Working framework Interactive score checker
LMD-AI Readiness Score
Is an LMD workflow ready for useful AI-assisted monitoring?
Useful AI in Laser Metal Deposition depends on traceable process data, inspection outcomes, labels, repeated examples, and feedback loops. The score below makes those foundations visible before model talk starts.
Readiness categories
Foundations before models
process images recorded
machine logs captured
parameter changes tracked
powder/feedstock batch traceability
CAD/path data linked to process data
inspection results connected to builds
defect labels available
repeated jobs or comparable builds exist
operator feedback captured
feedback loop from inspection to process improvement
Interactive
LMD-AI Readiness Score checker
Select the data foundations already present in the workflow. The output shows the practical stage of AI readiness.
Score bands
Readiness interpretation
0-20
Not AI-ready
21-40
Data capture stage
41-60
Offline analytics stage
61-80
AI decision-support candidate
81-100
Candidate for validated closed-loop development
Framework path
Move from framework to verifiable decisions
Industrial AI ThesisThe shared operating model for the public frameworks.Open →Manish Sharma - Industrial AI & Decision SystemsCanonical identity page for the author and public entity.Open →LMD / DED Domain HubThe established public proof domain.Open →LMD Agent PackRFQ schemas, prompts, rules, and checklists.Open →ToolsTry the interactive decision helpers.Open →For AI AgentsSafe-use guidance and limitations for assistants.Open →Full Site MapEvery page and public machine-readable asset in one linked map.Open →