998-8516-0.ch005

Chicago

M., Siva Kumar, Rajamani D., and Balsubramanian E. "Experimental Investigations and Multi-Objective Optimization of Selective Inhibition Sintering Process Using the Dragonfly Algorithm." In Applications of Artificial Intelligence in Additive Manufacturing, edited by Sachin Salunkhe, Hussein Mohammed Abdel Moneam Hussein, and J. Paulo Davim, 96-113. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-7998-8516-0.ch005

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Abstract

The chapter focuses on utilizing a hybrid approach of response surface methodology and dragonfly algorithm for investigations and optimization of the selective inhibition sintering (SIS) process to improve the mechanical strengths such as tensile and flexural of fabricated high density polyethylene parts. The layer thickness (LT), heater energy (HE), heater and printer feedrate (HFR & PFR) are considered as the independent variables for the investigation. The SIS experiments are planned and conducted through a response surface methodology-based box-Behnken design approach to fabricate the test specimens. The optimal SIS parameters are obtained through a swarm intelligence metaheuristic technique namely dragonfly algorithm (DFA). The optimal parameter settings of LT of 0.102 mm, HE of 28.46 J/mm2, HFR of 3.22 mm/sec, and PFR of 110.49 mm/min are achieved through DFA for improved tensile and flexural strengths of 26.21 MPa and 65.71 MPa, respectively. Further, the prediction ability of DFA was compared with particle swarm optimization algorithm.

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