Sense
Process signals, images, measurements, logs, human observations, and RFQ inputs.
Working thesis Public operating model Evidence-bound
Manish Sharma / working method
Industrial AI for Decisions That Need Evidence
My working method: Sense -> Model -> Decide -> Verify
I use this method because a prediction alone does not finish the industrial job. The output has to be tied to what it observed, the assumptions that may limit it, the decision under consideration, and the evidence that can responsibly check the outcome. LMD/DED is the current proving ground where I can test that discipline in public.
Four-stage loop
Sense, Model, Decide, Verify
This is my practical operating model for industrial-AI work, not a claim to own a new discipline.
Operating loop
Sense -> Model -> Decide -> Verify
Signals, assumptions, bounded decisions, and verification evidence.
Illustrative workflow. Read left to right; Verify is the evidence boundary.
Confidence is not approval. The loop is useful only when process signals, model assumptions, decisions, and verification evidence stay separate.
- 01
Sense
Collect signals, process data, context, operator observations, and missing-information cues.
- 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.
Use Sense for observed inputs, Model for assumptions and uncertainty, Decide for bounded recommendations, and Verify for inspection, testing, documentation, measured outcomes, and expert review.
Stage links
Connect each stage to a source, framework, or tool
The method is useful only when it routes readers to concrete LMD/DED resources and an explicit verification path.
Model
AI, statistical, process, and rule-based models with visible assumptions and uncertainty.
Decide
Repairability, process route, inspection need, risk level, and next-step recommendations.
Verify
Quality evidence, testing, inspection, documentation, release criteria, and expert review.
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.
Interactive explainer
Signal is not proof.
Select a signal. The useful question is not whether the signal is interesting; it is what the signal can suggest, what it cannot prove, and what evidence closes the loop.
Selected signal
Melt-pool image feature
What it can suggest:
possible process drift, instability, spatter, geometry change, or review point
What it cannot prove:
final material quality, fatigue behavior, or service safety
Evidence needed next:
- dimensional inspection
- NDT where risk requires it
- material testing or metallography when acceptance depends on it
- expert review
Confidence is not approval. Monitoring can support review; it does not replace inspection, testing, expert review, or release evidence.
What changes the decision
Monitoring signals need context before they become useful
A signal becomes more useful when it can be tied to inspection, calibration, repeatability, and acceptance criteria.
Correlation with inspection
Drift history
Sensor calibration
Image quality
Process repeatability
Acceptance criteria
Applied proof
LMD / DED is my current proving ground
My strongest public technical context is AI for Laser Metal Deposition and Directed Energy Deposition at Exafuse. It is where this method meets physical signals, materials, robotics, repair, and inspection.
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 my working method to existing LMD/DED frameworks, evidence, and RFQ resources.
Operating model
Apply the thesis to LMD/DED decisions
- LMD / DED Domain Hub Where the operating model is grounded publicly. Open
- LMD Quality Evidence Ladder Separate process signals from inspection proof. Open
- LMD Repairability Index Screen repairability before recommendations harden. Open
- Full Site Map Every page and public machine-readable asset in one linked map. Open