A hybrid PSO–BFO evolutionary algorithm for optimization of fused deposition modelling process parameters

Maraboina Raju, Munish Kumar Gupta (), Neeraj Bhanot and Vishal S. Sharma
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Maraboina Raju: Dr. B. R. Ambedkar NIT Jalandhar
Munish Kumar Gupta: NIT
Neeraj Bhanot: Indian Institute of Management
Vishal S. Sharma: Dr. B. R. Ambedkar NIT Jalandhar

Journal of Intelligent Manufacturing, 2019, vol. 30, issue 7, No 13, 2743-2758

Abstract: Abstract Fused deposition modeling (FDM), a well known 3D printing technology is widely used in various sorts of industrial applications because of its ability to manufacture complex objects in the stipulated time. However, the proper selection of input process parameters in FDM is a tedious task that directly affects the part performance. Here, in this work, the research efforts have been made to optimize the FDM process parameters in order to find out the best parameter setting as per the mechanical and surface quality perspectives by using hybrid particle swarm and bacterial foraging optimization (PSO–BFO) evolutionary algorithm. Taguchi L18 orthogonal array was used for the development of acro-nitrile butadiene styrene based 3D components by considering layer thickness, support material, model interior and orientation as a process parameters. Further, the relationships among selected FDM process parameters and output responses such as hardness, flexural modulus, tensile strength and surface roughness were established by using linear multiple regression. Then, the effects of individual process parameters on selected response parameters were examined by signal to noise ratio plots. Finally, a multi-objective optimization of process parameters has been performed with hybrid PSO–BFO, general PSO and BFO algorithm, respectively. The overall results reveal that the layer thickness of 0.007 mm, support material type sparse, part orientation of 60 $${^\circ }$$ ∘ and model interior of high density helps in achieving desired performance level.

Keywords: Evolutionary algorithm; Mechanical properties; Optimization; Surface roughness; Rapid prototyping (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10845-018-1420-0

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