A SURFACE TEXTURE EVALUATION METHODOLOGY FOR FUSED DEPOSITION MODELING (FDM) 3D PRINTED PARTS USING EXPERIMENTAL DATA AND STATISTICAL MODELING ALGORITHMS, WITH MATLAB

Dan Claudiu NEGRĂU, Salem NAZZAL, Gavril GREBENISAN, Claudiu-Ioan INDRE, Florin BLAGA, Alin Florin POP, Alin Dorin NEGRĂU

Abstract


This paper presents, in a concise and practical manner, the results of a comprehensive study  evaluating of surface texture characteristics for 3D printed parts using fused deposition modeling (FDM) technology. The results of a series of experiments coordinated by the authors were used, employing hybrid techniques and procedures, such as advanced statistical modeling techniques with advanced programming and implementation techniques in MATLAB, validated in numerical applications. Three types of engineering polymers (ABS+, PAHT, PC-FR) were investigated, considering two printing orientations (front and side), with a view to evaluating their influence (e.g., local sensitivity) on surface roughness parameters: Ra, Rz, and Rq. The implementation of the methodology, by incorporating these advanced ideas, techniques, and procedures, using Anova for factorial analysis, a Monte Carlo algorithm, a Pareto analysis, and finally generating a customized MATLAB program, has the stated objective of instantly issuing evaluated and verified recommendations for optimal combinations consisting of pairs of the following types: material orientation, characteristic parameters that play a role in minimizing deviation from target roughness values. The integrated approach of this methodology provides a robust framework to support engineers in selecting the optimal printing parameters to meet the stringent surface finish requirements in FDM applications

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