The Experts below are selected from a list of 54 Experts worldwide ranked by ideXlab platform
Siba Sankar Mahapatra - One of the best experts on this subject based on the ideXlab platform.
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bayesian regularization based levenberg marquardt neural model combined with bfoa for improving surface finish of fdm Processed Part
The International Journal of Advanced Manufacturing Technology, 2012Co-Authors: Siba Sankar Mahapatra, Anoop Kumar SoodAbstract:Fused deposition modeling has a complex Part building mechanism making it difficult to obtain reasonably good functional relationship between responses and process parameters. To solve this problem, present study proposes use of artificial neural network (ANN) model to determine the relationship between five input parameters such as layer thickness, orientation, raster angle, raster width, and air gap with three output responses viz., roughness in top, bottom, and side surface of the built Part. Bayesian regularization is adopted for selection of optimum network architecture because of its ability to fix number of network parameters irrespective of network size. ANN model is trained using Levenberg–Marquardt algorithm, and the resulting network has good generalization capability that eliminates the chance of over fitting. Finally, bacterial foraging optimization algorithm which attempts to model the individual and group behavior of Escherichia coli bacteria as a distributed optimization process is used to suggest theoretical combination of parameter settings to improve overall roughness of Part. This paper also investigates use of chaotic time series sequence known as logistic function and demonstrates its superiority in terms of convergence and solution quality.
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experimental investigation on wear of fdm Processed Part
Advanced Materials Research, 2012Co-Authors: Anoop Kumar Sood, R K Ohdar, Siba Sankar MahapatraAbstract:Fused deposition modelling (FDM) is one of the rapid prototyping (RP) processes that build Part of any geometry by sequential deposition of material on a layer by layer basis. Unlike other RP systems which involve an array of lasers, powders, resins, this process uses heated thermoplastic filaments which are extruded from the tip of nozzle in a prescribed manner. Present work focuses on extensive study to understand the effect of five important parameters such as layer thickness, Part build orientation, raster angle, raster width and air gap on the sliding wear of test specimen built through FDM. The study provides insight into complex dependency of wear on process parameters and proposes a statistically validated predictive equation. Microphotographs are used to explain the mechanism of wear. Finally, the predictive equation is used to find optimal parameter setting through bacteria foraging optimization algorithm (BFOA).
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improving dimensional accuracy of fused deposition modelling Processed Part using grey taguchi method
Materials & Design, 2009Co-Authors: Anoop Kumar Sood, R K Ohdar, Siba Sankar MahapatraAbstract:Abstract This paper presents experimental investigations on influence of important process parameters viz., layer thickness, Part orientation, raster angle, air gap and raster width along with their interactions on dimensional accuracy of Fused Deposition Modelling (FDM) Processed ABSP400 (acrylonitrile-butadine-styrene) Part. It is observed that shrinkage is dominant along length and width direction of built Part. But, positive deviation from the required value is observed in the thickness direction. Optimum parameters setting to minimize percentage change in length, width and thickness of standard test specimen have been found out using Taguchi’s parameter design. Experimental results indicate that optimal factor settings for each performance characteristic are different. Therefore, all the three responses are expressed in a single response called grey relational grade. Finally, grey Taguchi method is adopted to obtain optimum level of process parameters to minimize percentage change in length, width and thickness simultaneously. The FDM process is highly complex one and hardly any theoretical model exist for the prediction purpose. The process parameters influence the responses in a highly non-linear manner. Therefore, prediction of overall dimensional accuracy is made based on artificial neural network (ANN).
Dongdong Gu - One of the best experts on this subject based on the ideXlab platform.
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Selective laser melting of in-situ Al4SiC4 + SiC hybrid reinforced Al matrix composites: Influence of starting SiC Particle size
Surface and Coatings Technology, 2015Co-Authors: Fei Chang, Dongdong Gu, Donghua Dai, Pengpeng YuanAbstract:Selective laser melting (SLM) additive manufacturing of the SiC/AlSi10Mg composite powder systems with different starting SiC Particle sizes was performed to produce in-situ Al4SiC4+SiC hybrid reinforced Al matrix composites. The influence of starting SiC Particle size on the constitutional phases, microstructural features, and mechanical properties of the SLM-Processed composite Parts was studied. As the SiC Particle size decreased, the extent of in-situ reaction between aluminum melt and SiC Particles was enhanced, leading to the elevated formation of Al4SiC4 reinforcing phase. With the fine SiC Particles (D50=5μm) used, the residual SiC Particles with a reduced size of 3μm were dispersed homogeneously throughout the matrix, thereby enhancing the microstructural homogeneity of the Part. With the enhancement of in-situ reaction, the formation of plate-like and Particle-structured Al4SiC4 reinforcement was significantly accelerated, favoring the formation of (Al4SiC4+SiC)/Al hybrid reinforced composites after SLM. Using fine SiC Particles, the reinforcement/matrix wettability was improved, leading to a nearly full densification level of 97.2% theoretical density of SLM-Processed Part. The microhardness of 218.5HV0.1 showed at least 50% improvement upon SLM-Processed unreinforced AlSi10Mg. The fine SiC Particles also reduced the coefficient of friction (COF) by 19% and the wear rate by 66% compared to the SLM Part Processed with coarse SiC Particles.
