OPTIMIZATION OF FUSED DEPOSITION MODELING (FDM) PROCESS PARAMETERS FOR IMPROVING DIMENSIONAL ACCURACY USING GREY TAGUCHI METHOD

The 3D printers process is applied to create prototype components, but at the last 3D Printers are often applied as last products. So, high accuracy is required in this case. In this research will find the optimal setting of the dimensional accuracy 3D printers based fused deposition modeling. The method used is the Taguchi method, the reason for using this method its efficiency, this is because the Orthogonal Array matrix requires less number of experiments than the classical experimental design. Analysis of Variance is also needed in this method to see the factors that significantly influence the response variable. The results of this study indicate that the factors that significantly influence is printspeed by contributing 53.08%, flowrate contributes 16.4%, and temperature heater block contributes 3.85% and optimal setting is temperature heater block 190º, print speed 60mm/s and flowrate 6.28 mm3/s. (A1, C3 dan D2).

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Parameters Optimization of FDM for the Quality of Prototypes Using an Integrated MCDM Approach

Additive Manufacturing Technologies From an Optimization Perspective - Advances in Logistics, Operations, and Management Science ◽

10.4018/978-1-5225-9167-2.ch010 ◽

2019 ◽

pp. 199-220

Author(s):

Jagadish ◽

Sumit Bhowmik

Keyword(s):

Process Parameters ◽

Shell Thickness ◽

Fused Deposition Modeling ◽

Dimensional Accuracy ◽

Parameters Optimization ◽

Layer Height ◽

Fused Deposition ◽

Optimal Setting ◽

Deposition Modeling

Fused deposition modeling (FDM) is one of the emerging rapid prototyping (RP) processes in additive manufacturing. FDM fabricates the quality prototype directly from the CAD data and is dependent on the various process parameters, hence optimization is essential. In the present chapter, process parameters of FDM process are analyzed using an integrated MCDM approach. The integrated MCDM approach consists of modified fuzzy with ANP methods. Experimentation is performed considering three process parameters, namely layer height, shell thickness, and fill density, and corresponding response parameters, namely ultimate tensile strength, dimensional accuracy, and manufacturing time are determined. Thereafter, optimization of FDM process parameters is done using proposed method. The result shows that exp.no-4 yields the optimal process parameters for FDM and provides optimal parameters as layer height of 0.08 mm, shell thickness of 2.0 mm and fill density of 100%. Also, optimal setting provides higher ultimate TS, good DA, and lesser MT as well as improving the performance and efficiency of FDM.

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A Study on Dimensional Accuracy of Fused Deposition Modeling (FDM) Processed Parts using Fuzzy Logic

Journal for Manufacturing Science and Production ◽

10.1515/jmsp-2013-0010 ◽

2013 ◽

Vol 13 (3) ◽

pp. 183-197 ◽

Cited By ~ 23

Author(s):

Ranjeet Kumar Sahu ◽

S.S. Mahapatra ◽

Anoop Kumar Sood

Keyword(s):

Experimental Data ◽

Fuzzy Logic ◽

Process Parameters ◽

Fused Deposition Modeling ◽

Dimensional Accuracy ◽

Experimental Results ◽

Percentage Error ◽

Fuzzy Decision Making ◽

Fused Deposition ◽

Deposition Modeling

AbstractFused Deposition Modeling (FDM) is an additive manufacturing technology for rapid prototyping that can build intricate parts in minimal time with least human intervention. The process parameters such as layer thickness, orientation, raster angle, raster width and air gap largely influence on dimensional accuracy of built parts which can be expressed as change in length, width and thickness. This paper presents experimental data and a fuzzy decision making logic in integration with the Taguchi method for improving the dimensional accuracy of FDM processed ABSP 400 parts. It is observed that length and width decreases but thickness shows positive deviation from desired value of the built part. Experimental results indicate that optimal factor settings for each response are different. Therefore, all the three responses are expressed in a single response index through fuzzy logic approach. The process parameters are optimized with consideration of all the performance characteristics simultaneously. Finally, an inference engine is developed to perform the inference operations on the rules for fuzzy prediction model based on Mamdani method. Experimental results are provided to confirm the effectiveness of the proposed approach. The predicted results are in good agreement with the values from the experimental data with average percentage error of less than 4.5.

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Multifactor optimization of FDM process parameters for development of rapid tooling using SiC/Al2O3-reinforced LDPE filament

Journal of Thermoplastic Composite Materials ◽

10.1177/0892705718808572 ◽

2018 ◽

Vol 33 (5) ◽

pp. 581-598 ◽

Cited By ~ 5

Author(s):

Piyush Bedi ◽

Rupinder Singh ◽

IPS Ahuja

Keyword(s):

Particle Size ◽

Process Parameters ◽

Fused Deposition Modeling ◽

Dimensional Accuracy ◽

Rapid Tooling ◽

Scanning Calorimetry ◽

Fused Deposition ◽

Significant Process ◽

Deposition Modeling ◽

Sic Reinforcement

In this work, multifactor optimization of fused deposition modeling (FDM) process parameters has been reported for in-house prepared feedstock filament comprising of SiC/Al2O3 reinforced in recycled low-density polyethylene (LDPE) matrix with different particle sizes (i.e. single particle size (SPS), double particle size (DPS), and triple particle size (TPS) in different proportions). This study has been conducted on Al2O3-based DPS reinforcement in LDPE, which came out as a better solution during pilot experimentation in comparison to SPS, TPS, and SiC reinforcement, for printing of functional prototypes as rapid tooling (RT). The result of study suggests that infill angle in the FDM process is the most significant process parameter (contributing around 93%) for preparation of RT as regards dimensional accuracy and hardness is concerned. The RT so prepared is thermally stable as evident from differential scanning calorimetry analysis. Further, the photomicrographs observed in different planes suggest that, at the proposed settings, RT has a uniform distribution of reinforcement in LDPE matrix and can be gainfully used in light machining applications.

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