A method for predicting the morphology of single-track laser cladding layer based on SO-LSSVR

Li, Zhiqiang; Du, Yanbin; Hu, Yanfeng

doi:10.1016/j.mtcomm.2024.108666

Materials Today Communications

2024

DOI: 10.1016/j.mtcomm.2024.108666

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A method for predicting the morphology of single-track laser cladding layer based on SO-LSSVR

Zhiqiang Li,

Yanbin Du,

Yanfeng Hu

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Cited by 2 publications

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A deep learning-based combination method of spatio-temporal prediction for regional mining surface subsidence

Xiao,

Tao,

et al. 2024

Sci Rep

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In coal mining areas, surface subsidence poses significant risks to human life and property. Fortunately, surface subsidence caused by coal mining can be monitored and predicted by using various methods, e.g., probability integral method and deep learning (DL) methods. Although DL methods show promise in predicting subsidence, they often lack accuracy due to insufficient consideration of spatial correlation and temporal nonlinearity. Considering this issue, we propose a novel DL-based approach for predicting mining surface subsidence. Our method employs K-means clustering to partition spatial data, allowing the application of a gate recurrent unit (GRU) model to capture nonlinear relationships in subsidence time series within each partition. Optimization using snake optimization (SO) further enhances model accuracy globally. Validation shows our method outperforms traditional Long Short-Term Memory (LSTM) and GRU models, achieving 99.1% of sample pixels with less than 8 mm absolute error.

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A deep learning-based combination method of spatio-temporal prediction for regional mining surface subsidence

Xiao,

Tao,

et al. 2024

Sci Rep

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Prediction modeling, process optimization, and property analysis of laser cladding Ni60 alloy on 56NiCrMoV7 steel

Liu,

Yang,

Han

et al. 2024

Journal of Laser Applications

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A theoretical and experimental study on laser cladding of Ni60 alloy on 56NiCrMoV7 steel is presented for remanufacturing of machine parts. Due to multiparameter and nonlinearity of the laser cladding process, it is a challenge for developing an accurate method to predict the geometry dimension and the forming quality of multitrack coatings. In this paper, the nonlinear relationships between the process parameters (laser power, powder feeding rate, scanning speed, and overlap rate) and the geometry dimensions (melt width and melt height) as well as the forming qualities (dilution rate and microhardness) of the Ni60 alloy coating are established using the response surface methodology. Statistical analysis gives the influences of each process parameter and their interaction effects on the geometry dimensions and forming qualities of coatings. Process optimizations are also carried out to acquire the desired coating. The verification experiments under optimized conditions are conducted, which indicates that the results obtained by the prediction models agree well with the actual values. Metallographic observation shows that the grain sizes of the microstructure in the coating are different at different zones. X-ray diffraction and scanning electron microscopy results display that the microstructure of the Ni60 coating is mainly composed of austenite phase and (Cr, Fe)7C3 phase. The microhardness of the Ni60 coating is about twice that of 56NiCrMoV7 steel, and the compressive strength of the material with Ni60 coating is higher than that of the 56NiCrMoV7 material. Finally, the practical application of the presented method for repairing the damaged surfaces of a hammer head is proceeded successfully.

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A method for predicting the morphology of single-track laser cladding layer based on SO-LSSVR

Cited by 2 publications

References 24 publications

A deep learning-based combination method of spatio-temporal prediction for regional mining surface subsidence

A deep learning-based combination method of spatio-temporal prediction for regional mining surface subsidence

Prediction modeling, process optimization, and property analysis of laser cladding Ni60 alloy on 56NiCrMoV7 steel

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