A transfer learning-based machine learning approach to predict mechanical properties of different material types fabricated by selective laser melting process

Abstract

The necessity for a massive dataset has limited the desirability of the machine learning approaches for industrial applications, especially in the metal additive manufacturing processes, where, collecting a large dataset is expensive and virtually ineffective. Concerning this restriction, an effective machine learning technique should be developed to bridge the gap between the academia and the industry. Hence, in this research, a transfer learning-based artificial neural network (TL-ANN) model was developed to predict the mechanical properties of different metallic specimens fabricated by selective laser melting (SLM) process. This model was integrated with a Bayesian hyperparameters optimization algorithm to select the optimum training parameters of the model. The proposed model consists of a target network and a source network. The source network was trained based on the mechanical properties that were obtained experimentally for various materials, including pure and alloyed copper, steel, titanium, nickel, etc. The overall regression correlation coefficient ( R) of the TL-ANN model was about 0.99, with the mean square error of testing, validation, and training of datasets of about 2.031, 1.423, and 1.068, respectively, representing the successful execution of the source network in prediction of the mechanical properties of the SLMed parts. Using the achieved knowledge of the source network, the target network was trained to predict the mechanical properties of the target material (here SLMed pure and alloyed aluminum specimens). The obtained results revealed that with the help of the transfer learning, the hybrid neural network could predict the mechanical properties of SLM-fabricated aluminum parts with a high accuracy level, even with the small number of training dataset of the target material. To demonstrate the influence of transfer learning in the accuracy of the model, a separate network was developed from scratch, i.e. with random initial weights of the neurons. The R-values of the test dataset of the individual model for the output parameters of ultimate tensile strength, relative density, and yield strength of the fabricated aluminum samples were 0.787, 0.742, and 0.817, respectively, as compared with that of TL-ANN model of 0.966, 0.903, and 0.971, respectively, representing an average of 21% enhancement in the accuracy of the predictivity of the model by application of transfer learning algorithm.