Mathematical modeling and parametric optimization of surface roughness for evaluating the effects of fused deposition modeling process parameters on ABS material

— Fused deposition modeling (FDM) technology is production devices that use plastic material in the semi-molten state to harvest the products directly from the CAD model. This study describes the development of mathematical models to predict the effects of significant process parameters of the FDM on the surface roughness of ABS material. Experiments were planned as per Taguchi orthogonal array. Experiments were conducted under different printing input parameters of layer thickness, orientation angle, and infill angle. Response surface methods (RSM) have been employed to develop a predictive mathematical model in terms of controllable input parameters. Analysis of Variance (ANOVA), main effect and interaction plot, 3D surface, and contour plot were used to investigate the influence of various printing parameters on surface roughness. Finally, Taguchi methodology and RSM approaches have been applied successfully for the optimization of surface roughness (Ra) in FDM printing parts. It was observed that the models can adequately describe the responses within the ranges considered as the maximum error percent

[1]  Nikolaos M. Vaxevanidis,et al.  Optimization of fused deposition modeling process using a virus-evolutionary genetic algorithm , 2021, Comput. Ind..

[2]  S. Masood,et al.  Modeling, analysis, and optimization of dimensional accuracy of FDM-fabricated parts using definitive screening design and deep learning feedforward artificial neural network , 2021, Advances in Manufacturing.

[3]  S. Vinodh,et al.  Parametric optimization of fused deposition modelling process using Grey based Taguchi and TOPSIS methods for an automotive component , 2020 .

[4]  Nitin Mittal,et al.  Parametric optimization of fused deposition modeling using learning enthusiasm enabled teaching learning based algorithm , 2020, SN Applied Sciences.

[5]  Vimal Kumar Pathak,et al.  Modeling and optimization of fused deposition modeling (FDM) process through printing PLA implants using adaptive neuro-fuzzy inference system (ANFIS) model and whale optimization algorithm , 2020, Journal of the Brazilian Society of Mechanical Sciences and Engineering.

[6]  A. Elmushyakhi.  Freeze-thaw stabilization of fused deposition modeling 3D-printed SABIC structures , 2020 .

[7]  C. Abeykoon,et al.  Optimization of fused deposition modeling parameters for improved PLA and ABS 3D printed structures , 2020, International Journal of Lightweight Materials and Manufacture.

[8]  A. Haleem,et al.  Optimising Parameters of Fused Filament Fabrication Process to Achieve Optimum Tensile Strength Using Artificial Neural Network , 2020 .

[9]  K. DePalma,et al.  Assessment of 3D printing using fused deposition modeling and selective laser sintering for a circular economy , 2020 .

[10]  D. Chhabra,et al.  Exercising hybrid statistical tools GA-RSM, GA-ANN and GA-ANFIS to optimize FDM process parameters for tensile strength improvement , 2020 .

[11]  N. Naveed.  Investigate the effects of process parameters on material properties and microstructural changes of 3D-printed specimens using fused deposition modelling (FDM) , 2020 .

[12]  P. Eswaran,et al.  State of art on fusion deposition modeling machines process parameter optimization on composite materials , 2020 .

[13]  Bin Zou,et al.  Modeling of surface roughness based on heat transfer considering diffusion among deposition filaments for FDM 3D printing heat-resistant resin , 2019, Applied Thermal Engineering.

[14]  Chuanzhen Huang,et al.  Effects of printing parameters of fused deposition modeling on mechanical properties, surface quality, and microstructure of PEEK , 2019, Journal of Materials Processing Technology.

[15]  Deepak Chhabra,et al.  Optimization of FDM 3D printing process parameters for multi-material using artificial neural network , 2020 .

[16]  Vishal Wankhede,et al.  Experimental investigation of FDM process parameters using Taguchi analysis , 2020 .

[17]  M. Ajay Kumar,et al.  Mathematical approach for the geometrical deformation of fused deposition modelling build parts , 2020 .

[18]  N. A. Fountas,et al.  Experimental investigation and statistical modelling for assessing the tensile properties of FDM fabricated parts , 2020 .