Neural Network Gaussian Process Considering Input Uncertainty for Composite Structures Assembly
This work provides a more robust predictive analysis tool for engineers working with composite structures assembly, particularly in scenarios with high nonlinearity and inherent process uncertainties.
This paper addresses the challenge of predicting dimensional deviations and residual stress in composite structures assembly, which is crucial for smart manufacturing. The authors propose a neural network Gaussian process model that accounts for input uncertainty, demonstrating superior performance over benchmark methods in simulations and case studies involving nonsmooth and nonlinear response functions.
Developing machine learning enabled smart manufacturing is promising for composite structures assembly process. To improve production quality and efficiency of the assembly process, accurate predictive analysis on dimensional deviations and residual stress of the composite structures is required. The novel composite structures assembly involves two challenges: (i) the highly nonlinear and anisotropic properties of composite materials; and (ii) inevitable uncertainty in the assembly process. To overcome those problems, we propose a neural network Gaussian process model considering input uncertainty for composite structures assembly. Deep architecture of our model allows us to approximate a complex process better, and consideration of input uncertainty enables robust modeling with complete incorporation of the process uncertainty. Based on simulation and case study, the NNGPIU can outperform other benchmark methods when the response function is nonsmooth and nonlinear. Although we use composite structure assembly as an example, the proposed methodology can be applicable to other engineering systems with intrinsic uncertainties.