An assessment of the dimensional accuracy and geometry-resolution limit of desktop stereolithography using response surface methodology

2019 ◽  
Vol 25 (7) ◽  
pp. 1169-1186 ◽  
Author(s):  
Ivana Cotabarren ◽  
Camila Andrea Palla ◽  
Caroline Taylor McCue ◽  
Anastasios John Hart
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Layer Thickness ◽  
Orientation Angle ◽  
Dimensional Accuracy ◽  
Process Performance ◽  
Feature Location ◽  
Resolution Limit ◽  
Content Type ◽  
Dimensional Deviation

Purpose This paper aims to apply a robust methodology to establish relationships between user-configurable process parameters of commercial desktop stereolithography (SLA) printers and dimensional accuracy of a custom-designed test artifact. Design/methodology/approach A detailed response surface methodology study, Box–Behnken incomplete factorial design of four factors with three levels, was carried out to evaluate process performance of desktop SLA printers. The selected factors were as follows: printing orientation angle in x-direction, printing orientation angle in y-direction, position on build platform in spatial x-coordinate, position on build tray in spatial y-coordinate and layer thickness. The proposed artifact was designed to include 12 feature groups including thin walls, holes, bosses, bridges and overhangs. Two responses were associated with the features: the dimensional deviation according to the designed value and the minimum feature size. Findings Layer thickness was the most significant factor in 70% of the analyzed responses. For example, measurement deviation was reduced about 90% when cylindrical holes were printed with the lowest layer thickness. Further, in many cases, dimensional deviation was minimized for features at the center of the platform, where the beam cures the resin in a straight line. However, at distant positions, accuracy could be improved by compensating for beam deviation by changing the object orientation angle. Originality/value The findings of this study can serve, both generally and specifically, for SLA designers and engineers who wish to optimize printing process variables and feature location to achieve high-dimensional accuracy and further understand the many coupled considerations among part design, build configuration and process performance.

scholarly journals Multi-Response Optimization of Process Parameter in Fused Deposition Modelling by Response Surface Methodology

2019 ◽  
Vol 8 (3) ◽  
pp. 327-338 ◽  
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Ambient Temperature ◽  
Response Surface ◽  
Layer Thickness ◽  
Dimensional Accuracy ◽  
Fused Deposition Modelling ◽  
Cross Sectional ◽  
Factors Affecting ◽  
Fused Deposition

This paper reported on the effect of ambient temperature, layer thickness, and part angle on the surface roughness and dimensional accuracy. The response surface methodology (RSM) was employed by using historical data in the experiment to determine the significant factors and their interactions on the fused deposition modelling (FDM) performance. Three controllable variables namely ambient temperature (30 °C, 45 °C, 60 °C), layer thickness (0.178 mm, 0.267 mm, 0.356 mm) and part angle (22.5°, 45°, 67.5°) have been studied. A total of 29 numbers of experiments had been conducted, including two replications at the center point. The results showed that all the parameter variables have significant effects on the part surface roughness and dimensional accuracy. Layer thickness is the most dominant factors affecting surface roughness. Meanwhile, the ambient temperature was the most dominant in determining part dimensional accuracy. The responses of various factors had been illustrated in the cross-sectional sample analysis. The optimum parameter required for minimum surface roughness and dimensional accuracy was at ambient temperature 30 °C, layer thickness 0.18 mm and part angle 67.38°. The optimization has produced maximum productivity with RaH 3.21 µm, RaV 11.78 µm, and RaS 12.79 µm. Meanwhile, dimensional accuracy height eror 3.21%, width error 3.70% and angle 0.38°

Investigation of effect of process variable on dimensional accuracy of FDM component using response surface methodology

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nagendra Kumar Maurya ◽  
Manish Maurya ◽  
Shashi Prakash Dwivedi ◽  
Ashish Kumar Srivastava ◽  
Ambuj Saxena ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Layer Thickness ◽  
Process Parameters ◽  
Dimensional Accuracy ◽  
Percentage Error ◽  
Content Type ◽  
Average Percentage ◽  
Raster Angle ◽  
Radial Dimension ◽  
Average Percentage Error

