One of the key targets of Industry 4.0 and digital production, in general, is the support of faster, cleaner, and increasingly customizable manufacturing processes. Additive manufacturing (AM) is a natural fit in this context, as it offers the possibility to produce complex parts without the design constraints of traditional manufacturing routes, typically reducing both material waste and time to market. Nonetheless, the lack of repeatability of the manufacturing process, which typically translates into a lack of reproducibility and reliability of the quality of the final products compared to traditional subtractive technologies, is currently one of the major barriers to the widespread adoption of AM in mass production. To overcome this limitation, there are growing efforts in recent years toward better integration of advanced information technologies into AM, exploiting the layer-by-layer nature of the build. The consequence of these efforts is twofold: 1) the integration of advanced sensing technologies into the AM systems, making possible the in situ monitoring of huge amounts of data at multiple time scales and resolutions and 2) the ever-increasing role of data-driven approaches [especially machine learning (ML)] in the analysis of such data to provide real-time quality monitoring and process optimization. This article introduces and reviews the key technological developments of this phenomenon, with a special focus on metal powder bed fusion (PBF) technologies that are attracting the highest attention by the industrial AM community. After introducing the main manufacturing quality issues and needs that have to be developed and optimized, we provide a wide overview of the latest progress of in situ monitoring and control in metal PBF, with special regards to sensing technologies and ML approaches. Finally, we identify the open challenges and future research directions in this field.

中文翻译：

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优化粉末床金属增材制造的质量检查和控制：挑战和研究方向

通常，工业4.0和数字化生产的主要目标之一是对更快，更清洁和越来越可定制的制造过程的支持。增材制造（AM）在这种情况下是很自然的选择，因为它提供了在没有传统制造路线设计约束的情况下生产复杂零件的可能性，通常可以减少材料浪费和上市时间。尽管如此，与传统减法技术相比，制造过程缺乏可重复性通常会导致最终产品质量的再现性和可靠性不足，这是在大规模生产中广泛采用AM的主要障碍之一。为了克服这个限制，近年来，人们越来越努力地将高级信息技术更好地集成到AM中，以利用构建的逐层特性。这些努力的结果是双重的：1）将先进的传感技术集成到AM系统中，从而使原位在多个时间尺度和分辨率下监视大量数据，以及2）数据驱动方法[尤其是机器学习（ML）]在分析此类数据以提供实时质量监视和过程优化方面的作用日益增强。本文介绍并回顾了这种现象的关键技术发展，特别关注了金属粉末床融合（PBF）技术，这些技术引起了工业AM界的高度关注。在介绍了必须开发和优化的主要制造质量问题和需求之后，我们提供了有关以下方面的最新进展的广泛概述：原位金属PBF中的监视和控制，特别是涉及传感技术和ML方法。最后，我们确定了该领域的开放挑战和未来的研究方向。