Reference-based Virtual Metrology method with uncertainty evaluation for Material Removal Rate prediction based on Gaussian Process Regression

2021 ◽  
Author(s):  
Haoshu Cai ◽  
Jianshe Feng ◽  
Qibo Yang ◽  
Fei Li ◽  
Xiang Li ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Gaussian Process Regression ◽  
Uncertainty Evaluation ◽  
Rate Prediction ◽  
Virtual Metrology

scholarly journals Integrated optimization model in wire electric discharge machining using Gaussian process regression and wolf pack algorithm approach while machining SiCp/Al composite

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401878740 ◽  
Author(s):  
Jun Ma ◽  
Wuyi Ming ◽  
Jinguang Du ◽  
Hao Huang ◽  
Wenbin He ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Material Removal Rate ◽  
Optimization Model ◽  
Electrical Discharge ◽  
Material Removal ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Gaussian Process Regression ◽  
Wire Electrical Discharge Machining ◽  
Al Composite

To further improve prediction accuracy and optimization quality of wire electrical discharge machining of SiCp/Al composite, trim cuts were performed using Taguchi experiment method to investigate the influence of cutting parameters, such as pulse duration ( Ton), pulse interval ( Toff), water pressure ( Wp), and wire tension ( Wt)), on material removal rate and three-dimensional surface characteristics ( Sq and Sa). An optimization model to predict material removal rate and surface quality was developed using a novel hybrid Gaussian process regression and wolf pack algorithm approach based on experiment results. Compared with linear regression model and back propagation neural network, the availability of Gaussian process regression is confirmed by experimental data. Results show that the worst average predictive error of five independent tests for material removal rate, Sq, and Sa are not more than 10.66%, 19.85%, and 22.4%, respectively. The proposed method in this article is an effective method to optimize the process parameters for guiding the actual wire electrical discharge machining process.

PDE-Constrained Gaussian Process Model on Material Removal Rate of Wire Saw Slicing Process

2011 ◽  
Vol 133 (2) ◽  
Author(s):  
Hongxu Zhao ◽  
Ran Jin ◽  
Su Wu ◽  
Jianjun Shi
Keyword(s):  
Gaussian Process ◽  
Material Removal Rate ◽  
Material Removal ◽  
Process Model ◽  
Removal Rate ◽  
Kriging Model ◽  
Random Errors ◽  
Gaussian Process Model ◽  
Thickness Uniformity ◽  
Wire Saw

Thickness uniformity of wafers is a critical quality measure in a wire saw slicing process. Nonuniformity occurs when the material removal rate (MRR) changes over time during a slicing process, and it poses a significant problem for the downstream processes such as lapping and polishing. Therefore, the MRR should be modeled and controlled to maintain the thickness uniformity. In this paper, a PDE-constrained Gaussian process model is developed based on the global Galerkin discretization of the governing partial differential equations (PDEs). Three features are incorporated into the statistical model: (1) the PDEs governing the wire saw slicing process, which are obtained from engineering knowledge, (2) the systematic errors of the manufacturing process, and (3) the random errors, including both random manufacturing errors and measurement noises. Real experiments are conducted to provide data for the validation of the PDE-constrained Gaussian process model by estimating the model coefficients and further using the model to predict the overall MRR profile. The results of cross-validation indicate that the prediction performance of the PDE-constrained Gaussian process model is better than the widely used universal Kriging model with a mean of second order polynomial functions.

A deep learning-based approach to material removal rate prediction in polishing

CIRP Annals ◽  
2017 ◽  
Vol 66 (1) ◽  
pp. 429-432 ◽  
Author(s):  
Peng Wang ◽  
Robert X. Gao ◽  
Ruqiang Yan
Keyword(s):  
Deep Learning ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Rate Prediction
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