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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. 

Abstract Designing and printing metamaterials with customizable architectures enables the realization of unprecedented mechanical behaviors that transcend those of their constituent materials. These behaviors are recorded in the form of response curves, with stress-strain curves describing their quasi-static footprint. However, existing inverse design approaches are yet matured to capture the full desired behaviors due to challenges stemmed from multiple design objectives, nonlinear behavior, and process-dependent manufacturing errors. Here, we report a rapid inverse design methodology, leveraging generative machine learning and desktop additive manufacturing, which enables the creation of nearly all possible uniaxial compressive stress‒strain curve cases while accounting for process-dependent errors from printing. Results show that mechanical behavior with full tailorability can be achieved with nearly 90% fidelity between target and experimentally measured results. Our approach represents a starting point to inverse design materials that meet prescribed yet complex behaviors and potentially bypasses iterative design-manufacturing cycles. 

Additive friction stir deposition is an emerging solid-state additive manufacturing technology that enables site-specific build-up of high-quality metals with fine, equiaxed microstructures and excellent mechanical properties. By incorporating proper machining, it has the potential to produce large-scale, complex 3D geometries. Still early in its development, a thorough understanding of the thermal process fundamentals, including temperature evolution and heat generation mechanisms, has not been established. Here, we aim to bridge this gap through in situmonitoring of the thermal field and material flow behavior using complementary infrared imaging, thermocouple measurement, and optical imaging. Two materials challenging to print via beam-based additive technologies, Cu and Al-Mg-Si, are investigated. During additive friction stir deposition of both materials, we find similar trends of thermal features (e.g., the trends of peak temperature , exposure time, and cooling rate) with respect to the processing conditions (e.g., the tool rotation rate and in-plane velocity ). However, there is a salient, quantitative difference between Cu and Al-Mg-Si; exhibits a power law relationship with / in Cu but with / in Al-Mg-Si. We correlate this difference to the distinct interfacial contact states that are observed through in situ material flow characterization. In Cu, the interfacial contact between the material and tool head is characterized by a full slipping condition, so interfacial friction is the dominant heat generation mechanism. In Al-Mg-Si, the interfacial contact is characterized by a partial slipping/sticking condition, so both interfacial friction and plastic energy dissipation contribute significantly to the heat generation.