Research on twin method of transient temperature field in laser additive manufacturing based on optimization of measured parameters

Abstract

The transient temperature field during laser-directed energy deposition has a crucial impact on the quality of manufactured parts. In this study, in order to solve the problems that the transient temperature field is difficult to measure directly and the traditional numerical simulation data are not real time and the model is inaccurate, a transient temperature field twin method based on the optimization of measured parameters is proposed. First, based on heat transfer, a twin model of temperature-dependent thermophysical parameter fluctuations is built, and the model defines the time-dependent power parameters. In order to perform numerical simulations for the acquisition of the temperature field distribution and to verify the accuracy of the model through the comparison of synchronized lateral validation experiments, an integrated measurement-validation experimental system is constructed. A twin model validation method is proposed, in which the frontal measurement experiments are synchronized with the lateral validation experiments. The real-time radius of the molten pool and temperature parameters are extracted from the frontal measurement experiments and inputted into the twin model. In order to confirm the great realism of the built twin model, the simulation of the twin temperature field under various laser strengths is examined in the last step. The experimental findings demonstrate that the temperature field twin physical model developed in this work is capable of faithfully simulating temperature field variations brought about by real-time laser additive process parameter changes. This approach reduces the number and expense of actual tests, helps to adjust process parameters to ensure an improvement in product quality and performance, and makes up for the lack of real-time problems in traditional numerical simulation. It also improves the accuracy and real-time simulation model. Finally, it has the ability to instantly provide input and track the production process in real time. It helps to advance the application of digital-twin technology in the field of additive manufacturing.