Metallic powder bed fusion additive manufactured components have been extensively applied in the fields of aerospace, mobility, construction, etc. However, poor surface quality owing to residual powder, stair-step structure, un-melted track, etc. hinder its application in the market. Electrochemical polishing (EP) is a promising method for smoothing metal surfaces without
inducing extra mechanical damage to the product while some disadvantages appeared when being applied on the Laser-Powder Bed Fusion components including low polishing efficiency, poor geometry control, and high cost of the experiment investigation. To solve the problems, this thesis investigated the EP effect of L-PBF 316L stainless steel (316L SS) and TC4 (Ti-6Al-4V) utilising the methods of numerical simulation, experiment, and machine learning. Firstly, a novel 2-dimensional EP model based on the Finite Element Method
utilising the Spatial Frequency Method was proposed to simulate the viscous layer formation process with the consideration of the high surface roughness. In addition, the effect of parameters including diffusion coefficient, inlet velocity, inter-electrode distance, and more importantly, the surface textures on the thickness and uniformity of the viscous layer formation were investigated. Based on the simulation parameters, the EP effect of the current density ranging between 250 - 2000 mA/cm2 on the surface roughness, morphology, weight
loss, and geometry changes was investigated, and a two-step EP process was proposed for optimisation. The experiment results were adopted to machine learning with six algorisms including the Adaptive Boosting algorithm, Random Forest, Multilayer Perceptron Regression, Ridge Regression, Support Vector Regression, and Classification and Regression Trees. Simulation results showed that the diffusion coefficient should be smaller than 1.010-7 m2/s to generate the viscous layer. The conditions of 0 mm/s inlet velocity, at least 3 mm inter-electrode distance, and small and short peak features of the sample surface are
preferable to generate a uniform viscous layer with moderate thickness for L-PBF
components with initial surface roughness ranging between 10 µm - 20 µm. Experiment results showed that the two-step EP method could improve the polishing effect, especially for L-PBF TC4 whose roughness reduction was 70.8 % ±(7.4 %, 11.5 %) more than 66.6 % ± (14.3 %, 10.6 %) and 66.5 % ±(7.8 %, 9.1 %) for one-step EP methods with NaCl solutions and A2 electrolytes. Finally, the Multilayer Perceptron Regression and Random Forest algorithms have optimal prediction accuracy and stability, respectively. The corresponding mean and variance of the coefficient of determination values were 0.85 ± (0.08, 0.11) and
0.0017. This simulation-experiment-prediction procedure can also be applied to guide the EP process of other metals or electrolytes to improve polishing efficiency and reduce the experiment cost.