The B-52 System Program Office has an identified need to recover engineering data from 50-years of legacy paper documentation. The recovery of this data in digitally useable formats is of vital importance to the B-52 System Life Extension Program (SLEP). The availability of accurate historical data is essential for a Digital Engineering (DE) enabled B-52 SLEP. RJLG proposes an innovative Machine Learning (ML) enhanced Chart-Mining Tool to improve the cost/quality/speed of authoritative decisioning for B-52 modernization acquisition efforts. RJLG will utilize a high-value B-52 DE use-case and ML algorithms developed by Penn State University (PSU) to perform a feasibility study illustrating the possibility that such chart-mining capabilities are possible and that such capabilities can be further augmented by ML. Such capabilities will provide significant benefits to improve overall data-mining, complex data transformation and conflict identification capabilities to give B-52 SPO engineers the tools required to rapidly respond to future threats. RJLG will use its data ingestion/indexing/metadata-extraction/semantic-linking/data-curation capability, commercially referred to as SEAMS as the basis to build the DE toolset. The ultimate architecture will augment the existing SEAMS platform with more complex ML algorithms. The technology can be applied cross-industry in organizations where timely/data-driven decision making is critical.
