The Surface Quality Improvement Methods for FDM Printed Parts: A Review

Hashmi, Abdul Wahab; Mali, Harlal Singh; Meena, Anoj

doi:10.1007/978-3-030-68024-4_9

Cited by 39 publications

(19 citation statements)

References 50 publications

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“…The lower heat content restricted the localized remelting of the previously deposited material, thereby limiting the thermal stress accumulation at the interface [ 18 ]. Moreover, a precise raster width inhibited the radial expansion of the polymer melt exiting the nozzle [ 54 ] and hence led to reduced dimensional errors.…”

Section: Resultsmentioning

confidence: 99%

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Parametric Modeling and Optimization of Dimensional Error and Surface Roughness of Fused Deposition Modeling Printed Polyethylene Terephthalate Glycol Parts

et al. 2023

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Polyethylene Terephthalate Glycol (PETG) is a fused deposition modeling (FDM)-compatible material gaining popularity due to its high strength and durability, lower shrinkage with less warping, better recyclability and safer and easier printing. FDM, however, suffers from the drawbacks of limited dimensional accuracy and a poor surface finish. This study describes a first effort to identify printing settings that will overcome these limitations for PETG printing. It aims to understand the influence of print speed, layer thickness, extrusion temperature and raster width on the dimensional errors and surface finish of FDM-printed PETG parts and perform multi-objective parametric optimization to identify optimal settings for high-quality printing. The experiments were performed as per the central composite rotatable design and statistical models were developed using response surface methodology (RSM), whose adequacy was verified using the analysis of variance (ANOVA) technique. Adaptive neuro fuzzy inference system (ANFIS) models were also developed for response prediction, having a root mean square error of not more than 0.83. For the minimization of surface roughness and dimensional errors, multi-objective optimization using a hybrid RSM and NSGA-II algorithm suggested the following optimal input parameters: print speed = 50 mm/s, layer thickness = 0.1 mm, extrusion temperature = 230 °C and raster width = 0.6 mm. After experimental validation, the predictive performance of the ANFIS (mean percentage error of 9.33%) was found to be superior to that of RSM (mean percentage error of 12.31%).

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Section: Resultsmentioning

confidence: 99%

“…Raster Width is the width of the molten filament which is deposited on the FDM printer bed [ 54 ]. It depends on the diameter of the extruder nozzle tip.…”

Section: Methodsmentioning

confidence: 99%

Parametric Modeling and Optimization of Dimensional Error and Surface Roughness of Fused Deposition Modeling Printed Polyethylene Terephthalate Glycol Parts

et al. 2023

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“…Hashmi et al have reviewed the various surface finishing techniques, i.e., preprocessing and postprocessing techniques, to improve the surface quality [287][288][289]. They have suggested the measurement of surface roughness using machine vision technique for in-process measurement and optimization of surface roughness of the AM parts [290][291][292].…”

Section: Evaluation Of Surface Finishmentioning

confidence: 99%

Surface Characteristics Measurement Using Computer Vision: A Review

Hashmi¹,

Mali²,

Meena³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Computer vision provides image-based solutions to inspect and investigate the quality of the surface to be measured. For any components to execute their intended functions and operations, surface quality is considered equally significant to dimensional quality. Surface Roughness (Ra) is a widely recognized measure to evaluate and investigate the surface quality of machined parts. Various conventional methods and approaches to measure the surface roughness are not feasible and appropriate in industries claiming 100% inspection and examination because of the time and efforts involved in performing the measurement. However, Machine vision has emerged as the innovative approach to executing the surface roughness measurement. It can provide economic, automated, quick, and reliable solutions. This paper discusses the characterization of the surface texture of surfaces of traditional or non-traditional manufactured parts through a computer/machine vision approach and assessment of the surface characteristics, i.e., surface roughness, waviness, flatness, surface texture, etc., machine vision parameters. This paper will also discuss multiple machine vision techniques for different manufacturing processes to perform the surface characterization measurement.

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“…The Taguchi method is an approach for system optimization that researchers use in various applications, including Computerized Numeric Control (CNC) operations [ 39 , 40 ], laser cutting [ 41 , 42 ], the EDM process [ 43 ], investment casting [ 44 ], and drilling [ 45 ]. The authors [ 46 , 47 ] proposed the Taguchi technique for process optimization—a quick, systematic way to improve operations quality, performance, and cost. Authors [ 48 , 49 ] used the Taguchi method to investigate the process parameters optimization of a FFF 3DP material.…”

Section: Introductionmentioning

confidence: 99%

Parametric Effects of Fused Filament Fabrication Approach on Surface Roughness of Acrylonitrile Butadiene Styrene and Nylon-6 Polymer

et al. 2022

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This research objective is to optimize the surface roughness of Nylon-6 (PA-6) and Acrylonitrile Butadiene Styrene (ABS) by analyzing the parametric effects of the Fused Filament Fabrication (FFF) technique of Three-Dimensional Printing (3DP) parameters. This article discusses how to optimize the surface roughness using Taguchi analysis by the S/N ratio, ANOVA, and modeling methods. The effects of ABS parameters (initial line thickness, raster width, bed temperature, build pattern, extrusion temperature, print speed, and layer thickness) and PA-6 parameters (layer thickness, print speed, extrusion temperature, and build pattern) were investigated with the average surface roughness (Ra) and root-mean-square average surface roughness (Rq) as response parameters. Validation tests revealed that Ra and Rq decreased significantly. After the optimization, the Ra-ABS and Rq-PA-6 for the fabricated optimized values were 1.75 µm and 21.37 µm, respectively. Taguchi optimization of Ra-ABS, Rq-ABS, Ra-PA-6, and Rq-PA-6 was performed to make one step forward to use them in further research and prototypes.

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The Surface Quality Improvement Methods for FDM Printed Parts: A Review

Cited by 39 publications

References 50 publications

Parametric Modeling and Optimization of Dimensional Error and Surface Roughness of Fused Deposition Modeling Printed Polyethylene Terephthalate Glycol Parts

Parametric Modeling and Optimization of Dimensional Error and Surface Roughness of Fused Deposition Modeling Printed Polyethylene Terephthalate Glycol Parts

Surface Characteristics Measurement Using Computer Vision: A Review

Parametric Effects of Fused Filament Fabrication Approach on Surface Roughness of Acrylonitrile Butadiene Styrene and Nylon-6 Polymer

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