Purpose Nowadays, rapid prototyping is emerging as end use product in low volume. The accuracy of the fabricated components depends on various process parameters. Process parameters used in this investigation are layer thickness (150, 200 and 250 µm), infill pattern (linear, hexagonal and star fill), raster angle (0°, 45° and 90°) and infill density (40, 60 and 80%). Linear and radial dimension of knuckle joint are selected for the response factor. Design/methodology/approach The experiments are design by using response surface methodology (RSM). Four design variables at three levels are used to examine their influence on percentage error in linear dimension and radial dimension of the component. A prototype Knuckle joint is selected as component. Minitab-14 software is used for the design of experiments. Findings Experimental measure data is analyzed by using “smaller is better” quality characteristics. A regression model for the forecasting of percentage error in linear and radial dimension is developed. The developed model is within precision range. The optimum level of process for linear and radial dimensions are obtained: layer thickness of 150 µm, Infill pattern of linear, Raster angle of 90° and infill density of 40%. Research limitations/implications It proves that both the mathematical model is significant and can be able to approximate the desired output value close to the accurate dimensions. While comparing the calculated F-values for both linear and radial dimension with the standard table (F-table, 0.05), it is found that at the given set of degree of freedom the standard F-values (6.61) is lower for that regression, linear, square and interaction source of the predicted model, for which p-values have already less than 0.05. It is desirable for significant process parameters. Practical implications The dimensional accuracy with respect to average percentage error of FDM produced knuckle joint is successfully examined. The effect of process parameters, namely, layer thickness, infill pattern, raster angle and infill density on average percentage error was investigated by RSM and analysis of variance table. Social implications The novelty of this work lies in the fact that only few studies are available in archival literature related to influence of these process parameters on percentage error in linear and radial dimension for Polycarbonate (PC) material. Originality/value The novelty of this work lies in the fact only few studies are available in archival literature related to influence of these process parameters on percentage error in linear and radial dimension for Polycarbonate (PC) material.

Modeling of the machining parameters in turning of Al-5052/TiC/SiC composites: a statistical modeling approach using grey relational analysis (GRA) and response surface methodology (RSM)

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Venkateshwar Reddy Pathapalli ◽  
Meenakshi Reddy Reddigari ◽  
Eswara Kumar Anna ◽  
P. Srinivasa Rao ◽  
D V. Ramana Reddy
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Metal Matrix ◽  
Machining Parameters ◽  
Matrix Composites ◽  
Content Type ◽  
Relational Analysis ◽  
Desirability Approach ◽  
Grey Relational ◽  
Grey Taguchi

PurposeMetal matrix composites (MMC) has been a section which gives an overview of composite materials and owing to those exceptional physical and mechanical properties, particulate-reinforced aluminum MMCs have gained increasing interest in particular engineering applications. Owing to the toughness and abrasive quality of reinforcement components such as silicon carbide (SiC) and titanium carbide (TiC), such materials are categorized as difficult materials for machining. The work aims to develop the model for evaluating the machinability of the materials via the response surface technique by machining three distinct types of hybrid MMCs.Design/methodology/approachThe combined effects of three machining parameters, namely “cutting speed” (s), “feed rate” (f) and “depth of cut” (d), together with three separate composite materials, were evaluated with the help of three performance characteristics, i.e. material removal rate (MRR), cutting force (CF) and surface roughness (SR). Response surface methodology and analysis of variance (ANOVA) both were initially used for analyzing the machining parameters results.FindingsThe contours were developed to observe the combined process parameters along with their correlations. The process variables were concurrently configured using grey relational analysis (GRA) and the composite desirability methodology. Both the GRA and composite desirability approach obtained similar results.Practical implicationsThe results obtained in the present paper will be helpful for decision-makers in manufacturing industries, who work on metal cutting area especially composites, to select the suitable solution by implementing the Grey Taguchi and modeling techniques.Originality/valueThe originality of this research is to identify the suitability of process parameters combination based on the obtained research results. The optimization of machining parameters in turning of hybrid metal matrix composites is carried out with two different methods such as Grey Taguchi and composite desirability approach.

