Prediction of selective laser melting part quality using hybrid Bayesian network

Abstract

Additive manufacturing (AM) is gaining popularity because of its ability to manufacture complex parts in less time. Despite recent research involving designs of experiments (DOEs) to characterize the relationships between some AM process parameters and various part quality characteristics, to date, there seems to be no universally accepted comprehensive model that relates process parameters to part quality. In this paper, to support the goal of manufacturing parts right the first time, a Bayesian network in continuous domain is developed which relates four process parameters (laser power, scan speed, hatch spacing, and layer thickness) and five part quality characteristics (density, hardness, top layer surface roughness, ultimate tensile strength in the build direction and ultimate tensile strength perpendicular to the build direction). A machine learning algorithm is used to train the network on a database mined from a large number of publications with experimental data from parts built using 316L with selective laser melting. Using this Bayesian network, the user is able to enter a value for one or more known nodes or variables, and the network provides predictions on all the remaining nodes in the form of probability distributions. A method is developed whereby the user inputs are checked for reasonableness using an n -dimensional convex hull, and if necessary a recommendation is returned based on user-defined weights. The network is validated by retaining a subset of the training data for testing and comparing the network’s predictions to the known values. Accuracy is optimized by continually re-training the network using parts built with a specific machine of interest. The industrial relevance of this research is outlined with respect to four current challenges in AM, including the length of time to determine optimal process parameters for a new machine, ability to organize relevant knowledge, quantification of machine variability, and transfer of knowledge to new operators.