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tailoring surface quality through mass and momentum transfer modeling using a volume of fluid method in selective laser melting of tic alsi10mg powder
International Journal of Machine Tools & Manufacture, 2015Co-Authors: Dongdong GuAbstract:Abstract A selective laser melting (SLM) physical model of coupled radiation transfer and thermal diffusion is proposed, which provides a local temperature field. A strong difference in thermal conductivity between the powder bed and dense material is taken into account. Both thermo-capillary force and recoil pressure induced by the material evaporation, which are the major driving forces for the melt flow, are incorporated in the formulation. The effect of the laser energy input per unit length (LEPUL) on the temperature distribution, melt pool dynamics, surface tension and resultant surface morphology has been investigated. It shows that the surface tension plays a crucial role in the formation of the terminally solidified surface morphology of the SLM-Processed Part. The higher surface tension of the lower temperature metal near the edge of the melt pool and the thermal-capillary force induced by the surface temperature gradient tend to pull the molten metal away from the center of the melt pool. For a relatively high LEPUL of 750 J/m, the molten material in the center of the melt pool has a tendency to flow towards the rear Part, resulting in the stack of molten material and the attendant formation of a poor surface quality. For an optimized processing condition, LEPUL=500 J/m, a complete spreading of the molten material driven by the surface tension is obtained, leading to the formation of a fine and flat melt pool surface. The surface quality and morphology are experimentally acquired, which are in a good agreement with the results predicted by simulation.
Anoop Kumar Sood - One of the best experts on this subject based on the ideXlab platform.
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bayesian regularization based levenberg marquardt neural model combined with bfoa for improving surface finish of fdm Processed Part
The International Journal of Advanced Manufacturing Technology, 2012Co-Authors: Siba Sankar Mahapatra, Anoop Kumar SoodAbstract:Fused deposition modeling has a complex Part building mechanism making it difficult to obtain reasonably good functional relationship between responses and process parameters. To solve this problem, present study proposes use of artificial neural network (ANN) model to determine the relationship between five input parameters such as layer thickness, orientation, raster angle, raster width, and air gap with three output responses viz., roughness in top, bottom, and side surface of the built Part. Bayesian regularization is adopted for selection of optimum network architecture because of its ability to fix number of network parameters irrespective of network size. ANN model is trained using Levenberg–Marquardt algorithm, and the resulting network has good generalization capability that eliminates the chance of over fitting. Finally, bacterial foraging optimization algorithm which attempts to model the individual and group behavior of Escherichia coli bacteria as a distributed optimization process is used to suggest theoretical combination of parameter settings to improve overall roughness of Part. This paper also investigates use of chaotic time series sequence known as logistic function and demonstrates its superiority in terms of convergence and solution quality.
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experimental investigation on wear of fdm Processed Part
Advanced Materials Research, 2012Co-Authors: Anoop Kumar Sood, R K Ohdar, Siba Sankar MahapatraAbstract:Fused deposition modelling (FDM) is one of the rapid prototyping (RP) processes that build Part of any geometry by sequential deposition of material on a layer by layer basis. Unlike other RP systems which involve an array of lasers, powders, resins, this process uses heated thermoplastic filaments which are extruded from the tip of nozzle in a prescribed manner. Present work focuses on extensive study to understand the effect of five important parameters such as layer thickness, Part build orientation, raster angle, raster width and air gap on the sliding wear of test specimen built through FDM. The study provides insight into complex dependency of wear on process parameters and proposes a statistically validated predictive equation. Microphotographs are used to explain the mechanism of wear. Finally, the predictive equation is used to find optimal parameter setting through bacteria foraging optimization algorithm (BFOA).
