The dilution rate has a significant impact on the composition and microstructure of the coatings, and the dilution rate and process parameters have a complex coupling relationship. In this study, three process parameters, namely laser power, powder feeding rate, and scanning speed, were selected as variables to design the orthogonal experiment. The dilution rate and hardness data were obtained from AlCoCrFeNi coatings based on orthogonal experiments. Then, a BP neural network was used to establish a prediction model of the process parameters on the dilution rate. The established BP neural network exhibited good prediction of the dilution rate of AlCoCrFeNi coatings, and the average relative error between the predicted value and the experimental value was only 5.89%. Subsequently, the AlCoCrFeNi coating was fabricated with the optimal process parameters. The results show that the coating was well-formed without defects, such as cracks and pores. The microhardness of the AlCoCrFeNi coating prepared with the optimal process parameters was 521.6 HV0.3. The elements were uniformly distributed in the microstructure, and the grain size was about 20–60 μm. The microstructure of the AlCoCrFeNi coating was only composed of the BCC phase without the existence of the FCC phase and intermetallic compounds.
Optimization of process parameters for bio-enzymatic and enzymatic saccharification of waste broken rice for ethanol production using response surface methodology and artificial neural network–genetic algorithm
