Optimization of Process Parameters Applied to a Prototype Selective Laser Sintering System

Enzi, Abass; Mynderse, James A.

doi:10.1115/imece2017-70591

Cited by 3 publications

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“…The overall objective of this research is the optimization of SLS process parameters to improve surface roughness and porosity in finished parts. Five relevant process parameters were identified: side step, forward step, speed, layer depth, and platform temperature [11]. In this work, the design, building, and validation of a desktop-sized prototype SLS system are described.…”

Section: Introductionmentioning

confidence: 99%

Design and experimental validation of a small-scale prototype selective laser sintering system

Enzi

Mynderse

2019

SN Appl. Sci.

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Selective laser sintering (SLS) system is an additive manufacturing technique used in a variety of different applications, such as tool industries, medicine, aerospace, automotive, and electronics. A prototype SLS system was designed, built, and validated to improve SLS-manufactured part quality in terms of porosity and surface roughness. The prototype SLS system was designed for laboratory use only. Critical process parameters were identified to optimize the manufacturing process: forward step, side step, speed, platform temperature, and layer depth. The most essential defects associated with the SLS manufacturing process are porosity, shrinkage, surface roughness, and reduced hardness. The goals of this work were to design and build the prototype SLS system, then to quantify the effects of the selected process parameters on manufacturing defects and minimize manufacturing defects. Validation data from each SLS subsystem was compared to manufacturing simulation program parameters. Validation data included laser power, laser beam diameter, gantry motion in two axes, gantry vibration, and CAD/CAM interface program. Revisions of the prototype SLS system are proposed to maintain the resolution of 0.003 m and cost of less than $10,000.

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Section: Introductionmentioning

confidence: 99%

Design and experimental validation of a small-scale prototype selective laser sintering system

Enzi

Mynderse

2019

SN Appl. Sci.

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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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Optimization of Selective Laser Sintering Three-Dimensional Printing of Thermoplastic Polyurethane Elastomer: A Statistical Approach

Rahman,

Ahmed,

Karim

et al. 2023

JMMP

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This research addresses the challenge of determining the optimal parameters for the selective laser sintering (SLS) process using thermoplastic polyurethane elastomer (TPU) flexa black powder to achieve high-quality SLS parts. This study focuses on two key printing process parameters, namely layer thickness and the laser power ratio, and evaluates their impact on four output responses: density, hardness, modulus of elasticity, and time required to produce the parts. The primary impacts and correlations of the input factors on the output responses are evaluated using response surface methodology (RSM). A particular response optimizer is used to find the optimal settings of input variables. Additionally, the rationality of the model is verified through an analysis of variance (ANOVA). The research identifies the optimal combination of process parameters as follows: a 0.11 mm layer thickness and a 1.00 laser power ratio. The corresponding predicted values of the four responses are 152.63 min, 96.96 Shore-A, 2.09 MPa, and 1.12 g/cm3 for printing time, hardness, modulus of elasticity, and density, respectively. These responses demonstrate a compatibility of 66.70% with the objective function. An experimental validation of the predicted values was conducted and the actual values obtained for printing time, hardness, modulus of elasticity, and density at the predicted input process parameters are 159.837 min, 100 Shore-A, 2.17 MPa, and 1.153 g/cm3, respectively. The errors between the predicted and experimental values for each response (time, hardness, modulus of elasticity, and density) were found to be 4.51%, 3.04%, 3.69%, and 2.69%, respectively. These errors are all below 5%, indicating the adequacy of the model. This study also comprehensively describes the influence of process parameters on the responses, which can be helpful for researchers and industry practitioners in setting process parameters of similar SLS operations.

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Optimization of Process Parameters Applied to a Prototype Selective Laser Sintering System

Cited by 3 publications

References 0 publications

Design and experimental validation of a small-scale prototype selective laser sintering system

Design and experimental validation of a small-scale prototype selective laser sintering system

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

Optimization of Selective Laser Sintering Three-Dimensional Printing of Thermoplastic Polyurethane Elastomer: A Statistical Approach

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