Relative density prediction of additively manufactured Inconel 718: a study on genetic algorithm optimized neural network models

Abstract

PurposeThe quality and properties of Inconel 718 (IN718) from selective laser melting (SLM), a major additive manufacturing (AM) process, have been studied extensively. Among all aspects of quality, relative density (RD) is most widely investigated, and it significantly affects the mechanical properties of SLM-ed materials. This study aims to develop robust RD prediction models based on the data accumulated in literature using machining learning approaches.Design/methodology/approachBy mining the literature of SLM-ed IN718, a comprehensive data set is created, which consists of the four major process parameters of laser power, scan speed, hatch spacing, layer thickness and RD data. A back propagation neural network (BPNN) model, along with its two optimized models: genetic algorithm (GA) optimized BPNN (GA-BPNN) and adaptive GA optimized BPNN (AGA-BPNN) models are created for predicting the RD of SLM-ed IN718, and their prediction performances are compared.FindingsOverall, satisfactory prediction accuracies are obtained – the R2 values of the built BPNN, GA-BPNN and AGA-BPNN models are 73.5%, 75.3% and 79.9%, respectively. This also shows that by incorporating the optimization technique, the prediction accuracy of BPNN is improved and AGA-BPNN has the highest accuracy. Moreover, SLM experiments are conducted to test the model predictability. It is found that the predictions generally agree well with the experiment data, and the order of the model prediction accuracies is consistent with that based on the literature data.Originality/valueThis research highlights that by mining literature data, prediction models of RD of SLM-ed IN718 can be obtained with satisfactory performance, which consider more process parameters and cover wider parameter ranges than any individual studies, in a cost-effective manner.