Optimization of FDM 3D printing process parameters for multi-material using artificial neural network

The properties of functional parts printed by additive manufacturing are highly dependent on various process parameters of the machine. The process parameters can be optimized by hybrid statistical tools to enhance the objective function. The present study investigates the tensile strength of Dogbone American Society for Testing and Materials (ASTM)-D638-V standardized parts fabricated by FDM 3D printer, using poly lactic acid (PLA) plus material. The test specimens were fabricated by varying three parameters: infill density (20–100%), speed (50–150 mm/s) and temperature (190–210 °C). For the parametric combination, response surface methodology (RSM) based central composite design (CCD) matrix was developed using second order polynomial fitting model. The maximum tensile strength of testing specimens on UNITEK-94100 universal testing machine (UTM) was recorded as 45.27 MPa. Further, hybrid optimization techniques like genetic algorithm-artificial neural network (GA-ANN), genetic algorithm-response surface methodology (GA-RSM) and genetic algorithm-adaptive neuro fuzzy interface system (GA-ANFIS) are deployed to optimize these process parameters. Among these tools, the maximum accuracy of 99.89% obtained with GA-ANN which results in optimized parameters as infill density 100%, temperature 210 °C, and speed 124.778 mm/s to achieve the maximum tensile strength of 47.0212 MPa. The results of this examination will facilitate the added substance producing units to choose the optimize factor value of input factors for FDM parts fabrication with improved mechanical properties. The hybrid developed models could be proposed for precise prediction and optimization of other process parameters and results for any industrial application problems.

Exercising hybrid statistical tools GA-RSM, GA-ANN and GA-ANFIS to optimize FDM process parameters for tensile strength improvement

Additive manufacturing (AM) technologies such as fused deposition modeling (FDM) have been widely used in various fields of today’s manufacturing industries such as transportation, aerospace, and medical because of its ability to produce parts of complex designs with less manufacturing time and cost. However, a proper selection of input process parameters is a vital aspect in order to obtain the best quality of the printed part. This paper presents several approaches, namely response surface methodology, particle swarm optimization, and symbiotic organism search, to find the optimal parameter settings for better surface quality, i.e., surface roughness of the FDM printed part. Layer height, print speed, print temperature, and outer shell speed were considered as the input parameters and surface roughness as the output response. The experimental design was carried out using response surface methodology (RSM) method. Then, the relationship between the input parameters and the surface roughness was established using regression model. Once the accuracy of the model had been validated, the model was then coupled with particle swarm optimization (PSO) and symbiotic organism search (SOS) to optimize the input parameters that would lead to minimum surface roughness. Experimental results showed that the surface roughness obtained using PSO and SOS have improved about 8.5% and 8.8%, respectively, compared with the conventional method, i.e., RSM. A good agreement between the predicted surface roughness and the experimental values was also observed.

Optimization of surface roughness in FDM 3D printer using response surface methodology, particle swarm optimization, and symbiotic organism search algorithms

Modeling and analysis of significant process parameters of FDM 3D printer using ANFIS

3D printable biomaterials for orthopedic implants: Solution for sustainable and circular economy

In this study, significant process parameters (layer thickness, build orientation, infill density and number of contours) are optimized for enhancing the magnitude/dimensional preciseness of fused deposition modeling (FDM) devise units. Hybrid statistical tools such as response surface methodology–genetic algorithm (RSM–GA), artificial neural network (ANN) and artificial neural network-genetic algorithm (ANN-GA) in MAT LAB 16.0 are utilized for training and optimization. An attempt has been made to build up a mathematical model in order to set up an indirect correlation between various FDM process parameters and magnitude preciseness. Sequentially to verify the different developed models and the optimum process parameters setting validation tests were also performed. The results showed that various hybrid statistical tools such as RSM-GA, ANN and ANN-GA are very adequate tools for FDM process parameter optimization. The minimum percentage variation in length = 0.06409%, width = 0.03961% and thickness = 0.85689% can be obtained by using ANN-GA.

Modeling and parametric optimization of FDM 3D printing process using hybrid techniques for enhancing dimensional preciseness

Fused deposition modeling (FDM) is one of the RP technique in which a plastic filament is melted in the extruder of the 3D printer and deposited on the build platform of the 3D printer to form the object layer by layer. Part quality and mechanical properties of the FDM fabricated parts extensively depends on process variable parameters such as layer thickness, raster angle, part orientation, raster width, air gap. Hence, selection and optimization of FDM process parameters is vital. The aim and objective of this article is to study and determine the influence of these parameters on processed part through the research work carried out so far. A number of optimization techniques and designs of experiments for the determination of optimum process parameter have been studied.

Rajan Narang

Papers

Modeling and parametric optimization of FDM 3D printing process using hybrid techniques for enhancing dimensional preciseness

Analysis of Process Parameters of Fused Deposition Modeling (FDM) Technique