Improvement of tensile strength of fused deposition modelling (FDM) part using artificial neural network and genetic algorithm techniques

Abstract

PurposeThis paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.Design/methodology/approachI-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.FindingsThis study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.Research limitations/implicationsThe fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.Practical implicationsThis study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.Originality/valueThe proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.

Parametric Optimization of FDM Process for PA12-CF Parts Using Integrated Response Surface Methodology, Grey Relational Analysis, and Grey Wolf Optimization

“…Six optimization methods were used: cohort intelligence (CI), PSO, GA, teaching–learning-based optimization (TLBO), simulated annealing (SA), and JAYA, which yielded the highest tensile strength. Boppana and Ali [ 17 ] improved the tensile strength of polycarbonate (PC) samples printed using FDM by employing an integrated approach of I-optimal design, ANN, and GA techniques. Mohanty et al [ 18 ] optimized the dimensional accuracy of FDM-printed parts using 10 different metaheuristic approaches, namely GA, SA, PSO, GWO, moth flame optimization (MFO), WOA, JAYA, sunflower optimization algorithm, Lichtenberg algorithm optimization, and forensic-based investigation optimization.…”