Data-driven Optimisation of Process Parameters for Reducing Developed Surface Area Ratio in Laser Powder Bed Fusion

Optimising process parameters is a key category of approaches to improve the surface quality of laser powder bed fusion parts. So far, many optimisation methods have been presented, which provide effective ideas and approaches for improving surface quality. However, these methods all focus on the improvement of R_a , S_a , R_sk , or R_Δ q . These parameters are sufficient for most applications. They are however not suitable for applications where functional performance is linked with surface area. To quantify the quality of surfaces for these applications, the developed surface area ratio ( S_dr ) is a more appropriate parameter as it can be used to quantitatively express the exposed functional surface area. In this paper, a data-driven method for optimising five process parameters, namely layer thickness, laser power, hatch spacing, point distance, and exposure time, to reduce the developed surface area ratio of laser powder bed fusion parts is proposed. Firstly, experiments are designed, and actual build and measurement experiments are conducted to acquire a fixed amount of data. A Bayesian ridge regression model for predicting a developed surface area ratio from the five process parameters is then trained and tested and compared with several other machine learning models using the acquired data. After that, optimisation of the five process parameters to reduce developed surface area ratio is carried out using the genetic algorithm, in which the objective function values (developed surface area ratios) are predicted using the established Bayesian ridge regression model. Finally, an additional actual build and measurement experiment is conducted to validate the optimisation. The testing results show that the Bayesian ridge regression model can obtain an average R ^2 score of 0.77 and an average mean absolute error of 8.48 on the testing dataset. The validation results suggest that the developed surface area ratios generated by the optimisation are relatively small, and on average, they are 52.11