An online approach for robust parameter design with incremental Gaussian process

Abstract

Abstract Robust parameter design (RPD), an important method for quality improvement, can effectively mitigate the negative impact of fluctuations on product quality. Traditional RPD adopts offline design, that is, the optimal level of parameter combination is fixed by one-time modeling throughout the production process. This strategy is obviously unreasonable. Online RPD breaks through the limitation of traditional offline design, which can update the optimal setting by utilizing the new sample when the current optimal setting of controllable factors is not suitable. However, there are still some problems in the current version of online RPD, such as poor data fitting ability of response surface model and low efficiency of parameter design. In this paper, a new online RPD method based on Gaussian process (GP) is proposed. The GP model is used to construct the response surface, which has the capacity of dealing with high-dimensional nonlinear data. But traditional GP method adopts batch learning, it cannot update the model online with new samples. So this paper proposes an incremental Gaussian process model (IGP), which can update the response surface in real-time. In the proposed IGP based online robust parameter design method (IGP-RPD), an effective optimization strategy is used to find the optimal setting of controllable factors, and a reasonable selection criterion is used to determine the noise factor setting for the next stage. The optimal setting of the controllable factor in the previous stage and the currently observed noise factor are used as input, and the corresponding quality characteristic is taken as the output. The input and output form a new sample to update the response surface model. In this way, the RPD process can be redone continuously until the desirable optimal setting of the controllable factor is found. Three cases are used to verify the IGP-RPD method and compare it with the existing methods. The experiments manifest that the IGP-RPD method has better performance in both accuracy and efficiency.