Effect of machining parameters on surface roughness for compacted graphite cast iron by analyzing covariance function of Gaussian process regression
| Autor: | Xiao Ping Liao, Bin Xue, Junyan Ma, Zhenkun Zhang, Shanshan Hu, Yuan Xuepeng, Juan Lu |
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| Rok vydání: | 2020 |
| Předmět: |
Materials science
Covariance function Applied Mathematics 020208 electrical & electronic engineering 010401 analytical chemistry 02 engineering and technology engineering.material Condensed Matter Physics 01 natural sciences 0104 chemical sciences Exponential function Support vector machine Machining Kriging Ground-penetrating radar 0202 electrical engineering electronic engineering information engineering Surface roughness engineering Cast iron Electrical and Electronic Engineering Instrumentation Algorithm |
| Zdroj: | Measurement. 157:107578 |
| ISSN: | 0263-2241 |
| DOI: | 10.1016/j.measurement.2020.107578 |
| Popis: | This study employs Gaussian process regression (GPR) with square exponential covariance function to predict surface roughness of Compacted Graphite Cast Iron (CGI). In addition, a comparative study is conducted on prediction performances for GPR with and without cross-validation, back propagation neural network (BPNN) and support vector machine (SVM) for milling experiment of CGI. Experimental results indicate that prediction performances of GPR without cross-validation and GPR with cross-validation (GPRCV) are similar, and both superior to BPNN and SVM. The effect of machining parameters on surface roughness characterized via length-scale hyperparameters of covariance function is excavated according to prediction principle of GPR. The result shows that cutting speed and feed speed significantly affect surface roughness, depth of cut produces little impact on surface roughness within the given parameter intervals. The analysis of distance between test and training sets and 3D merged surface of surface roughness have verified that the effect is reasonable. |
| Databáze: | OpenAIRE |
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