NDE 4.0 and Digital Twins in Industry: Current User Experiences and Future Development
Source abstract
Abstract
Abstract Digital twins of physical structures require input from up-to-date measurement data, including information on the condition of the structure from non-destructive evaluation (NDE) techniques. However, currently, in certain engineering sectors, NDE data are very sparse, and where measurements are available, links to digital twins are non-existent or not well-defined. Here, we survey experts across industry sectors, to define the situation within industry and identify challenges to the effective coupling of measurement and digital twins. Thematic analysis of transcripts from semi-structured interviews and focus group discussions with experts in both measurement and digital twin fields has been used to identify common patterns in the current use of NDE and digital twins, along with future development needs. ‘Clusters’ of required research and development goals have been defined to fill knowledge gaps, certain of which must be led by the NDE community: a) Improve data collection; b) Optimise/improve measurement and measurement systems; c) Continuous monitoring capabilities; d) Human out of the loop/automation capabilities; e) Data management (NDE); f) Commercial considerations (NDE/SHM (Structural health monitoring)). Others will require communication and collaboration with the wider engineering and digital twin communities: g) Communication processes and protocols; h) Security of data; i) Reliability of digital twins; j) Commercial considerations (digital tools). To fill these gaps, goals have been organised into a roadmap with a relative timeline, intended as a guideline for the community to effectively develop and exploit digital twin technologies within the NDE 4.0 paradigm.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Digital Transformation in Industry · Educational Leadership and Innovation · Machine Fault Diagnosis Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (55)
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