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This study presents an overview and a few case studies to explicate the transformative power of diverse imaging techniques for smart manufacturing, focusing largely on variousin-situandex-situimaging methods for monitoring fusion-based metal additive manufacturing (AM) processes such as directed energy deposition (DED), selective laser melting (SLM), electron beam melting (EBM).In-situimaging techniques, encompassing high-speed cameras, thermal cameras, and digital cameras, are becoming increasingly affordable, complementary, and are emerging as vital for real-time monitoring, enabling continuous assessment of build quality. For example, high-speed cameras capture dynamic laser-material interaction, swiftly detecting defects, while thermal cameras identify thermal distribution of the melt pool and potential anomalies. The data gathered fromin-situimaging are then utilized to extract pertinent features that facilitate effective control of process parameters, thereby optimizing the AM processes and minimizing defects. On the other hand,ex-situimaging techniques play a critical role in comprehensive component analysis. Scanning electron microscopy (SEM), optical microscopy, and 3D-profilometry enable detailed characterization of microstructural features, surface roughness, porosity, and dimensional accuracy. Employing a battery of Artificial Intelligence (AI) algorithms, information from diverse imaging and other multi-modal data sources can be fused, and thereby achieve a more comprehensive understanding of a manufacturing process. This integration enables informed decision-making for process optimization and quality assurance, as AI algorithms analyze the combined data to extract relevant insights and patterns. Ultimately, the power of imaging in additive manufacturing lies in its ability to deliver real-time monitoring, precise control, and comprehensive analysis, empowering manufacturers to achieve supreme levels of precision, reliability, and productivity in the production of components. 

Abstract The research reported in this article is concerned with the question of detecting and subsequently determining the endpoint in a long-stretch, ultraprecision surface polishing process. While polishing endpoint detection has attracted much attention for several decades in the chemical-mechanical planarization of semiconductor wafer polishing processes, the uniqueness of the surface polishing process under our investigation calls for novel solutions. To tackle the research challenges, we develop both an offline model and an online detection method. The offline model is a functional regression that relates the vibration signals to the surface roughness, whereas the online procedure is a change-point detection method that detects the energy turning points in the vibration signals. Our study reveals a number of insights. The offline functional regression model shows clearly that the polishing process progresses in three states, including a saturation phase, over which the polishing action could be substantially shortened. The online detection method signals in real-time when to break a polishing cycle and to institute a follow-up inspection, rather than letting the machine engage in an overpolishing cycle for too long. When implemented properly, both sets of insights and the corresponding methods could lead to substantial savings in polishing time and energy and significantly improve the throughput of such polishing processes without inadvertently affecting the quality of the final polish. 

This article presents an overview of the emerging themes in Autonomous Materials Discovery andManufacturing (AMDM). This interdisciplinary field is garnering a growing interest among the sci-entists and engineers in the materials and manufacturing domains as well as those in the ArtificialIntelligence (AI) and data sciences domains, and it offers immense research potential for the indus-trial systems engineering (ISE) and manufacturing fields. Although there are a few reviews relatedto this topic, they had focused exclusively on sequential experimentation techniques, AI/machinelearning applications, or materials synthesis processes. In contrast, this review treats AMDM as acyberphysical system, comprising an intelligent softwarebrainthat incorporates various computa-tional models and sequential experimentation strategies, and a hardwarebodythat integratesequipment platforms for materials synthesis with measurement and testing capabilities. Thisreview offers a balanced perspective of the software and the hardware components of an AMDMsystem, and discusses the current state-of-the-art and the emerging challenges at the nexus ofmanufacturing/materials sciences and AI/data sciences in this nascent, exciting area