Fused Deposition modeling process parameters optimization and effect on mechanical properties and part quality: Review and reflection on present research

• Published in: Materials today : proceedings Vol. 21; pp. 1659 - 1672
• Main Authors: Jaisingh Sheoran, Ankita, Kumar, Harish
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 2020
• Subjects: Additive Manufacturing (AM); Design of Experiments (DOE); Fused Deposition modeling (FDM); Mechanical properties; Optimization; Process parameters; ANN; PLA; CCD; Additive Manufacturing (AM); Mechanical properties; BFO; GMDH; AM; PETG; RSM; PSO; Optimization; FDM; OFAT; FEM; ABS; Design of Experiments (DOE); ANOVA; Fused Deposition modeling (FDM); Process parameters; GA; PEEK
• ISSN: 2214-7853; 2214-7853
• DOI: 10.1016/j.matpr.2019.11.296

Additive manufacturing (AM) was developed initially as a technique for rapid prototyping, to visualize, test and authenticate a design, before end-user production of the design. In recent years, Additive Manufacturing (AM) technique Fused Deposition modeling (FDM), has developed to become a rapid manufacturing technique because of the ability to produce complex parts layer-by-layer in lesser production cycle time than as compared to conventional machining processes. FDM also offers the advantage of the lowest cost because of no tooling requirements. Despite these advantages, building parts by utilizing FDM for end-use is still a demanding endeavor. This is because FDM has multiple processing parameters, which affect the part quality, mechanical properties, build time and dimensional accuracy. These FDM processing parameters include air gap, build orientation, infill percentage, raster angle, layer thickness, etc. Depending upon the application, for which the part is manufactured, careful selection of these process parameters needs to be done. For a specific output requirement, some of the process parameters are significant than the rest, these significant process parameters need to be identified and optimized. Due to this, researchers have explored and utilized various experimental or statistical Design of Experiment (DOE) techniques for optimizing the FDM process parameters to improve the mechanical properties or part quality or both. Some of these DOE techniques include the Taguchi method, Genetic algorithm (GA), gray relational, Response surface method (RSM), fractional factorial, Artificial Neural networks (ANN), Fuzzy logic, ANOVA, etc. This article aims at reviewing the current research on the statistical and experimental design techniques for different applications or output responses such as enhancing mechanical properties, build time, part quality, etc.