考虑输入不确定性的复合材料结构装配神经网络高斯过程
Neural Network Gaussian Process Considering Input Uncertainty for Composite Structures Assembly
摘要：
发展基于机器学习的智能制造是复合材料结构装配过程的一个重要发展方向。为了提高装配过程的生产质量和效率，需要对复合材料结构的尺寸偏差和残余应力进行准确的预测分析。新型复合材料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;以及（ii）组装过程中不可避免的不确定性。针对这些问题，提出了一种考虑输入不确定性的复合材料结构装配神经网络高斯过程模型。我们的模型的深层架构使我们能够更好地近似复杂的过程，并且考虑输入不确定性使鲁棒建模能够完全结合过程不确定性。仿真和算例分析表明，当响应函数为非光滑非线性时，NNGPIU方法的性能优于其他基准方法。虽然我们使用复合材料结构装配作为一个例子，所提出的方法可以适用于其他工程系统的内在不确定性。
英文摘要：
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.
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