Optimal surrogate building using SVR for an industrial grinding process

Transient states modeling of industrial grinding process with significant accuracy is extremely essential to run these energy intensive processes in optimal conditions ensuring sustainability. Traditional modeling using physics-based approach not only demands extensive process knowledge but also results in time-expensive models difficult to use during iterative processes like optimization. Proposing Support Vector Regression (SVR) as an alternative data driven tool, such a surrogate building task has been performed under an optimization framework. Minimizing square root of mean square error (RMSE) between ground truth and model predictions, optimal hyper-parameter combination is achieved using a novel genetic algorithm-based formulation indicating a paradigm shift as opposed to the usual practice of determining them heuristically. The RMSE of the grinding model obtained using the proposed formulation is reported as 0.00496. When compared with another model obtained using conventional approach, prediction plots indicate the effectiveness of the novel algorithm. Comparison with coefficient of correlation leads similar conclusion, where the least RMSE model has 99.88% and the conventional model has 71.40% correlation value. Such dynamic surrogates with optimal hyper-parameter settings can be extremely useful for control and optimization of grinding processes and can be easily extendable to design of experiment-based response surface modeling.

Automated summary

Researchers propose a data-driven modeling method using Support Vector Regression (SVR) for transient state modeling of industrial grinding processes. By optimizing the hyper-parameter combination and comparing with traditional methods, the results indicate the superiority of this approach in terms of accuracy and effectiveness.