Robust parameter design based on the ensemble Bayesian model averaging

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There are various uncertainties in the manufacturing process, which may have a significant impact on the accuracy of modeling, leading to the failure of the final process design. Therefore, building accurate models plays an important role in quality improvement for manufacturing processes. To address the above problems, a robust parameter design method based on ensemble Bayesian model averaging (EBMA) is proposed. The proposed method first identifies significant effects using the factorial effect principle and then builds the ensemble models with the EBMA method. An optimization scheme is then developed using the desirability function to determine the optimal parameter settings. Finally, the effectiveness of the proposed method is demonstrated through a real polymer case and a laser beam machining case. The results show that the proposed method not only provides a way to calculate the weights of sub-models in ensemble modeling but also demonstrates enhanced robustness and accuracy compared to existing methods.