Abstract
The formation process of surface coatings fabricated with laser cladding is very complicated and coating quality is closely related to laser cladding process parameters. Generally, the optimization and control of process parameters play key roles when preparing high-quality ceramic coating. In this paper, three reasonable parameters were selected for each process parameter based on the preliminary experiment. The experiment of Ti(C, N) ceramic coating prepared with laser cladding was designed via the Taguchi method. The laser power, spot diameter, overlapping ratio, and scanning velocity were selected as the main process parameters, and their effects on coating micro-hardness were analyzed using the signal-to-noise (S/N) ratio and analysis of variance (ANOVA). Then, based on the back-propagation neural network (BPNN) and quantum-behaved particle swarm optimization (QPSO) algorithm, we created the prediction model of BPNN-QPSO neural network for laser cladding Ti(C, N) ceramic coating. The mapping of process parameters to the micro-hardness of the coating was obtained according to the model and we analyzed the influence of process parameters that interacted with the coating’s micro-hardness. The results showed that the interaction of laser cladding process parameters had a significant effect on the micro-hardness of the coating. The established BPNN-QPSO neural network model was able to map the relationship between laser cladding process parameters and coating micro-hardness. The process parameters optimized by this model had similar results with ANOVA. This research provides guidance for the selection and control of ceramic coating process parameters Ti(C, N) prepared via laser cladding.
Highlights
Laser cladding is an important surface modification technique that uses a high power density laser beam to melt powder material and form a coating bonded metallurgically to the substrate surface
The formation process of surface coatings fabricated with laser cladding is very complicated and there are many process parameters that affect coating quality, such as laser power, spot diameter, scanning velocity, preplaced powder thickness, Appl
The results show that the established back-propagation neural network (BPNN)-quantum-behaved particle swarm optimization (QPSO)
Summary
Laser cladding is an important surface modification technique that uses a high power density laser beam to melt powder material and form a coating bonded metallurgically to the substrate surface. It can significantly improve the wear-resistance [1,2], corrosion resistance [3,4], and oxidation resistance [5,6]. The formation process of surface coatings fabricated with laser cladding is very complicated and there are many process parameters that affect coating quality, such as laser power, spot diameter, scanning velocity, preplaced powder thickness, Appl. It is necessary to establish a mapping relationship between process parameters and coating quality to analyze the mapping rule for laser cladding process parameters to coating quality
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