Optimization parameter effects on the quality surface finish of the three-dimensional printing (3D-printing) fused deposition modeling (FDM) using RSM

Radhwan, H.; Shayfull, Z.; Farizuan, M. R.; Effendi, M. S. M.; Irfan, A. R.

doi:10.1063/1.5118163

Cited by 40 publications

(8 citation statements)

References 4 publications

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“…speed results in large voids, a low surface finish and then to a high roughness value. Similar observations were made for FDM printing of polylactic acid (PLA) by Radhwan et al [48].…”

Section: Effect Of Nozzle Speedsupporting

confidence: 78%

Copper extrusion 3D printing using metal injection moulding feedstock: Analysis of process parameters for green density and surface roughness optimization

Singh

Missiaen²,

Bouvard³

et al. 2021

Additive Manufacturing

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In the present study, the extrusion-based 3D printing process was explored using metal injection moulding (MIM) copper feedstock to fabricate dense copper parts. The influence of process parameters of 3D printing, namely layer thickness, nozzle speed, extrusion multiplier and extrusion temperature on green density and surface roughness were studied. Based on the central composite design method, a set of experiments was chosen to study the individual and interaction effects of parameters. Analysis of variance was performed to identify the significant factors and statistical models were obtained by regression analysis. The green density was observed to increase with varying the layer thickness from 0.25 to 0.05 mm and nozzle speed from 100 to 20 mm/s. Similarly, surface roughness was improved by decreasing the layer thickness and the nozzle speed and by increasing the extrusion multiplier up to a specific value. The extrusion temperature gave the best results at 200 ℃ for both green density and surface roughness. The significant interactions between the parameters for both green density and surface roughness were also studied. A multi-objective optimization approach was used to maximize the green density and minimize the surface roughness within the range of the parameters. Micro-tomography scans were used to analyze the porosity and voids in samples printed with optimized and non-optimized parameters. Besides, sintering was performed on the optimized printed sample to fabricate a dense copper part and analyze linear shrinkage during sintering. Sintered copper parts with high density and low surface roughness were obtained with the optimized printing process parameters.

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“…speed results in large voids, a low surface finish and then to a high roughness value. Similar observations were made for FDM printing of polylactic acid (PLA) by Radhwan et al [48].…”

Section: Effect Of Nozzle Speedsupporting

confidence: 78%

Copper extrusion 3D printing using metal injection moulding feedstock: Analysis of process parameters for green density and surface roughness optimization

Singh

Missiaen²,

Bouvard³

et al. 2021

Additive Manufacturing

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show abstract

“…Mainly, it is interesting to note that as the printing speed increases, the quality of the surface improves. This result is the opposite of that presented by Radhwan et al 35 and Pramanik et al 36 The authors found that the lower the printing speed, the better the surface roughness. Thus, this counterintuitive result may be further researched.…”

Section: Influence Of the Selected Factorscontrasting

confidence: 61%

Examining the surface quality of inner and outer surfaces in material extrusion printed samples

Río

Carou

Pereira

2023

Proceedings of the Institution of Mechanical Engineers, Part L:

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This article presents an experimental study of the material extrusion process to examine the impact of less-analyzed factors on surface roughness, not including the commonly known layer height. The study also gives attention to the inner surfaces of printed samples. The design of experiments used a fractional factorial design with five two-level factors: bed temperature, fan percentage, printing speed, printing temperature, and wall thickness. Thus, the purpose of the design is screening. A two-dimensional contact profilometer was used to measure the surface roughness. Generally, the inner faces of the printed samples showed lower surface roughness. In addition, four analysis of variance showed that wall thickness (for Ra and Rz) and printing speed (for Ra) are significant for the outer and inner surfaces, respectively. As the wall thickness increased, the surface roughness decreased. Meanwhile, the lower the printing speed, the worse the surface quality on the inner faces.

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“…The normal plot of residuals graph is one of the techniques used to determine whether the data is normally distributed or not. Data will be normally distributed if it is close to a straight line [25].…”

Section: Figure 1 Normal Plot Of Residuals Graphicmentioning

confidence: 99%

Optimization of Enzymatic Hydrolysis of Mackerel (Rastrelliger sp.) Muscle Protein Hydrolysate Using Response Surface Methodology

Riyadi,

Romadhon,

Bramantyo

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Mackerel (Rastrelliger sp.) is a widely distributed epipelagic species in South East Asia. Mackerel has a high amount nutrient such as protein (20.83 %) and fat (1.03 %). The high amount of protein and low amount of fat will allow it to be used as a material to produce a good protein hydrolysate. The aim of this study is to determine the optimal enzymatic hydrolysis conditions (time, temperature, and pH) using Response Surface Methodology (RSM). Mackerel Protein Hydrolysate (MPH) was prepared using commercial Flavourzyme. Optimization of MPH was performed by employing Box Behnken Design method of RSM. SN-TCA method was used to calculate the degree of hydrolysis (DH) which is the key parameter in hydrolysis reaction. Optimum hydrolysis conditions were obtained at pH 7, temperature 55°C and 60 min of process. Under these conditions the DH obtained was 17.7293 % with 4% enzyme to substrate ratio. The suggested model for the hydrolysis process is quadratic with the desirability factor of 1. The MPH was further assessed for its amino acid composition using High Performance Liquid Chromatography (HPLC). The hydrolysis process increases the amino acid amounts namely L-Glutamic Acid (19.77%), L-Valin (14.20%), L-Aspartic Acid (11.42%), Glycine (11.04%), L-Alanin (14.20%), L-Prolin (16.80%), and L-Histidin (27.06%). The study suggested that mackerel muscle can be considered to be utilized as fish protein hydrolysis materials.

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Optimization parameter effects on the quality surface finish of the three-dimensional printing (3D-printing) fused deposition modeling (FDM) using RSM

Cited by 40 publications

References 4 publications

Copper extrusion 3D printing using metal injection moulding feedstock: Analysis of process parameters for green density and surface roughness optimization

Copper extrusion 3D printing using metal injection moulding feedstock: Analysis of process parameters for green density and surface roughness optimization

Examining the surface quality of inner and outer surfaces in material extrusion printed samples

Optimization of Enzymatic Hydrolysis of Mackerel (Rastrelliger sp.) Muscle Protein Hydrolysate Using Response Surface Methodology

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