Improving the electromagnetic compatibility of electronic products by using response surface methodology and artificial neural network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ching-Hsiang Chen ◽  
Chien-Yi Huang ◽  
Yan-Ci Huang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Electromagnetic Compatibility ◽  
Heat Dissipation ◽  
Optimal Parameter ◽  
Content Type ◽  
Early Stages ◽  
Artificial Neural

Purpose The purpose of this study is to use the Taguchi Method for parametric design in the early stages of product development. electromagnetic compatibility (EMC) issues can be considered in the early stages of product design to reduce counter-measure components, product cost and labor consumption increases due to a number of design changes in the R&D cycle and to accelerate the R&D process. Design/methodology/approach The three EMC characteristics, including radiated emission, conducted emission and fast transient impulse immunity of power, are considered response values; control factors are determined with respect to the relevant parameters for printed circuit board and mechanical design of the product and peripheral devices used in conjunction with the product are considered as noise factors. The optimal parameter set is determined by using the principal component gray relational analysis in conjunction with both response surface methodology and artificial neural network. Findings Market specifications and cost of components are considered to propose an optimal parameter design set with the number of grounded screw holes being 14, the size of the shell heat dissipation holes being 3 mm and the arrangement angle of shell heat dissipation holes being 45 degrees, to dispose of 390 O filters on the noise source. Originality/value The optimal parameter set can improve EMC effectively to accommodate the design specifications required by customers and pass test regulations.

Combination of importance-performance analysis and response surface methodology for enhancing satisfaction

2016 ◽  
Vol 33 (6) ◽  
pp. 792-802 ◽  
Author(s):  
Ro Jin Pak
Keyword(s):  
Response Surface Methodology ◽  
Performance Analysis ◽  
Response Surface ◽  
Online Courses ◽  
Current Status ◽  
Entrance Examination ◽  
College Entrance Examination ◽  
College Entrance ◽  
Statistical Tool ◽  
Content Type

Purpose – In order to know how to enhance the satisfaction of online courses in preparing for the college entrance examination in Korea, the purpose of this paper is to combine both the importance-performance analysis (IPA) and the response surface methodology (RSM). Design/methodology/approach – IPA is a simple but powerful tool for understanding the current status of factors or attributes in management problems. However, it lacks to provide the proper indication of what to do next on those factors or attributes to optimize a goal. RSM is a statistical tool helping us to set up a direction of factors or attributes in optimizing an output. The author attempts to combine both IPA and RSM in order to discover the next step after IPA for optimizing the goal. As an example, the author considers how to enhance the satisfaction of online courses in preparing for the college entrance examination in Korea. Findings – The combination of IPA and RSM enables us to find a way to attain a goal, for example, satisfying customers in a concrete and creative way. Research limitations/implications – Sample size is enough for research purpose but is a bit small for general purpose. Practical implications – This research tries to answer what to do next after IPA. Social implications – This research provides a predictive guide to satisfy customers. Originality/value – As far as the author knows, combining both IPA and RSM has not been made so far. It is a fusion of managerial and engineering techniques.

scholarly journals Process Parameter Optimization in Fused Deposition Modeling (FDM) Using Response Surface Methodology (RSM)

2020 ◽  
Author(s):  
Muhammad Salman Mustafa ◽  
Muhammad Qasim Zafar ◽  
Muhammad Arslan Muneer ◽  
Muhammad Arif ◽  
Farrukh Arsalan Siddiqui ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Response Surface Methodology ◽  
Impact Strength ◽  
Response Surface ◽  
Layer Thickness ◽  
Fused Deposition Modeling ◽  
Experimental Results ◽  
Fused Deposition ◽  
Deposition Modeling ◽  
Printing Parameters

Abstract Fused Deposition Modeling (FDM) is a widely adopted additive manufacturing process to produce complex 3D structures and it is typically used in the fabrication of biodegradable materials e.g. PLA/PHA for biomedical applications. However, FDM as a fabrication process for such material needs to be optimized to enhance mechanical properties. In this study, dogbone and notched samples are printed with the FDM process to determine optimum values of printing parameters for superior mechanical properties. The effect of layer thickness, infill density, and print bed temperature on mechanical properties is investigated by applying response surface methodology (RSM). Optimum printing parameters are identified for tensile and impact strength and an empirical relation has been formulated with response surface methodology (RSM). Furthermore, the analysis of variance (ANOVA) was performed on the experimental results to determine the influence of the process parameters and their interactions. ANOVA results demonstrate that 44.7% infill density, 0.44 mm layer thickness, and 20C° printing temperatures are the optimum values of printing parameters owing to improved tensile and impact strength respectively. The experimental results were found in strong agreement with the predicted theoretical results.

