Improving dimensional accuracy of Fused Deposition Modelling processed part using grey Taguchi method

2009 ◽  
Vol 30 (10) ◽  
pp. 4243-4252 ◽  
Author(s):  
Anoop Kumar Sood ◽  
R.K. Ohdar ◽  
S.S. Mahapatra
Keyword(s):  
Taguchi Method ◽  
Dimensional Accuracy ◽  
Fused Deposition Modelling ◽  
Fused Deposition ◽  
Grey Taguchi

Evaluation of geometrical benchmark artifacts containing multiple overhang lengths fabricated using material extrusion technique

2020 ◽  
Vol 14 (3) ◽  
pp. 7296-7308
Author(s):  
Siti Nur Humaira Mazlan ◽  
Aini Zuhra Abdul Kadir ◽  
N. H. A. Ngadiman ◽  
M.R. Alkahari
Keyword(s):  
3D Model ◽  
Laser Scanner ◽  
Dimensional Accuracy ◽  
Fused Deposition Modelling ◽  
Layer By Layer ◽  
Fused Deposition ◽  
Layer Technique ◽  
Subtractive Manufacturing ◽  
Manufacturing Features ◽  
Layer Problem

Fused deposition modelling (FDM) is a process of joining materials based on material entrusion technique to produce objects from 3D model using layer-by-layer technique as opposed to subtractive manufacturing. However, many challenges arise in the FDM-printed part such as warping, first layer problem and elephant food that was led to an error in dimensional accuracy of the printed parts especially for the overhanging parts. Hence, in order to investigate the manufacturability of the FDM printed part, various geometrical and manufacturing features were developed using the benchmarking artifacts. Therefore, in this study, new benchmarking artifacts containing multiple overhang lengths were proposed. After the benchmarking artifacts were developed, each of the features were inspected using 3D laser scanner to measure the dimensional accuracy and tolerances. Based on 3D scanned parts, 80% of the fabricated parts were fabricated within ±0.5 mm of dimensional accuracy as compared with the CAD data. In addition, the multiple overhang lengths were also successfully fabricated with a very significant of filament sagging observed.

scholarly journals On the application of grey Taguchi method for benchmarking the dimensional accuracy of the PLA fused filament fabrication process

2020 ◽  
Vol 2 (6) ◽  
Author(s):  
Kyriaki-Evangelia Aslani ◽  
Konstantinos Kitsakis ◽  
John D. Kechagias ◽  
Nikolaos M. Vaxevanidis ◽  
Dimitrios E. Manolakos
Keyword(s):  
Taguchi Method ◽  
Dimensional Accuracy ◽  
Fabrication Process ◽  
Fused Filament Fabrication ◽  
Grey Taguchi

Parametric optimization of fused deposition modelling process using Grey based Taguchi and TOPSIS methods for an automotive component

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sakthivel Murugan R. ◽  
Vinodh S.
Keyword(s):  
Taguchi Method ◽  
Process Parameters ◽  
Parametric Optimization ◽  
Multiple Objective ◽  
Fused Deposition Modelling ◽  
Automotive Component ◽  
Content Type ◽  
Fused Deposition ◽  
Relative Closeness ◽  
Ranking Order

Purpose This paper aims to optimize the process parameters of the fused deposition modelling (FDM) process using the Grey-based Taguchi method and the results to be verified based on a technique for order preference by similarity to ideal solution (TOPSIS) and analytical hierarchy process (AHP) calculation. Design/methodology/approach The optimization of process parameters is gaining a potential role to develop robust products. In this context, this paper presents the parametric optimization of the FDM process using Grey-based Taguchi, TOPSIS and AHP method. The effect of slice height (SH), part fill style (PFS) and build orientation (BO) are investigated with the response parameters machining time, surface roughness and hardness (HD). Multiple objective optimizations were performed with weights of w1 = 60%, w2 = 20% and w3 = 20%. The significance of the process parameters over response parameters is identified through analysis of variance (ANOVA). Comparisons are made in terms of rank order with respect to grey relation grade (GRG), relative closeness and AHP index values. Response table, percentage contributions of process parameters for both GRG and TOPSIS evaluation are done. Findings The optimum factor levels are identified using GRG via the Grey Taguchi method and TOPSIS via relative closeness values. The optimized factor levels are SH (0.013 in), PFS (solid) and BO (45°) using GRG and SH (0.013 in), PFS (sparse-low density) and BO (45°) using TOPSIS relative closeness value. SH has higher significance in both Grey relational analysis and TOPSIS which were analysed using ANOVA. Research limitations/implications In this research, the multiple objective optimizations were done on an automotive component using GRG, TOPSIS and AHP which showed a 27% similarity in their ranking order among the experiments. In the future, other advanced optimization techniques will be applied to further improve the similarity in ranking order. Practical implications The study presents the case of an automotive component, which illustrates practical relevance. Originality/value In several research studies, optimization was done on the standard test specimens but not on a real-time component. Here, the multiple objective optimizations were applied to a case automotive component using Grey-based Taguchi and verified with TOPSIS. Hence, an effort has been taken to find optimum process parameters on FDM, for achieving smooth, hardened automotive components with enhanced printing time. The component can be explored as a replacement for the existing product.

