Experimental investigation and optimization of the FDM process using PLA

Sahoo, Sudhansu S.; Sutar, Harekrushna; Senapati, Pragyan; Mohanto, Bhabani Shankar; Dhal, Prasant Ranjan; Baral, Subrat Kumar

doi:10.1016/j.matpr.2022.11.208

Cited by 6 publications

(5 citation statements)

References 7 publications

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“…As with any manufacturing process, FDM can be optimised, but this requires knowledge of the machine parameters. A recent study by S. Sahoo et al [ 14 ] investigated layer thickness (0.1–0.2–0.3 mm), fill density (25–35–45%), and printing speed (40–50–60 mm/s). The optimal parameters for high strength were a layer thickness of 0.1 mm, a density of 35%, and a printing speed of 60 mm/s.…”

Section: Introductionmentioning

confidence: 99%

Study of walls’ influence on the mechanical properties of 3D printed onyx parts: Experimental, analytical and numerical investigations

Nikiema,

Sène,

Balland

et al. 2023

Heliyon

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Section: Introductionmentioning

confidence: 99%

Study of walls’ influence on the mechanical properties of 3D printed onyx parts: Experimental, analytical and numerical investigations

Nikiema,

Sène,

Balland

et al. 2023

Heliyon

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“…All of the constructions had the same infill density of 10%. This study chose PLA as the printing medium because it performed well in various applications [15,16].…”

Section: Methodsmentioning

confidence: 99%

Optimization of the Printing Parameters to Improve the Surface Roughness in Fused Deposition Modeling

Kothandaraman,

Balasubramanian

2023

E3S Web Conf.

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A better surface finish is an essential requirement of any component in particular medical components. The recent development in additive manufacturing technology produces components with a good surface finish. However, the optimization of process parameters helps to achieve a better surface finish. This paper focuses on the optimization of printing parameters of the surface roughness of a flat object developed from an FDM printer. FDM (Fused Deposition Modeling) is a layer-by-layer deposition process to develop 3D objects. It uses solid-state material (Filament) to print the product by melting and depositing the material on the printing bed. Several factors in the FDM process can affect the product’s quality. The parameters such as printing temperature, bed temperature, printing speed, fill density, layer thickness, and air gap influence the quality of the printed products. This investigation has considered printing temperature, layer height, and printing as process parameters. In addition, the parameter affecting the printed object’s surface finish is determined using ANOVA optimization and S/N ratios. PLA (Polylactic Acid) is taken as study material which is one of the feedstocks used in polymer filament and finds its applications in implant printing and medical tools.

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“…Optimization techniques play a crucial role in identifying optimal parameters for enhanced performance. Subsequently, researchers [ 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ] have applied various optimization techniques to refine the parameters directly affecting the mechanical performance of 3D-printed parts. Response surface methodology is a common method used in scientific papers [ 41 , 43 , 48 , 50 , 51 , 52 , 53 , 54 , 55 ] for optimization analysis.…”

Section: Introductionmentioning

confidence: 99%

“…The research of [ 42 ] investigates the impact of three process variables, namely layer thickness, infill percentage, and print speed, on the hardness and strength of PLA fabricated specimens based on the Taguchi L9 orthogonal array. The study unveiled that both layer thickness and infill percentage play a vital role in determining the mechanical properties of FDM structures.…”

Section: Introductionmentioning

confidence: 99%

Optimization of 3D Printing Parameters for Enhanced Surface Quality and Wear Resistance

et al. 2023

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In recent years, there has been a growing interest in the field of 3D printing technology. Among the various technologies available, fused deposition modeling (FDM) has emerged as the most popular and widely used method. However, achieving optimal results with FDM presents a significant challenge due to the selection of appropriate process parameters. Therefore, the objective of this research was to investigate the impact of process parameters on the tribological and frictional behavior of acrylonitrile butadiene styrene (ABS) and polylactic acid (PLA) 3D-printed parts. The design of experiments (DOE) technique was used considering the input design parameters (infill percentage and layer thickness) as variables. The friction coefficient values and the wear were determined by experimental testing of the polymers on a universal tribometer employing plane friction coupling. Multi-response optimization methodology and analysis of variance (ANOVA) were used to highlight the dependency between the coefficient of friction, surface roughness parameters, and wear on the process parameters. The optimization analysis revealed that the optimal 3D printing input parameters for achieving the minimum coefficient of friction and linear wear were found to be an infill percentage of 50% and layer thickness of 0.1 mm (for ABS material), and an infill percentage of 50%, layer thickness of 0.15 mm (for PLA material). The suggested optimization methodology (which involves minimizing the coefficient of friction and cumulative linear wear) through the optimized parameter obtained provides the opportunity to select the most favorable design conditions contributing to a more sustainable approach to manufacturing by reducing overall material consumption.

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Experimental investigation and optimization of the FDM process using PLA

Cited by 6 publications

References 7 publications

Study of walls’ influence on the mechanical properties of 3D printed onyx parts: Experimental, analytical and numerical investigations

Study of walls’ influence on the mechanical properties of 3D printed onyx parts: Experimental, analytical and numerical investigations

Optimization of the Printing Parameters to Improve the Surface Roughness in Fused Deposition Modeling

Optimization of 3D Printing Parameters for Enhanced Surface Quality and Wear Resistance

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