Design and process optimization are key aspects of manufacturing engineering. This contribution details a machine learning (ML) methodology capable of learning from simulation results, experimental data, or sensor signals, and able to predict and optimize specific user-defined process and design parameters. The prediction-optimization algorithm is based on an enhanced Extreme Gradient Boosting (XGB) algorithm, highly responsive and accurate even for large datasets and complex variable interactions. Once the XGB function is trained, the optimization is carried out by a metaheuristic search algorithm defined according to the Differential Evolution (DE) architecture, allowing for the definition of the combination of independent variables that grant the minimization, or maximization, of the user's defined objective function. The prediction and optimization capabilities have been assessed by applying the XGB-DE algorithm to a previously published authors' dataset and experimental results relevant for the radial-axial ring rolling (RARR) process, showing a prediction accuracy and optimization capability equal to 2.33% and 27.4%, respectively, with respect to authors’ previous finite element and experimental results. The XGB-DE methodology showed a remarkable capability in catching the trend and global minimum of a multi-variable and complex objective function, such as the one involved in complex thermo-mechanical forming processes.
Bibliographical noteFunding Information:
This research was supported by the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF-5199990614253).
© 2023 The Author(s)
- Design optimization
- Differential evolution
- Extreme gradient boosting
- Machine learning
- Process optimization