Manish Sharma Lab
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Industrial AI & Decision Systems LMD / DED focus Engineering review prep

Manish Sharma Lab

AI for Laser Metal Deposition decisions you can verify.

I develop AI frameworks, RFQ tools, and process-monitoring notes that help engineers structure LMD/DED repair, cladding, and metal additive manufacturing questions before a detailed technical review.

Built to organize questions before expert review.
Start LMD Decision Cockpit See worked example Request Exafuse review

Illustrative LMD process schematic

Process signal to inspection evidence

Read from the deposition zone toward the evidence needed for technical review.

  1. Laser beamFocused energy reaches the deposition zone.
  2. Melt poolThe local molten region is a process observation point.
  3. Deposited trackMaterial is added along the intended repair or build path.
  4. Process signalMonitoring can flag a review question, not final quality.
  5. Inspection evidenceInspection and expert review establish the next defensible claim.

Input

a rough LMD question

Output

Decision Brief v1.0

Next step

structured expert review

Active module

Decision Cockpit

  • Local session
  • Browser-local
  • Decision-support only
LMD Decision CockpitLMD Decision Brief v1.0Conservative output

Start with a rough LMD question. Leave with a brief.

Pick the situation, mark what is known, then expose missing information, risk flags, evidence needed, and an Exafuse review route. Inputs stay in this browser session only.

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Example scenario: worn steel shaft near bearing seat.

Review the worked output, start your own brief here, or open the full workbench.

Repair damaged/worn part: start with LMD Repairability Quick Check. Not enough information. Missing information and risk flags remain visible in the brief.

Thesis: a signal is not proof

Sense -> Model -> Decide -> Verify

A signal is not proof. Process signals can support review. They do not replace inspection, testing, expert review, or release evidence.

Try explainer No-hype boundary

Start here

Choose the path that matches your question

Repair decision Evaluate a worn or damaged component. Expose material, damage, access, finishing, and inspection gaps before repair claims harden. Start repair brief RFQ preparation Turn a rough request into a usable RFQ brief. Separate known facts, critical gaps, useful context, risks, and the next review action. Structure an RFQ Monitoring evidence Interpret a process signal without overclaiming quality. Connect monitoring context to inspection evidence and keep the release boundary visible. Review a signal Process selection Compare LMD with SLM / LPBF route signals. Start with part size, local deposition, feature complexity, tolerance, and qualification needs. Compare routes

Flagship assets

Start with the three LMD decision assets

These are the shortest route from a vague LMD/DED question to a structured decision-support workflow.

LMD Quality Evidence Ladder

Problem
Monitoring data is often treated as release proof.
Helps decide
Which evidence level is needed for a claim.
Cannot prove
Final part quality without inspection/testing.
 Open framework

LMD Repairability Index

Problem
Repair requests arrive before material, damage, access, and inspection facts are clear.
Helps decide
Whether a repair candidate is worth expert review.
Cannot prove
Repair feasibility, release, or service safety.
 Open index

LMD RFQ Toolkit

Problem
Vague RFQs hide missing facts and risk flags.
Helps decide
How to structure known facts, gaps, assumptions, and next questions.
Cannot prove
Replace Exafuse commercial review or engineering approval.
 Open toolkit

Documented examples

Public context for LMD/DED decisions

Company-owned case details and RFQs belong to Exafuse. Manish Sharma Lab uses public examples only to explain monitoring, repairability, RFQ structure, and evidence planning.

Open evidence map Review source notes
CS15 Public Exafuse source

Duisburg Bridge Components

Decision it helps
Helps decide how much evidence is needed before large-part LMD monitoring data becomes a defensible engineering discussion.
Source note
Case-specific source available through Exafuse contact or current public pages.
What the example shows
Public context for large-part LMD scale, monitoring, validation, and inspection planning in one documented Exafuse case.
What still needs part-specific review
It does not prove that monitoring alone certifies quality or that this site can approve structural parts.
Related framework
LMD Quality Evidence Ladder
Useful decision pattern
Large-scale LMD requires evidence planning, monitoring context, and an inspection boundary.

Use the pattern as context only; feasibility still depends on the actual part, material, service conditions, inspection needs, and expert review.

Start a brief from this pattern Contact Exafuse
CS01 Public Exafuse source

Forging Hammer Repair

Decision it helps
Helps decide whether local wear repair should move into expert review instead of becoming an automatic repair claim.
Source note
Case-specific source available through Exafuse contact or current public pages.
What the example shows
Public context for local wear repair, material strategy, finishing, and release-evidence planning.
What still needs part-specific review
It does not prove every hammer or safety-critical part is repairable.
Related framework
LMD Repairability Index
Useful decision pattern
Local repair needs material, damage depth, machining route, and inspection plan.

Use the pattern as context only; feasibility still depends on the actual part, material, service conditions, inspection needs, and expert review.

Start a brief from this pattern Contact Exafuse

Latest field notes

Short technical notes for better LMD decisions

Focused explanations of the assumptions, evidence gaps, and review questions that often change an LMD/DED decision.

All lab notes RSS feed

Height Is Not Cosmetic

Bead height, build-up height, and machining allowance affect tolerance, inspection, and repair planning.

 Read note

A Camera Is Not a Certificate

A camera can show a process event, but inspection and acceptance evidence decide what the event means.

 Read note

RFQs Fail When They Hide Risk

A useful RFQ makes missing material, damage, geometry, service, tolerance, and inspection facts visible.

 Read note

Public technical layer

Use this site for public frameworks and AI-readable guidance.

The useful paths are thesis, tools, documented public context, agent files, and evidence-aware LMD/DED frameworks.

Public profile Guide for AI systems

Commercial boundary

For services, RFQs, and company claims, use Exafuse.

This personal site avoids confidential employer/customer data and does not replace expert review, inspection, certification, or formal approval.

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