Selective LASER melting part quality prediction and energy consumption optimization

Selective LASER Melting (SLM) popularity is increasing because of its ability to quickly produce components with acceptable quality. The SLM process parameters, such as LASER power and scan speed, play a significant role in assuring the quality of customized SLM products. Therefore, the process parameters must be tuned appropriately to achieve high-quality customized products. Most existing methods for adjusting the SLM's parameters use multiple inputs and one or two outputs to develop a model for achieving their desired quality. However, the number of the model's input and output parameters to be considered can be increased to achieve a more comprehensive model. Furthermore, energy consumption is also a factor that should be considered when adjusting input parameters. This paper presents a multi-inputs-multi-outputs (MIMO) artificial neural network model to predict the SLM product qualities. We also try to combine training data from different sources to achieve a more general model that can be used in real applications by industries. The model inputs are LASER power, scan speed, overlap rate, and hatch distance. Moreover, four critical product quality measures: relative density, hardness, tensile strength, and porosity, are used as the model's outputs. After finding a proper model, an energy optimization method is developed using the genetic algorithm in this paper. The objective of the optimization is to minimize the energy consumption of SLM manufacturing with a less compromised output quality. The results of this study can be used in the industry to decrease energy consumption while maintaining the required quality.