Optimization of aqueous extraction of orevactaene and flavanoid pigments produced by Epicoccum nigrum

2019 ◽  
Vol 48 (4) ◽  
pp. 301-308 ◽  
Author(s):  
Sawinder Kaur ◽  
Paramjit S. Panesar ◽  
Sushma Gurumayum ◽  
Prasad Rasane ◽  
Vikas Kumar
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Maximum Yield ◽  
Polynomial Equation ◽  
Extraction Process ◽  
Aqueous Extraction ◽  
Extraction Temperature ◽  
Content Type ◽  
Epicoccum Nigrum ◽  
Broken Rice

Purpose The extraction of bioactive compounds such as pigments from natural sources, using different solvents, is a vital downstream process. The present study aims to investigate the effect of different variables, namely, extraction temperature, mass of fermented rice and time on the extraction process of orevactaene and flavanoid pigment from Epicoccum nigrum fermented broken rice. Design/methodology/approach Central composite rotatable design under response surface methodology was used for deducing optimized conditions. The pigments were extracted under conditions of extraction temperature (40-70°C), mass of fermented rice (0.5-1.5 g) and time (30-90 min), using water as the extraction media. The experimental data obtained were studied by analysis of variance. Data were fitted to a second-order polynomial equation using multiple regression analysis. Findings The optimum conditions generated by the software for aqueous extraction process, i.e. extraction temperature of 55.7°C, 0.79 g of fermented matter and extraction time of 56.6 min, resulted in a pigment yield of 52.7AU/g orevactaene and 77.2 AU/g flavanoid. Research limitations/implications The developed polynomial empirical model for the optimal recovery of the orevactaene and flavanoid pigments could be used for further studies in prediction of yield under specified variable conditions. Practical implications The response surface methodology helped in optimizng the conditions for the eco-friendly low-cost aqueous extarction process for orevactaene and flavanoid pigments, produced by Epicoccum nigrum during solid state fermentation of broken rice. This optimization can provide the basis for scaling up for industrial extraction process. Originality/value This paper focuses on optimizing the extraction conditions to get the maximum yield of orevactaene and flavanoid pigments, using water as the extracting media. No literature is available on the optimization of the extraction process of Epicoccum nigrum pigments, to the best of the authors’ knowledge.

scholarly journals Optimization of Soybean Meal Fermentation for Aqua-Feed with Bacillus subtilis natto Using the Response Surface Methodology

Fermentation ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. 306
Author(s):  
Yukun Zhang ◽  
Manabu Ishikawa ◽  
Shunsuke Koshio ◽  
Saichiro Yokoyama ◽  
Serge Dossou ◽  
...  
Keyword(s):  
Amino Acids ◽  
Response Surface Methodology ◽  
Bacillus Subtilis ◽  
Response Surface ◽  
Layer Thickness ◽  
Soybean Meal ◽  
Essential Amino Acids ◽  
Total Phenolic ◽  
Bacillus Subtilis Natto ◽  
Substrate Ratio

This study aimed to improve the nutritional value of soybean meal (SBM) by solid-state fermentation (SSF) using Bacillus subtilis natto (B. s. natto) to overcome the limitations of SBM usage in aquafeed. The response surface methodology (RSM) was employed to explore the relationships of fermentation conditions, such as temperature, time, water-substrate ratio, and layer thickness, on the degree of protein hydrolysis (DH) and the crude protein (CP) content. The optimum conditions for achieving the higher DH (15.96%) and CP (55.76%) were 43.82 °C, 62.32 h, 1.08 of water-substrate ratio, and a layer thickness of 2.02 cm. CP and DH in the fermented soybean meal (FSM) increased by 9.8% and 177.1%, respectively, and crude fiber decreased by 14.1% compared to SBM. The protein dispersibility index (PDI) decreased by 29.8%, while KOH protein solubility (KPS) was significantly increased by 17.4%. Flavonoids and total phenolic acid content in FSM were increased by 231.0% and 309.4%, respectively. Neutral protease activity (NPA) also reached a high level (1723.6 U g−1). Total essential amino acids (EAA) in FSM increased by 12.2%, higher than the 10.8% increase of total non-essential amino acids (NEAA), while the total free amino acids content was 12.76 times higher than that of SBM. Major anti-nutritional factors in SBM were significantly reduced during the process, and almost all SBM protein macromolecules were decomposed. Together with the cost-effectiveness of SSF, B. s. natto-fermented SBM products have great potential to improve the plant composition and replace high-cost ingredients in aquafeed, contributing to food security and environmental sustainability.

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