Mohammed YUNUS, Mohammad S. ALSOUFI
Mechanical Engineering Department, College of Engineering and Islamic Architecture,
Umm Al-Qura University, Abidiah, Makkah, Kingdom of Saudi Arabia
Abstract: Implementation of the “Selective Inhibition type of Sintering (SIS)” method in the field of additive manufacturing, which develops the components by building layers, depends upon the proper selection of their optimum input attributes. Components manufacturing as per anticipated size is accomplished by the optimization of factors, namely, heater power (H), layer height (L), the feed rate of the heater (F) and roller (R), and support’s temperature (S) using “Multi-Response Particle Swarm Optimization (MRPSO)”. Analysis of variance was employed to substantiate the competence of the established models. Trials have shown that mechanical properties of manufactured components are characterized by Design of experiments in a direct relationship with F and R, but inversely related to H and L. Finally, unique MRPSO algorithm has been employed for parallel optimization of multiple outputs. Introduced mutation functions (of genetic algorithm) into MRPSO algorithm to prevent early convergence. Optimal-solutions of Pareto-front achieved by MRPSO were graded by the compound grades (resulting from maximum deviation theory) to increase preciseness in making the decisions. The MRPSO analysis offered valuable information for monitoring the factors to improve the accuracy of SIS components. Massive optimal-solution data have been generated for every possible combination of factors to maximize the responses.
Keywords: Selective Inhibition Sintering; Multi-objective Optimization; ANOVA; Mechanical Properties; Additive Manufacturing; MOPSO.
CITE THIS PAPER AS:
Mohammed YUNUS, Mohammad S. ALSOUFI, Multi-objective optimizations of mechanical characteristics of objects in computer aided
SIS manufacturing process using empirical PSO algorithm, Romanian Journal of Information Technology and Automatic Control, ISSN 1220-1758, vol. 30(3), pp. 23-36, 2020. https://doi.org/10.33436/v30i3y202002