A Study on Wire Electric Discharge Machining Process Parameters Prediction Model Using Deep Learning Neural Network

Gupta, Khanish; Goyal, Kapil Kumar; Phanden, Rakesh; Rani, Deepika

doi:10.1007/978-981-19-0707-4_45

Cited by 2 publications

(2 citation statements)

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“…The strength and quality of the printed 3D parts depend upon the selection of influential input process parameters. [ 54,55 ] Good dimensional accuracy and higher part strength are obtained with the lower layer height using different nozzle diameters. [ 56,57 ] In a study, [ 58 ] the tensile properties of the PLA printed samples considering build orientation, raster pattern and filling percentage were investigated, and the 3D printed parts with triangular raster pattern for biomedical implant performs superior in mechanical strength as compared to 3D parts printed with grid and tri‐hexagonal pattern.…”

Section: Methodsmentioning

confidence: 99%

“…[24,[45][46][47][48][49][50][51][52][53] In a study, [29] the influence of process parameters, that is, cutting speed, feed and depth of cut was investigated on tool wear and supervised feed-forward ANN prediction model was developed to estimate the tool wear in turning process. [54] Gupta et al [55] developed the deep learning neural network model to study the wire electric discharge machining (WEDM) process parameters. It was observed that the RSM model got overshoot which shows that the RSM is incapable for prediction purposes, however, the incapability of RSM method was easily handled by ANN approach.…”

Section: Literature Summarymentioning

confidence: 99%

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Development of artificial intelligence‐based neural network prediction model for responses of additive manufactured polylactic acid parts

et al. 2022

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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 L 27 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 R 2 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.

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Section: Methodsmentioning

confidence: 99%