ArticleScientific reports2022
Mechanical strength and shape accuracy optimization of polyamide FFF parts using grey relational analysis.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Article
- Application of an IEW-CRITIC-CoCoSo method based on interval-valued T-spherical fuzzy for optimizing process parameters of 3D printed recycled polypropylene composites.Scientific reports · 2026Article
- Multi-objective optimization of surface finish and VOC emissions in FFF 3D printing using ANN-NSGA-II approach.Scientific reports · 2025Article
- Multi objective optimization of FDM 3D printing parameters set via design of experiments and machine learning algorithms.Scientific reports · 2025Article
- Analyzing the effects of printing parameters to minimize the dimensional deviation of polylactic acid parts by applying three different decision-making approaches.Scientific reports · 2024Article
- The Influence of Fused Deposition Modeling Parameters on the Properties of PA6/PA66 Composite Specimens by the Taguchi Method and Analysis of Variance.3D printing and additive manufacturing · 2024Article
- Effects of thermomechanical parameters on surface texture in filament materials extrusion: outlook and trends.F1000Research · 2024Review
- Experimental investigation on fatigue life and tensile strength of carbon fiber-reinforced PLA composites based on fused deposition modeling.Scientific reports · 2023Article
- Material Extrusion of Multi-Polymer Structures Utilizing Design and Shrinkage Behaviors: A Design of Experiment Study.Polymers · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper investigates the effect of different additive manufacturing process parameters such as chamber temperature, Printing temperature, layer thickness, and print speed on five essential parameters that characterize the manufactured components: cylindricity, circularity, strength, and Young's modulus, and deformation by Gray Relational Analysis method simultaneously. Taguchi method was used to design the experiments, and the PA6 cylindrical parts were fabricated using a German RepRap X500® 3D printer. Then the Gray Relational Grade (GRG) values were calculated for all experiments. In the 8th trial, the highest value of GRG was observed. Then, to discover the optimal parameters, the GRG data were analyzed using ANOVA and S/N analysis, and it was determined that the best conditions for enhancing GRG are 60 °C in the chamber temperature, 270 °C in the printing temperature, 0.1 mm layer thickness, and 600 mm/min print speed. Finally, by using optimal parameters, a verification test was performed, and new components were investigated. Finally, comparing the initial GRG with the GRG of the experiments showed an improvement in the gray relational grade (14%) which is accompanying with improving of GRG value.
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Registered trials
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