Journal
COMPUTERS & INDUSTRIAL ENGINEERING
Volume 156, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cie.2021.107238
Keywords
Additive manufacturing; Fused deposition modeling; Process parameter optimization; Design of experiments; Polylactic acid
Funding
- Italian Ministry of Education, University and Research [TESUN-83486178370409, CAP. 1694 TIT. 232 ART. 6]
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The research uses a Design of Experiments approach to study the effects of FDM process parameters on PLA component production, optimizes response outputs through mathematical models, and verifies the optimizations through experiments.
The results show that different parameter settings can lead to different goals in terms of part quality and process efficiency.
This method can help designers determine the optimal process parameter settings to enhance both part quality and process efficiency.
YY Fused Deposition Modeling (FDM) is an additive manufacturing technique for fabricating parts directly from computer-aided design data by melting, extruding, and resolidifying a thermoplastic filament. This paper presents a methodology for optimizing both process efficiency, i.e., time and energy consumption, and part quality, i.e., surface roughness and dimensional accuracy, of Polylactic Acid (PLA) components produced by FDM. In this work, a Design of Experiments (DoE) approach is adopted to quantify the effects of deposition parameters on process efficiency and part quality outputs. Specifically, the investigated input parameters are layer height, fill density, extruder temperature, part orientation, number of shells, print speed and retraction speed. The mathematical models relating the significant process parameters to the output responses are developed and the responses are optimized considering different scenarios. An experimental validation is performed to test the adequacy of such optimizations. These experimental results showed that, according to the context, different parameter settings pursue different goals in terms of part quality and process efficiency. The proposed approach may effectively help designers determine process parameters' settings to optimize both part quality and process efficiency and can be applied to either prototype or part production.
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