Modeling of Steel Laser Cutting Process Using Finite Element, Machine Learning, and Kinetic Monte Carlo Methods

Laser cutting is a manufacturing technology that uses a focused laser beam to melt, burn and vaporize materials, resulting in a high-quality cut edge. Although previous efforts are primarily based on a trial-and-error approach, there is insufficient understanding of the laser cutting process, thus hindering further development of the technology. Therefore, the motivation of this thesis is to address this research need by developing a series of models to understand the thermal and microstructure evolution in the process. The goal of the thesis is to design a tool for optimizing the steel laser cutting process through a modeling approach. The goal will be achieved through three interrelated objectives: (1) understand the thermal field in the laser cutting process of ASTM A36 steel using the finite element (FE) method coupled with the user-defined Moving Heat Source package; (2) apply machine learning method to predict heat-affected zone (HAZ) and kerf, the key features in the laser cutting process; and (3) employ kinetic Monte Carlo (kMC) simulation to simulate the resultant microstructures in the laser cutting process. Specifically, in the finite element model, a laser beam was applied to the model with the parameters of the laser’s power, cut speed, and focal diameter being tested. After receiving results generated by the finite element model, they were then used by two machine learning algorithms to predict the HAZ distance and kerf width that is produced due to the laser cutting process. The two machine learning algorithms tested were a neural network and a support vector machine. Finally, the thermal field was imported into the kMC model as the boundary conditions to predict grain evolution’s in the metals. The results of the research showed that by increasing the focal diameter of a laser, the kerf width can be decreased and the HAZ distance experienced a large decrease. Additionally, a pulse-like pattern was observed in the kerf width through modeling and can be minimized into more of a uniform cut through the increase of the focal diameter. By increasing the power of a laser, the HAZ distance, kerf width, and region of the material above its original temperature increase. Additionally, through the increase of the cut speed, the HAZ distance, kerf width, kerf pulse-like pattern, and region of the material above its original temperature decrease. Through the incorporation of machine learning algorithms, it was found that they can be used to effectively predict the HAZ distance to a certain degree. The Neural Network and Support Vector Machine models both show that the experimental HAZ distance data lines up with the results derived from ANSYS. The Gaussian Process Regression HAZ model shows that the algorithm is not powerful enough to create an accurate prediction. Additionally, all of the kerf width models show that the experimental data is being overfit by the ANSYS results. As such, the kerf width results from ANSYS need additional validation to prove their accuracy. Using the kMC model to examine the microstructure change due to the laser cutting process, three observations were made. First, the largest growth occurs at the edge of the laser where the material was not hot enough to be cut. Then, grain growth decays as the distance from the edge increases. Finally, at the edge of the HAZ boundary, grain growth does not occur