Keywords
materials
systems
evidence synthesis
reliability
provenance
Abstract
This critical review examines knowledge extraction and traceability in data-centric reliability for sustainable manufacturing, with links to intelligent manufacturing and evidence governance. The organizing question is how relational evidence should be extracted, linked to sources, and kept distinct from model-generated interpretation. Ten or more scholarly and user-supplied records are synthesized through a mechanism-to-decision framework spanning system boundaries, measurement, representation, evaluation, translation, and governance. No experiment, participant dataset, effect estimate, or production result is invented. Particular attention is given to extracted relations being treated as causal facts without source-level verification. The review argues that credible translation requires source-level traceability, explicit validity domains, failure-aware evaluation, and revalidation triggers. Google Scholar-supplied records are retained in structured APA form, and no missing DOI or pagination field is completed by conjecture.
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