Jatinder Singh

The transformation of plastic waste into valuable products remains a significant challenge for sustainable manufacturing.

The purpose of the study is to examine and analyze the effect of different process parameters of fused deposition modeling process, namely, nozzle diameter, build orientation, infill density, raster pattern, layer height, and print speed on the response variables, namely, tensile strength, build time, and material consumption. Taguchi L₂₇ orthogonal array experimental design has been adopted to print the PLA samples for experimental investigation. The optimum values of process parameters are obtained from the signal‐to‐noise ratio values of responses. The performed confirmation tests revealed that optimum process parameters combination determined through Taguchi method improves the tensile strength, reduce the build time, and material consumption significantly. Confirmation from analysis of variance (ANOVA) results revealed that the build orientation and nozzle diameter are the most influential process parameters for all the considered responses. The infill density is found to be an influential process parameter for material consumption. Further, the developed regression models for prediction of response characteristics and normal probability plots show significance of coefficients for predicted models. Results from the confirmation test revealed that the PLA part printed with optimum settings ((Nd)₃‐(Bo)₃‐(Id)₃‐(Rp)₁‐(Lh)₂‐(Ps)₁)) for tensile strength shows maximum tensile strength of 61.24 MPa with an improvement of 6.26%. Similarly, the part printed with the optimum settings ((Nd)₃‐(Bo)₁‐(Id)₂‐(Rp)₁‐(Lh)₃‐(Ps)₃) for build time takes 0.32 h of time with a reduction of 8.57%. The part printed with the optimum settings ((Nd)₁‐(Bo)₁‐(Id)₁‐(Rp)₂‐(Lh)₁‐(Ps)₂) for material consumption consumes material of 7.6 g with a reduction of 2.56%. Fracture mechanics results depict that variation of process parameters during the fabrication of samples has significant influence on the tensile strength.

Fused deposition modeling (FDM) is one of the most economical and popular technology amongst numerous additive manufacturing techniques. The quality of FDM fabricated parts is highly sensitive to the production parameters. Thus, in the present work, an investigation on the FDM printed polylactic acid parts has been performed considering six printing process parameters, that is, nozzle diameter, build orientation, raster pattern, layer height and print speed to develop the feedforward backpropagation (FFBP) artificial neural network prediction model for the prediction of responses, namely, tensile strength, material consumption, build time and surface quality. Tensile specimens as per L₂₇ orthogonal array are printed considering the various combination of parameters. The printed samples have been subjected to tensile strength testing, surface roughness measurement, build time recording, and material consumption evaluation. The highest tensile strength of 57.633 MPa, lowest surface roughness of 1.71 μm, lowest build time of 0.35 h and lowest material consumption of 7.8 g are observed. The experimental results have been used to develop the artificial intelligence‐based prediction model through FFBP algorithm and sigmoid transfer function to predict the responses. The best performance of the developed neural network with R² for testing (0.99343), training (0.99366), and validation (0.99372) of data is recorded for prediction of responses with minimum percentage error. The study concluded that developed model is capable of predicting the responses of FDM process according to the input process parameters.