论文标题
神经网络高斯流程考虑复合结构组件的输入不确定性
Neural Network Gaussian Process Considering Input Uncertainty for Composite Structures Assembly
论文作者
论文摘要
开发启用机器学习的智能制造对于复合结构组装过程是有希望的。为了提高组装过程的生产质量和效率，需要对尺寸偏差和复合结构的残余应力进行准确的预测分析。新型的复合结构组装涉及两个挑战：（i）复合材料的高度非线性和各向异性特性； （ii）组装过程中不可避免的不确定性。为了克服这些问题，我们提出了一个神经网络高斯过程模型，考虑了复合结构组装的输入不确定性。模型的深度体系结构使我们能够更好地近似一个复杂的过程，并且考虑输入不确定性可以通过完整纳入过程不确定性来实现强大的建模。基于仿真和案例研究，当响应函数非平滑和非线性时，NNGPIU可以超越其他基准方法。尽管我们以复合结构组件为例，但提出的方法可以适用于具有内在不确定性的其他工程系统。
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.
