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Abstract Active learning is a subfield of machine learning that focuses on improving the data collection efficiency in expensive-to-evaluate systems. Active learning-applied surrogate modeling facilitates cost-efficient analysis of demanding engineering systems, while the existence of heterogeneity in underlying systems may adversely affect the performance. In this article, we propose the partitioned active learning that quantifies informativeness of new design points by circumventing heterogeneity in systems. The proposed method partitions the design space based on heterogeneous features and searches for the next design point with two systematic steps. The global searching scheme accelerates exploration by identifying the most uncertain subregion, and the local searching utilizes circumscribed information induced by the local Gaussian process (GP). We also propose Cholesky update-driven numerical remedies for our active learning to address the computational complexity challenge. The proposed method consistently outperforms existing active learning methods in three real-world cases with better prediction and computation time.
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Abstract Surface defect identification is a crucial task in many manufacturing systems, including automotive, aircraft, steel rolling, and precast concrete. Although image-based surface defect identification methods have been proposed, these methods usually have two limitations: images may lose partial information, such as depths of surface defects, and their precision is vulnerable to many factors, such as the inspection angle, light, color, noise, etc. Given that a three-dimensional (3D) point cloud can precisely represent the multidimensional structure of surface defects, we aim to detect and classify surface defects using a 3D point cloud. This has two major challenges: (i) the defects are often sparsely distributed over the surface, which makes their features prone to be hidden by the normal surface and (ii) different permutations and transformations of 3D point cloud may represent the same surface, so the proposed model needs to be permutation and transformation invariant. In this paper, a two-step surface defect identification approach is developed to investigate the defects’ patterns in 3D point cloud data. The proposed approach consists of an unsupervised method for defect detection and a multi-view deep learning model for defect classification, which can keep track of the features from both defective and non-defective regions. We prove that the proposed approach is invariant to different permutations and transformations. Two case studies are conducted for defect identification on the surfaces of synthetic aircraft fuselage and the real precast concrete specimen, respectively. The results show that our approach receives the best defect detection and classification accuracy compared with other benchmark methods.more » « less
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Abstract Aerospace composites assemblies/joining demand ultra-high precision due to critical safety requirements, which necessitate adherence to indicators of risk that are often difficult to quantify. This study examines one important indicator, the residual stress that arises as a result of dimensional mismatch between mating components during the composite structures assembly process. Conventional simulations of large components assemblies investigated the process at a local or global scale, but lacked detailed exploitation of multi-layer stress analysis at integrated scale for composite structures. We develop a novel digital twin simulation for joining large composite structures with mechanical fasteners. The digital twin simulation integrates global features and local features for detailed investigation of stresses. We perform a statistical analysis to better understand the numerical properties of residual stresses after the fastening. Goodness-of-Fit tests and normality tests are used to explore the probabilistic distributions of the stresses exceeding a chosen safety threshold. The case study is conducted based on composite fuselage joining. The results show the stresses in composite structures assembly follow extreme value distributions (such as Weibull, Gumbel) rather than the widely used Gaussian distribution. The stresses in joined composite structures differ across layers, which can be attributed to the anisotropic material behavior.