Developing physics-informed filters to align unattributed fragmental manufacturing data against a digital characteristics space (DCS)

Metadata are essential to the manufacturing sector. Encompassing a broad spectrum of information related to manufacturing processes, voluminous metadata are extensively obtained from sensing networks and experimentally validated finite element (FE) models. In the era of digital manufacturing, where metadata holds immense potential, its full value remains largely untapped without thorough analysis and characterisation. Yet, an overwhelming majority of the manufacturing metadata obtained during production lacks crucial information and is categorised as ‘fragmental data’, containing only a few (e.g., 1–2) essential pieces of information. Extremely sparse information within the fragmental data hinders the further analysis and characterisation of underlying scientific patterns. To address this challenge, two physics-informed filters, the probability density function filter (PDFF) and feature-driven neighbour filter (FDNF), were developed and embedded within the Evolutionary Binary (EB) algorithm. These filters enabled the alignment by identifying the origins of a set of naturally unattributed fragmental data, taking the digital characteristics space (DCS) of manufacturing processes as an alignment reference. This was realised by comparing the thermo-mechanical digital characteristics (DC), such as the temperature DC, to the counterparts stored in the DCS. An overall accuracy of 90 % was achieved when identifying the origins of unattributed fragmental hot stamping data using PDFF with a minimum length of 10 and FDNF with minimum length of 25. Results demonstrate a novel methodology to unlock the inherent values from unattributed fragmental data that contains extremely sparse information, thereby revolutionising insights into advanced manufacturing sciences.