Data form the backbone of manufacturing sciences, initiating a revolutionary transformation in our understanding of manufacturing processes by unravelling complex scientific patterns embedded within them. Digital characteristics (DC) is defined as a strategic framework mapping the manufacturing metadata and integrates essential information across the entire spectrum spanning from the design, manufacturing, and application phases of manufactured products. By carrying these inherent distinctive features, DC serves as the ‘DNA’ for every manufacturing process. Through enormous experimental and simulation efforts, a digital characteristics space (DCS) was established to provide access to the up-to-date and information-rich DC repository containing over 140 manufacturing processes. In digital manufacturing, sensing networks play a pivotal role in metadata acquisition, contributing nearly 2000 petabytes of metadata annually. However, an overwhelming majority-nearly 100 %-of the data collected through sensing networks can be categorised as ‘fragmental data’, encompassing only a few (e.g., 1–2) essential pieces of information. Moreover, the current absence of efficient metadata identification methods presents an emerging and critical need to enable industry to unlock the full potential of manufacturing metadata. To this end, the authors of the present paper developed a physics-based alignment filter, considering DCS as an alignment reference similar to the ‘GenBank’. Specifically, the origins of naturally unattributed fragmental data were identified with an overall probability exceeding 82 % with a minimum length of 10 data points. The probability increased to 99 % when aligning the fragmental data with length of 100 data points. This was realised by comparing the thermo-mechanical DC of fragmental data with their counterparts stored in the DCS. Subsequently, we analysed the distinct DC of this identified manufacturing process to facilitate digitally-enhanced research. This study introduces a pioneering methodology developed to extract latent values embedded in manufacturing metadata derived from unattributed fragmental data. By revolutionising insights into advanced manufacturing sciences, our work provides an enabling approach for identifying and leveraging fragmental data sourced from sensing networks. This empowers the exploration of manufacturing metadata, promising transformative implications for the field.

中文翻译：

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通过数字特征 (DC) 对齐释放制造元数据的内在价值


数据构成了制造科学的支柱，通过揭示其中嵌入的复杂科学模式，引发了我们对制造过程的理解的革命性转变。数字特征 (DC) 被定义为映射制造元数据并集成制造产品设计、制造和应用阶段整个范围内的基本信息的战略框架。通过携带这些固有的独特特征，DC 成为每个制造过程的“DNA”。通过大量的实验和模拟工作，建立了数字特征空间 (DCS)，以提供对包含 140 多个制造流程的最新且信息丰富的 DC 存储库的访问。在数字制造中，传感网络在元数据获取中发挥着关键作用，每年贡献近 2000 PB 的元数据。然而，绝大多数（几乎100%）通过传感网络收集的数据可以归类为“碎片数据”，仅包含少数（例如1-2）条基本信息。此外，当前缺乏有效的元数据识别方法，迫切需要使行业能够充分发挥制造元数据的潜力。为此，本文的作者开发了一种基于物理的对齐滤波器，将 DCS 视为类似于“GenBank”的对齐参考。具体来说，自然无归属的碎片数据的起源被识别，总体概率超过 82%，最小长度为 10 个数据点。当对齐长度为 100 个数据点的碎片数据时，概率增加到 99%。 这是通过将碎片数据的热机械 DC 与存储在 DCS 中的对应数据进行比较来实现的。随后，我们分析了该已确定的制造过程的独特 DC，以促进数字增强的研究。本研究介绍了一种开创性的方法，该方法旨在提取源自未归属碎片数据的制造元数据中嵌入的潜在值。通过彻底改变对先进制造科学的见解，我们的工作提供了一种识别和利用来自传感网络的碎片数据的可行方法。这促进了对制造元数据的探索，有望对该领域产生变革性影响。