Prediction of Formation Quality of Inconel 625 Clads Using Support Vector Regression

The process parameters of pulsed tungsten inert gas (PTIG) have a significant influence on the formation quality, mechanical properties and corrosion resistance of the weld overlay. The PTIG was utilized to deposit Inconel 625 clads with various combinations of the process parameters, which were det...

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Bibliographic Details
Published in:Shanghai jiao tong da xue xue bao 2020-12, Vol.25 (6), p.746-754
Main Authors: Guo, Longlong, Wu, Zebing, He, Yutian, Wei, Wenlan, Xia, Shengyong, Ju, Luyan, Wang, Bo, Zhang, Yong
Format: Article
Language:English
Subjects:
Architecture
Computer Science
Corrosion resistance
Dilution
Electrical Engineering
Engineering
Kernel functions
Life Sciences
Materials Science
Mechanical properties
Nickel base alloys
Performance prediction
Polynomials
Predictions
Process parameters
Radial basis function
Rare gases
Regression analysis
Regression models
Superalloys
Support vector machines
Tungsten
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Summary:The process parameters of pulsed tungsten inert gas (PTIG) have a significant influence on the formation quality, mechanical properties and corrosion resistance of the weld overlay. The PTIG was utilized to deposit Inconel 625 clads with various combinations of the process parameters, which were determined by the central composite design (CCD) method. Based on the experimental results, the relationship between process parameters of PTIG and formation quality of the Inconel 625 clads was established using support vector regression (SVR) with different kernel functions, including polynomial kernel function, radial basis function (RBF) kernel function, and sigmoid kernel function. The results indicate that the kernel functions have a great influence on the prediction of height, width and dilution. The models with RBF kernel function feature the best goodness of fitting and the most accurate against the other SVR models for estimating the height and the dilution. However, the model with polynomial kernel function is superior to the other SVR models for predicting the width. Meanwhile, the prediction performance of the SVR models was compared with the general regression analysis. The results demonstrate that the optimized SVR model is much better than the general regression model in the prediction performance.
ISSN:1007-1172
1674-8115
1995-8188
DOI:10.1007/s12204-020-2225-9