01
SENSE
Collect signals, process data, context, operator observations, and missing-information cues.
Working thesis Public operating model Evidence-bound
Manish Sharma Lab
Industrial AI for Decisions You Can Verify
Sense -> Model -> Decide -> Verify
Industrial AI becomes useful when it does more than produce a confident output. It must stay connected to the signals it observed, the model assumptions it used, the decision it recommends, and the evidence that can verify the outcome.
Four-stage loop
Sense, Model, Decide, Verify
This is a practical operating model for public industrial-AI work, not a claim to own a new discipline.
02
MODEL
Combine machine learning, engineering rules, uncertainty, constraints, and traceable assumptions.
03
DECIDE
Structure recommendations, trade-offs, risk priorities, next actions, and human-review boundaries.
04
VERIFY
Connect decisions to inspection, measured outcomes, feedback loops, and physical evidence.
Principles
Decision support has to keep uncertainty visible
The model is useful only when the decision path remains inspectable.
A signal is not proof.
A model output is not automatically an engineering decision.
Missing information should remain visible.
Confidence should not hide uncertainty.
Human review must be explicit where risk requires it.
Verification must match the consequence of failure.
Useful industrial AI creates traceability, not only predictions.
Applied proof
Grounded publicly in LMD / DED
The established public proof domain for this thesis is AI for Laser Metal Deposition and Directed Energy Deposition at Exafuse.
Process monitoring
Signals from LMD/DED systems can expose instability candidates, drift, and review points.
Melt-pool signals
A melt-pool signal can support awareness, but it does not prove final part quality by itself.
Bead geometry
Geometry indicators can guide process review and machining planning when tied to inspection evidence.
Repairability screening
Material, damage, access, tolerance, inspection, and criticality must remain visible before a recommendation hardens.
RFQ preparation
Known facts, missing information, assumptions, risks, and next steps should be separated.
Process route selection
LMD, DED, cladding, SLM/LPBF, machining, replacement, or no repair should be compared with explicit trade-offs.
Inspection evidence
Dimensional checks, NDT, material evidence, and expert review close the loop where risk requires it.
Use with
Public resources that make the thesis practical
These links connect the operating model to existing LMD/DED frameworks, evidence, and RFQ resources.
Operating model
Apply the thesis to the established proof domain
LMD / DED Domain HubWhere the operating model is grounded publicly.Open →LMD Quality Evidence LadderSeparate process signals from inspection proof.Open →LMD Repairability IndexScreen repairability before recommendations harden.Open →Evidence BaseSource context behind the public work.Open →About Manish SharmaHuman background and public career context.Open →Full Site MapEvery page and public machine-readable asset in one linked map.Open →