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Working framework Markdown available Decision-support only
The LMD-AI Maturity Model by Manish Sharma
A six-level model for moving from manual LMD observation to sensor recording, offline analysis, real-time decision support, semi-closed-loop correction, and validated closed-loop control.
Preliminary decision-support only. Final feasibility depends on base material, geometry, service conditions, inspection requirements, and expert review.
Operating loop
Maturity loop
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Model
03
Decide
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Verify
Data before models
The maturity question is not which model to use first. It is whether process, material, geometry, and inspection data are connected.
Decision support before autonomy
Start with missing-information checks, risk routing, and evidence planning before any closed-loop claims.
Validation before confidence
Useful AI maturity depends on validation, escalation rules, drift monitoring, and expert review.
Framework brief
Use maturity as a planning model
Preliminary decision-support only. Final feasibility depends on base material, geometry, service conditions, inspection requirements, and expert review.
Definition
A six-level model for moving LMD workflows from manual observation to validated closed-loop control candidates.
Problem it solves
Organizations often jump to model claims before data capture, traceability, inspection linkage, and governance are ready.
Who it is for
Manufacturing teams, engineers, AI developers, students, and managers planning LMD data maturity.
Inputs required
RFQ records, material traceability, CAD/path linkage, logs, monitoring data, inspection outcomes, labels, comparable jobs, governance, and review rules.
Output
A maturity level and the next practical capability to build.
Evidence needed
Traceable records, linked inspection outcomes, validation results, drift checks, escalation rules, and model-update control.
Limitations
It is a planning framework, not a qualification system or proof that closed-loop control is ready or safe.
Example use case
Deciding whether a team should improve job IDs and inspection links before starting AI anomaly detection.
Thesis connection
Sense records, model maturity, decide the next data capability, and verify with measured outcomes.
Maturity stages
From scattered records to validated loops
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Manual observation
Maturity signal: Operators watch the process and record judgment in notes, photos, or memory.
AI capability: AI has no reliable process history to learn from.
Next move: Start collecting consistent RFQ, material, parameter, photo, and inspection records.
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Sensor recording
Maturity signal: Images, melt-pool video, machine logs, powder feed, laser power, and robot path data are recorded.
AI capability: AI can help organize records, but linkage to outcomes is still weak.
Next move: Create job IDs that connect RFQ, CAD/path, parameters, feedstock, and inspection evidence.
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Offline analysis
Maturity signal: Jobs have linked material, geometry, parameters, feedstock batches, monitoring data, and operator notes.
AI capability: AI can classify anomalies after the job and support offline risk summaries.
Next move: Connect process signals to measured inspection outcomes and comparable build families.
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Real-time decision support
Maturity signal: Monitoring data, parameter changes, inspection results, and defect labels can be analyzed together.
AI capability: AI can warn operators during the build and route signals into evidence questions.
Next move: Validate model behavior against known outcomes and define escalation rules.
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Semi-closed-loop correction
Maturity signal: AI outputs are tested, bounded, logged, reviewed, and connected to expert decisions and controlled parameter-change recommendations.
AI capability: AI can recommend parameter adjustments under engineering governance.
Next move: Build governance for model updates, drift checks, and closed-loop experiments.
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Validated closed-loop control
Maturity signal: Validated feedback loops connect process, inspection, and outcome data across repeated jobs.
AI capability: AI may adjust the process automatically only with inspection-backed validation and controlled qualification.
Next move: Treat deployment as a qualification program, not a software toggle.
Related resources
Use with the readiness score and evidence ladder
The maturity model explains the organizational path. The readiness score checks the current data foundations. The evidence ladder keeps AI outputs connected to physical validation.
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 →Full Site MapEvery page and public machine-readable asset in one linked map.Open →