Prediction of compressive strength in additively fabricated part using long short term memory based neural network

Abstract

With ability to produce complex geometries and accurate parts, additive manufacturing (AM) could be applied in various sectors including aerospace and biomedical, though the frequent occurrence of defects and unavailability of feasible monitoring and control system prohibits their effective utilization. Technological developments and use of artificial intelligence, machine learning models could be a perfect solution to the problems. In this study, a predictive model is developed using deep learning (DL) algorithm and investigated the compressive strength of fused deposition modelling (FDM) printed part. An improved recurrent neural network (RNN) model called long short-term memory (LSTM) is used for predicting the compressive strength of part produced by FDM process. Model developed with LSTM utilizes layer-wise relevance propagation (LRP) algorithm to establish relation between the FDM part and LSTM layers. Predictive model developed in this study uses the back propagation algorithm to establish the relation between the compressive strength of part and input process variables. A total of 81 poly lactic acid samples were printed and tested, out of which 57 samples were used for training the model and 16 data were used for validating the model and remaining data were used for testing the model. Effects of process variables including infill density, print orientation, raster orientation and layer thickness have been analyzed. The values obtained from the predictive model was in good agreement with experimental results along 1% error. LSTM based DL model predicted the compressive strength closer to the measured values. From the results obtained, the developed model holds good for predicting the compressive strength of FDM printed part.