No abstract

Micro-arc discharge events and dielectric breakdown of oxide films play an important role in the formation process of plasma electrolytic oxidation coating. Single pulse anodization of micro-electrodes was employed to study the discharge behavior and dielectric breakdown of oxide films deposited on aluminum in an alkaline silicate electrolyte. Voltage and current waveforms of applied pulses were measured and surface morphology of micro-electrodes was characterized from images obtained using scanning electron microscope (SEM). A feasible identification method for the critical breakdown voltage of oxide film was introduced. Different current transients of voltage pulses were obtained, depending on applied pulse voltage and duration. In addition, the active capacitive effect and complex non-linear nature of plasma electrolytic oxidation process is confirmed using dynamic electrical characteristic curves. A good correlation between the pulse parameters and shape of discharge channels was observed. Circular opened pores were found to close with increasing potential and pulse width. Finally, the characteristic parameters of a single discharge event were estimated.

Plasma electrolytic oxidation processing is a novel promising surface modification approach for various materials. However, its large-scale application is still restricted, mainly due to the problem of high energy consumption of the plasma electrolytic oxidation processing. In order to solve this problem, a novel intelligent self-adaptive control technology based on real-time active diagnostics and on the precision adjustment of the process parameters was developed. Both the electrical characteristics of the plasma electrolytic oxidation process and the microstructure of the coating were investigated. During the plasma electrolytic oxidation process, the discharges are maintained in the soft-sparking regime and the coating exhibits a good uniformity and compactness. A total specific energy consumption of 1.8 kW h m−2 μm−1 was achieved by using such self-adaptive plasma electrolytic oxidation processing on pre-anodized 6061 aluminum alloy samples.

In a small sample welding test space, and to achieve online prediction and self-optimisation of process parameters for the resistance welding joint quality of power lithium battery packs, this paper proposes a welding quality prediction model. The model combines a chaos game optimisation algorithm (CGO) with the multi-output least-squares support vector regression machine (MLSSVR), and a multi-objective process parameter optimisation method based on a particle swarm algorithm. First, the MLSSVR model was constructed, and a hyperparameter optimisation strategy based on CGO was designed. Next, the welding quality was predicted using the CGO–MLSSVR prediction model. Finally, the particle swarm algorithm (PSO) was used to obtain the optimal welding process parameters. The experimental results show that the CGO–MLSSVR prediction model can effectively predict the positive and negative electrode nugget diameters, and tensile shear loads, with root mean square errors of 0.024, 0.039, and 5.379, respectively, which is better than similar methods. The average relative error in weld quality for the optimal welding process parameters is within 4%, and the proposed method has a good application value in the resistance spot welding of power lithium battery packs.