Modeling of laser aided direct metal deposition of stainless steel using supervised deep learning algorithms

Abstract

Laser Direct Metal Deposition (LDMD) is a very promising additive manufacturing methodology which provides metal cladding layer over the substrate with good corrosion resistance, desired surface finish and very accurate geometry. In this work, an attempt has been made to develop a forward and reverse model of laser aided direct metal deposition utilizing deep learning algorithms. Forward model aims at predicting the deposited bead height and width from the known set of LDMD process parameters such as laser power, scanning velocity, and powder flow rate. Reverse modelling shows the effectiveness of each input parameter over the chosen response factors. Therefore, in the reverse modelling, the optimized input parameters values have been tried to predict to get the desired deposition characteristics during experimentation. Three different neural networks based deep learning architectures, namely, Multi-Layer Feed Forward Neural Network (MLFFNN), Recurrent Neural Networks (RNN) and Radial Basis Function Neural Network (RBFNN), have been put forward to develop the said predictive models. The supervised learning algorithms are used to train the neural networks. The performances of MLFFNN, RNN and RBFNN models are compared among themselves. The results show that all the models are capable of making better predictions and the models can be effectively used in shop floor in optimal selection of most influential parameters for the desired outputs.