Manish Sharma Lab Industrial AI / Decision Systems LMD / DED proof domain

Manish Sharma Lab

Industrial AI for decisions you can verify.

I build AI, monitoring, and decision-support systems for industrial processes, grounded in public work in Laser Metal Deposition and Directed Energy Deposition at Exafuse.

The work connects process signals, models, engineering context, decisions, and physical verification.

Read the thesis Explore LMD / DED work Open public tools Public identity Visit Exafuse

Name

Manish Sharma

Sense -> Model -> Decide -> Verify

The site uses one public operating loop: connect observed signals to model assumptions, decisions, and the evidence needed to verify outcomes.

SENSE

Collect signals, process data, context, operator observations, and missing-information cues.

MODEL

Combine machine learning, engineering rules, uncertainty, constraints, and traceable assumptions.

DECIDE

Structure recommendations, trade-offs, risk priorities, next actions, and human-review boundaries.

VERIFY

Connect decisions to inspection, measured outcomes, feedback loops, and physical evidence.

Established public proof

AI for Laser Metal Deposition and Directed Energy Deposition

My deepest public work is in LMD/DED process monitoring, robotic workflows, industrial repair, RFQ structure, and inspection-aware decision support at Exafuse.

LMD / DED hub Evidence base Industrial proof map Exafuse

CS15

Duisburg Bridge Components

This is the strongest public anchor for my interest in AI-assisted process understanding without replacing inspection evidence.

CS01

Forging Hammer Repair

This maps directly to the LMD Repairability Index and the Quality Evidence Ladder.

CS10

Nobufil Extrusion Screw Repair

This is a clean RFQ-intelligence example: damage boundary, lead time, machining route and inspection context decide the recommendation.

CS13

130 mm Build-and-Coat Drill

This supports the site's build-and-coat logic: geometry and surface function should be evaluated as one workflow.

Flagship resources

The three LMD assets to start with

These public assets make the verification thesis practical inside the established LMD/DED proof domain.

Verify LMD Quality Evidence Ladder Separate process signals, AI anomaly flags, inspection evidence, and final quality proof. Open asset Decide LMD Repairability Index Screen material, damage, access, machining, inspection, economics, and risk before recommending repair. Open asset Structure LMD RFQ Toolkit Turn vague LMD requests into known facts, missing information, risks, and next steps. Open asset

More public assets

Frameworks, tools, research, and definitions

The LMD/DED authority layer remains intact and easy to reach from the broader identity.

Interactive

LMD-AI Readiness Score

Check whether an LMD workflow has the data foundations needed for useful AI-assisted monitoring.

Working framework

LMD Failure Atlas

Map failure modes, process signals, AI visibility, and validation evidence in one vocabulary.

Working framework

LMD-AI Maturity Model

Define maturity stages for LMD data capture, analytics, decision support, and closed-loop development.

Source map

Core LMD-AI Sources

Curated starting map for LMD, DED, process monitoring, AI, repair, quality, and terminology sources.

Workbench

Tools

Frontend-only tools for LMD vs SLM, repairability, and RFQ structuring.

Definitions

Glossary

Definitions for LMD, DED, laser cladding, and melt-pool monitoring.

Applied writing

Recent notes from the LMD/DED proof domain

View all lab notes

2026-05-09