scholarly journals Process Parameter Optimization When Preparing Ti(C, N) Ceramic Coatings Using Laser Cladding Based on a Neural Network and Quantum-Behaved Particle Swarm Optimization Algorithm

2020 ◽  
Vol 10 (18) ◽  
pp. 6331
Author(s):  
Zixin Deng ◽  
Tao Chen ◽  
Haojun Wang ◽  
Shengchen Li ◽  
Defu Liu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Laser Cladding ◽  
Process Parameters ◽  
Ceramic Coating ◽  
Process Parameter ◽  
Micro Hardness ◽  
Swarm Optimization ◽  
Laser Cladding Process ◽  
And Control

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.

scholarly journals Optimization of lapping process parameters of CP-Ti based on PSO with mutation and BPNN

2021 ◽  
Author(s):  
Kaiqiang Ye ◽  
Jianbin Wang ◽  
Hong Gao ◽  
Liu Yang ◽  
Ping Xiao
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Process Parameters ◽  
Particle Swarm ◽  
Process Parameter ◽  
Optimal Process ◽  
Swarm Optimization ◽  
The Neural Network ◽  
Cp Ti ◽  
Optimal Process Parameter

Abstract This work aims to improve the surface quality of commercially pure titanium (CP-Ti) with free alumina lapping fluid and establish the relationship between the main process parameters of lapping and roughness. On this basis, the optimal process parameters were searched by performing particle swarm optimization with mutation. First, free alumina lapping fluid was used to perform an L9(33) orthogonal experiment on CP-Ti to acquire data samples to train the neural network. At the same time, a BP neural network was created to fit the nonlinear functional relation among the lapping pressure P, spindle speed n, slurry flow Q and roughness Ra. Then, the range of the node numbers in the hidden layer of the neural network was determined by empirical formulas and the Kolmogorov theorem. On this basis, particle swarm optimization with mutation was used to search for the optimal process parameter configurations for lapping CP-Ti. The optimal process parameter configurations were used in the neural network to calculate the prediction value. Finally, the accuracy of the prediction was verified experimentally. The optimum process parameter configurations found by particle swarm optimization were as follows: the lapping pressure was 5 kPa, spindle speed was 60 r·min− 1 and slurry flow was 50 ml·min− 1. Then, the configurations were applied to a neural network to simulate prediction: the roughness was 0.1127 µm. The roughness obtained by experiments was 0.1134 µm. The error was 0.62%, which indicates that the well-trained neural network can achieve a good prediction when experimental data are missing. Applying the particle swarm optimization (PSO) algorithm with mutation to a neural network will obtain the optimal process parameter configurations, which can effectively improve the surface quality of CP-Ti lapped with free abrasive.

scholarly journals Microstructure and Mechanical Properties of TiC/TiB Composite Ceramic Coatings In-Situ Synthesized by Ultrasonic Vibration-Assisted Laser Cladding

Coatings ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 99
Author(s):  
Hangbiao Mi ◽  
Tao Chen ◽  
Zixin Deng ◽  
Shengchen Li ◽  
Jian Liu ◽  
...  
Keyword(s):  
Ultrasonic Vibration ◽  
Laser Cladding ◽  
Ceramic Coating ◽  
Composite Ceramic ◽  
Ceramic Coatings ◽  
Residual Tensile Stress ◽  
Micro Hardness ◽  
Metallurgical Bonding ◽  
Laser Cladding Process

Laser cladding coating has many advantages in surface modification, such as a small heat-affected zone, and good metallurgical bonding. However, some serious problems such as pores, and poor forming quality still exist in the coating. To suppress these problems, a novel process of ultrasonic vibration-assisted laser cladding process was adopted to in-situ synthesize TiC/TiB composite ceramic coating on the surface of titanium alloy. Results showed that the introduction of ultrasonic vibration effectively improved the surface topography of the coating, reduced the number of pores in the coating, refined the crystal grains of the coating, decreased the residual tensile stress in the coating, and increased the micro-hardness of the coating. The tribological properties of the coating were significantly improved by the ultrasonic vibration, the wear resistance of the coating fabricated with ultrasonic vibration at power of 400 W increased about 1.2 times compared with the coating fabricated without ultrasonic vibration, and the friction coefficient decreased by 50%.

Optimization of the welding process parameters of Mg–5Gd–3Y magnesium alloy plates with a hybrid Kriging and particle swarm optimization algorithm

2017 ◽  
Vol 232 (22) ◽  
pp. 4038-4048
Author(s):  
Xiaoying Ma ◽  
Zhili Sun ◽  
Peng Cui ◽  
Junwei Wu
Keyword(s):  
Mechanical Properties ◽  
Tensile Strength ◽  
Particle Swarm Optimization ◽  
Process Parameters ◽  
Particle Swarm ◽  
Welding Process ◽  
Kriging Model ◽  
Weld Bead ◽  
Micro Hardness ◽  
Swarm Optimization

Selecting suitable welding process parameters to obtain optimal mechanical properties of the weld bead in AC gas tungsten arc welding is of vital importance. This paper presents a combination method of the Kriging model and particle swarm optimization for optimizing welding process parameters to achieve the optimum mechanical properties, such as the tensile strength and micro-hardness, of the weld bead in AC gas tungsten arc welding of GW53 magnesium alloy plates. The Taguchi orthogonal array is first employed to construct a database including the input process parameters (welding speed, welding current, and protection gas flow) and the responses (the tensile strength and micro-hardness of the weld joint). Then, the Kriging model is used to establish the relationships between the input process parameters and the responses. The optimal mechanical properties of the weld bead corresponding to the welding process parameters are obtained by the proposed hybrid Kriging and particle swarm optimization algorithm. Finally, the effectiveness of the proposed method is verified by contrasting the mechanical properties, such as the tensile strength and the average micro-hardness, in the welding base metal and the weld bead. Furthermore, the main reasons for the decrease in the mechanical properties of welded plates are described in this paper.

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