Kapil Kumar Goyal

In this work, an effort has been made to determine the effect of different shape surgical burr on the thermogenesis during bone osteotomy. The abrasion during bone grinding leads to heat generation and subsequently rise in the temperature which may have adverse effects such as osteonecrosis,...

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.

In the present research, the AZ31 alloy is machined by wire-cut electric discharge machining (WEDM). The experiments were designed according to the Box-Behnken design (BBD) of response surface methodology (RSM). The input process variables, namely servo feed (SF), pulse on-time (Ton), servo voltage (SV), and pulse off-time (Toff), were planned by BBD, and experiments were performed to investigate the cutting rate (CR) and recast layer thickness (RCL). The analysis of variance (ANOVA) was performed to determine the influence of machining variables on response characteristics. The empirical models developed for CR and RCL were solved using Multi-Objective Particle Swarm Optimization (MOPSO). Pareto optimal front is used for the collective optimization of CR and RCL. The optimal solution suggested by the hybrid approach of RSM-MOPSO is further verified using a confirmation test on the random setting indicated by the hybrid algorithm. It is found that the minimum RCL (6.34 µm) is obtained at SF: 1700; SV: 51 V; Toff: 10.5 µs; and Ton: 0.5 µs. However, maximum CR (3.18 m/min) is predicted at SF: 1900; SV: 40 V; Toff: 7 µs; and Ton: 0.9 µs. The error percentage of ±5.3% between the experimental results and predicted solutions confirms the suitability of the proposed hybrid approach for WEDM of AZ31.