The Impact of FDM Process Parameters on the Compression Strength of 3D Printed PLA Filaments for Dental Applications

Hamed, Mostafa; Abbas, Tahseen Fadhil

doi:10.12913/22998624/169468

Cited by 5 publications

(1 citation statement)

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“…The component strength to weight ratio has a major role in infill pattern selection; lighter components print more quickly and with less material consumption. Compared to material costs, print expenses have a greater influence on production costs [12]. The results indicated by optimizing process variables such as: layer thickness, infill density, infill patterning, raster direction and component build orientation, etc.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Fused Deposition Modelling Process Parameters for Medical Grade Polymethylmethacrylate Flexural Strength

Obaeed,

Hamdan

2024

Adv. Sci. Technol. Res. J.

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The production of functional parts, including those employed by the biomedical industry has been achieved a promising candidate in fused deposition modelling (FDM). The essential properties of these biomedical parts which manufactured by additive manufacturing as compared to some other conventional manufacturing processes depend on structural and process parameters rather than material properties alone. Regarding to the evaluation the flexural strength of medical-grade, polymethylmethacrylate (PMMA) has been received relatively very little investigation to date. PMMA is a biocompatible filament that be used in manufacturing of patient-specific implants such as dental prosthesis and orthopaedic implants. The proposed work explores the effect of three process parameters that vary with respect of three levels on the flexural strength. These levels can be specified by layer height (120, 200, 280 µm), infill density (40, 65, 90%) and skewing angle (0º, 45º, 90º) on the flexural strength of medical-grade PMMA. Maximum and minimum flexural strength that be obtained in this work about 93 and 57 MPa respectively. The analysis of variance (ANOVA) results shows that the most effective factor is the layer height followed by infill density. The flexural strength rises significantly with decreases layer height and the skewing angle is in zero direction. The process parameters have been optimized through utilizing of genetic algorithms. The optimal results that emerged based on genetic algorithm technique are approximately 276 μm as layer height, 46% infill density, and skewing angle 89º, which maximize the flexural strength to 97 MPa at crossover for ten generation.

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Section: Introductionmentioning

confidence: 99%

Optimizing Fused Deposition Modelling Process Parameters for Medical Grade Polymethylmethacrylate Flexural Strength

Obaeed,

Hamdan

2024

Adv. Sci. Technol. Res. J.

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A Statistical Investigation and Prediction of the Effect of FDM Variables on Flexural Stress of PLA Prints

Mansor,

Shabeeb,

Hussein

et al. 2024

Tikrit j. eng. sci.

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Due to its many engineering applications, low manufacturing costs, and environmental friendliness, 3D printing is considered one of the most promising manufacturing technologies. The quality of printed parts will inevitably be affected by the controllable variables used in the 3D printing process. The present study aims to investigate how different printing process parameters affect the bending strength of PLA prints. The ASTM D790 standard was used to fabricate the samples in this work, while the Taguchi principle was used to design the experiments. The following values were chosen: shell width (0.8, 1.2, 1.6, and 2 mm), layer thickness (0.15, 0.2, 0.25, and 0.3 mm), and infill density (40%, 60%, 80%, and 100%). The results showed that fill density is the most effective variable for improving bending strength. Measurements of infill density (100%), layer thickness (0.15 mm), and shell width (2 mm) gave the best results, which were calculated to be 83.1479 MPa in bending test. The mathematical model in this study was developed using linear regression analysis, and the residuals confirmed that the model fit the data well, with a maximum error of 6.1%.

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Experimental and Investigation of ABS Filament Process Variables on Tensile Strength Using an Artificial Neural Network and Regression Model

Hamed

2024

NJES

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Fused deposition modeling (FDM) is a commonly used 3D printing technique that involves heating, extruding, and depositing thermoplastic polymer filaments. The quality of FDM components is greatly influenced by the chosen processing settings. In this study, the Taguchi technique and artificial neural network were employed to predict the ultimate tensile strength of FDM components and establish a mathematical model. The mechanical properties of ABS were analyzed by varying parameters such as layer thickness, printing speed, direction angle, number of parameters, and nozzle temperature at five different levels. FDM 3D printers were used to fabricate samples for testing, following the ASTM-D638 standards, using the Taguchi orthogonal array experimental design method to set the process parameters. The results indicated that the printing process factors had a significant impact on tensile strength, with test values ranging from 31 to 38 MPa. The neural network achieved a maximum error of 5.518% when predicting tensile strength values, while the analytical model exhibited an error of 19.376%.

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The Impact of FDM Process Parameters on the Compression Strength of 3D Printed PLA Filaments for Dental Applications

Cited by 5 publications

References 0 publications

Optimizing Fused Deposition Modelling Process Parameters for Medical Grade Polymethylmethacrylate Flexural Strength

Optimizing Fused Deposition Modelling Process Parameters for Medical Grade Polymethylmethacrylate Flexural Strength

A Statistical Investigation and Prediction of the Effect of FDM Variables on Flexural Stress of PLA Prints

Experimental and Investigation of ABS Filament Process Variables on Tensile Strength Using an Artificial Neural Network and Regression Model

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