Investigations for partial denture casting by fused deposition modelling-assisted chemical vapour smoothing

2020 ◽  
Vol 40 (5) ◽  
pp. 745-754
Author(s):  
Gurpartap Singh ◽  
Rupinder Singh ◽  
S.S. Bal
Keyword(s):  
Ph Value ◽  
Low Cost ◽  
Chemical Vapour ◽  
Dimensional Accuracy ◽  
Acrylonitrile Butadiene Styrene ◽  
Fused Deposition Modelling ◽  
View Point ◽  
Content Type ◽  
Fused Deposition ◽  
Set Up

Purpose The purpose of this study is to investigate dimensional accuracy (Δd), surface roughness (Ra) and micro hardness (HV) of partial dentures (PD) prepared with synergic combination of fused deposition modelling (FDM) assisted chemical vapour smoothing (CVS) patterns and conventional dental casting (DC) from multi-factor optimization view point. Design/methodology/approach The master pattern for PD was prepared with acrylonitrile butadiene styrene (ABS) thermoplastic on FDM set-up (one of the low cost additive manufacturing process) followed by CVS process. The final PD as functional prototypes was casted with nickel–chromium-based (Ni-Cr) alloy by varying Ni% (Z). The other input parameters were powder to water ratio P/W (X) and pH value (Y) of water used. Findings The results of this study suggest that for controlling the Δd and Ra of the PD, most important factor is X, followed by Z. For hardness of PD, the most important factor is Z. But from overall optimization viewpoint, the best settings are X-100/12, Y-10 and Z-61% (in Ni-Cr alloy). Further, based upon X-bar chart (for HV), the FDM-assisted DC process used for preparation of PD is statistically controlled. Originality/value This study highlights that PD prepared with X-100/12, Y-10 and Z-61% gives overall better results from multi-factor optimization view point. Finally, X-bar chart has been plotted to understand the statistical nature of the synergic combination of FDM, CVS and DC.

scholarly journals PENENTUAN SETTING OPTIMAL MESIN 3D PRINTER BERBASIS FUSED DEPOSITION MODELING MENGGUNAKAN METODE TAGUCHI

2018 ◽  
Vol 4 (2) ◽  
Author(s):  
Anggit Prakasa ◽  
Setya Permana Sutisna ◽  
Anton Royanto Ahmad
Keyword(s):  
Experimental Design ◽  
Taguchi Method ◽  
Fused Deposition Modeling ◽  
Dimensional Accuracy ◽  
High Accuracy ◽  
3D Printer ◽  
Fused Deposition ◽  
3D Printers ◽  
Optimal Setting ◽  
Deposition Modeling

<p>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<br />setting of the dimensional accuracy 3D printers based fused deposition modeling. The method used is<br />the Taguchi method, the reason for using this method its efficiency, this is because the Orthogonal<br />Array matrix requires less number of experiments than the classical experimental design. Analysis of<br />Variance is also needed in this method to see the factors that significantly influence the response<br />variable. The results of this study indicate that the factors that significantly influence is printspeed by<br />contributing 53.08%, flowrate contributes 16.4%, and temperature heater block contributes 3.85% and<br />optimal setting is temperature heater block 190º, print speed 60mm/s and flowrate 6.28 mm3/s. (A1,<br />C3 dan D2).</p>

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