STUDY ON AI-GENERATED 3D PRINTED ABS PARTS: INFLUENCE OF INFILL TYPE AND PERCENTAGE USING FACTORIAL ANOVA, REGRESSION, AND MULTI-OBJECTIVE OPTIMIZATION

Cristian POP, Carmen STICLARU, Florina POP, Aurel TULCAN

Abstract


This study presents a comprehensive factorial analysis and ANOVA-based approach to optimizing 3D printing efficiency by investigating the impact of infill type and infill percentage on printing time and material consumption. The research integrates text-based and code-based CAD generation based on AI tools for designing the 3D printed part, followed by statistical evaluation using MATLAB. The results provide useful insights into balancing print speed and material utilization, enhancing additive manufacturing efficiency. The material used for the investigated printed parts is ABS.

References


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