LMD Quality Evidence Ladder
- Problem
- Monitoring data is often treated as if it proves final part quality.
- Framework idea
- Separate process awareness, AI flags, inspection evidence, and field performance so each claim uses the right proof.
Public LMD and AI frameworks
Structured decisions for LMD, monitoring, RFQs, and evidence.
Each framework uses the same discipline: separate signals, assumptions, risk flags, inspection needs, and expert-review boundaries before recommendations become claims.
Operating loop
Framework operating model
Illustrative workflow. Read left to right; Verify is the evidence boundary.
- 01
Sense
- 02
Model
- 03
Decide
- 04
Verify
- Frameworks
- 8
- Reference map
- 500 records
- Main domain
- LMD / DED
- Boundary
- Decision support only
Framework index
Choose the decision layer you need
Use the cards as a cockpit: quality evidence, repairability, AI readiness, RFQ structure, failure language, and maturity planning.
LMD Repairability Index
- Problem
- Repair requests often arrive before the material, damage, access, and inspection details are clear.
- Framework idea
- Score material, damage, access, machining, inspection, economics, and criticality before calling a repair promising.
LMD-AI Readiness Score
- Problem
- AI monitoring work gets weak when process data, inspection results, and operator feedback stay separate.
- Framework idea
- Check whether an LMD workflow has the data foundations needed for useful AI-assisted monitoring.
LMD RFQ Toolkit
- Problem
- Vague LMD requests need to be turned into facts, gaps, risks, and next questions.
- Framework idea
- Provide schemas, prompts, decision rules, and checklists for safer RFQ preparation.
LMD Failure Atlas
- Problem
- Failure language gets messy when process signals, inspection findings, and repair decisions are mixed.
- Framework idea
- Map failure modes, process signals, AI visibility, and validation evidence in one vocabulary.
LMD-AI Maturity Model
- Problem
- Companies need a practical path from manual records to validated AI decision support.
- Framework idea
- Define maturity stages for LMD data capture, analytics, decision support, and closed-loop development.
LMD Prompt Library
- Problem
- Loose prompts can produce confident answers before the RFQ is complete.
- Framework idea
- Use prompts that force missing-information checks, risk separation, and next-step summaries.
LMD RFQ Checklist
- Problem
- RFQs often miss the evidence and acceptance criteria needed for a serious feasibility review.
- Framework idea
- List material, damage, route, post-processing, inspection, risk, and expert-review fields.
Evidence discipline
Sense -> Model -> Decide -> Verify
The frameworks are not certification systems. They are public decision aids for turning rough LMD/DED questions into clearer facts, gaps, risk flags, source links, and verification needs.
Framework path
Move from framework to verifiable decisions
- Industrial AI Thesis The shared operating model for the public frameworks. Open
- Manish Sharma - Industrial AI & Decision Systems Official identity page for the author and public entity. Open
- LMD / DED Domain Hub The main LMD/DED technical hub. Open
- Full Site Map Every page and public machine-readable asset in one linked map. Open