Title: Enhancement of the torsional strength of commercial ABS samples manufactured using a tabletop 3D printer: an application of innovative hybridised tools and techniques

Authors: Boppana V. Chowdary; Schuravi Mallian

Addresses: Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, University of the West Indies, St. Augustine Campus, Trinidad, Trinidad and Tobago ' Pyramid Engineering and Fabricating Services Limited, Point Lisas Industrial Estate, Couva, Trinidad and Tobago

Abstract: This study focuses on performance enhancement of the fused filament fabrication (FFF) of ABS part using a tabletop 3D printer. The objective is accomplished by understanding the effects of raster width, raster angle, part orientation and layer thickness on build time, material consumption and maximum torsional stress by application of hybridised machine learning techniques like artificial neural network (ANN) and genetic algorithm (GA). Further, response surface methodology (RSM)-based Box-Behnken experimental design is followed to develop the initial regression model. Furthermore, multi-objective GA (MOGA) tool is deployed to determine the optimum parameter values. The study had shown that complex nonlinear relationship exists between the process parameters and performance measures and the ANN-GA technique had a better fit when compared to the RSM-GA model. Thus, ANN-GA could be a promising multi-objective approach for optimisation of the FFF process as an alternative commercial manufacturing technique to meet the contemporary industry needs.

Keywords: additive manufacturing; material extrusion; torsional strength; build time; material consumption; response surface methodology; RSM; Box-Behnken design; artificial neural network; ANN; multi-objective optimisation; genetic algorithm.

DOI: 10.1504/IJRIC.2023.132940

International Journal of Research, Innovation and Commercialisation, 2023 Vol.5 No.1, pp.1 - 27

Received: 06 Jan 2023
Accepted: 02 Mar 2023
Published online: 20 Aug 2023 *

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