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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.
Liu et al. (Wed,) studied this question.