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improving dimensional accuracy of fused deposition modelling Processed Part using grey taguchi method
Materials & Design, 2009Co-Authors: Anoop Kumar Sood, R K Ohdar, Siba Sankar MahapatraAbstract:Abstract This paper presents experimental investigations on influence of important process parameters viz., layer thickness, Part orientation, raster angle, air gap and raster width along with their interactions on dimensional accuracy of Fused Deposition Modelling (FDM) Processed ABSP400 (acrylonitrile-butadine-styrene) Part. It is observed that shrinkage is dominant along length and width direction of built Part. But, positive deviation from the required value is observed in the thickness direction. Optimum parameters setting to minimize percentage change in length, width and thickness of standard test specimen have been found out using Taguchi’s parameter design. Experimental results indicate that optimal factor settings for each performance characteristic are different. Therefore, all the three responses are expressed in a single response called grey relational grade. Finally, grey Taguchi method is adopted to obtain optimum level of process parameters to minimize percentage change in length, width and thickness simultaneously. The FDM process is highly complex one and hardly any theoretical model exist for the prediction purpose. The process parameters influence the responses in a highly non-linear manner. Therefore, prediction of overall dimensional accuracy is made based on artificial neural network (ANN).
H. Sang - One of the best experts on this subject based on the ideXlab platform.
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Prediction of porosity in metal-based additive manufacturing using spatial Gaussian process models
Additive Manufacturing, 2016Co-Authors: Gustavo Tapia, Alaa Elwany, H. SangAbstract:Additive manufacturing (AM) is a set of emerging technologies that can produce physical objects with complex geometrical shapes directly from a digital model. With many unique capabilities, such as design freedom, it has recently gained increasing attention from researchers, practitioners, and public media. However, achieving the full potential of AM is hampered by many challenges, including the lack of predictive models that correlate processing parameters with the properties of the Processed Part. We develop a Gaussian process-based predictive model for the learning and prediction of the porosity in metallic Parts produced using selective laser melting (SLM – a laser-based AM process). More specifically, a spatial Gaussian process regression model is first developed to model Part porosity as a function of SLM process parameters. Next, a Bayesian inference framework is used to estimate the statistical model parameters, and the porosity of the Part at any given setting is predicted using the Kriging method. A case study is conducted to validate this predictive framework through predicting the porosity of 17-4 PH stainless steel manufacturing on a ProX 100 selective laser melting system.
R K Ohdar - One of the best experts on this subject based on the ideXlab platform.
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experimental investigation on wear of fdm Processed Part
Advanced Materials Research, 2012Co-Authors: Anoop Kumar Sood, R K Ohdar, Siba Sankar MahapatraAbstract:Fused deposition modelling (FDM) is one of the rapid prototyping (RP) processes that build Part of any geometry by sequential deposition of material on a layer by layer basis. Unlike other RP systems which involve an array of lasers, powders, resins, this process uses heated thermoplastic filaments which are extruded from the tip of nozzle in a prescribed manner. Present work focuses on extensive study to understand the effect of five important parameters such as layer thickness, Part build orientation, raster angle, raster width and air gap on the sliding wear of test specimen built through FDM. The study provides insight into complex dependency of wear on process parameters and proposes a statistically validated predictive equation. Microphotographs are used to explain the mechanism of wear. Finally, the predictive equation is used to find optimal parameter setting through bacteria foraging optimization algorithm (BFOA).
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improving dimensional accuracy of fused deposition modelling Processed Part using grey taguchi method
Materials & Design, 2009Co-Authors: Anoop Kumar Sood, R K Ohdar, Siba Sankar MahapatraAbstract:Abstract This paper presents experimental investigations on influence of important process parameters viz., layer thickness, Part orientation, raster angle, air gap and raster width along with their interactions on dimensional accuracy of Fused Deposition Modelling (FDM) Processed ABSP400 (acrylonitrile-butadine-styrene) Part. It is observed that shrinkage is dominant along length and width direction of built Part. But, positive deviation from the required value is observed in the thickness direction. Optimum parameters setting to minimize percentage change in length, width and thickness of standard test specimen have been found out using Taguchi’s parameter design. Experimental results indicate that optimal factor settings for each performance characteristic are different. Therefore, all the three responses are expressed in a single response called grey relational grade. Finally, grey Taguchi method is adopted to obtain optimum level of process parameters to minimize percentage change in length, width and thickness simultaneously. The FDM process is highly complex one and hardly any theoretical model exist for the prediction purpose. The process parameters influence the responses in a highly non-linear manner. Therefore, prediction of overall dimensional accuracy is made based on artificial neural network (ANN).