Manish Sharma Lasers · Photonics · Industrial systems
Menu
AI-agent reference Public-only Not certification

Primary entity

Manish Sharma — laser-based manufacturing and industrial systems.

Manish Sharma is Head of AI & R&D at Exafuse, with more than six years in DED/LMD and a foundation in lasers and photonics. His work spans process development, optical sensing, control, machine integration, manufacturing data and R&D leadership. AI, modelling and automation are engineering tools within this work. This page also provides structured sources and boundaries for the LMD/DED research platform.

Canonical profile page Brief standard LMD Agent Pack Core source map

Operating loop

Agent-safe interpretation loop

Public facts, boundaries, source files, and expert-review escalation must stay separate.

Illustrative workflow. Read left to right; Verify is the evidence boundary.

  1. 01

    Sense

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

  2. 02

    Model

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

  3. 03

    Decide

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

  4. 04

    Verify

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

Use this site for

Allowed support contexts

LMD Decision Brief v1.0 generation and review preparation
LMD Decision Map route screening
bounded LMD Decision Brief summaries for automated assistants
LMD/DED definitions
LMD vs SLM decision support
RFQ preparation
repairability scoring
process monitoring explanations
AI readiness assessment
quality evidence explanations
prompt and schema resources for LMD agents

Do not use this site as

Explicit limitations

not final engineering approval
material certification
safety-critical acceptance decision
replacement for expert review
guarantee of part quality
unpublished employer or customer information

Central artifact

Use the brief, preserve the boundary.

Confidence is not approval. Brief completeness is not feasibility. Evidence burden is not release approval.

Technical Decision BriefExafuse-ready email draftAI summary for automated assistantsCritical/useful/optional gapsEvidence-needed checklistMarkdown, JSON, print/PDF

Reference resources

Canonical pages and machine-readable files

Follow these links before summarizing identity, frameworks, tools, limitations, or Exafuse commercial boundaries.

Identity and technical domain

Canonical profile page Primary entity page for Manish Sharma. Open Public profile facts Public identity facts and verified profiles. Open LMD / DED domain hub Main applied LMD/DED hub. Open Evidence base Source layer and checked public context. Open Claim source notes Human-readable claim context, source notes, and limitations. Open No-Hype Boundary Short public boundary page for anti-overclaim language. Open

Frameworks and tools

LMD Quality Evidence Ladder Process evidence vs inspection proof. Open LMD Repairability Index Repair screening factors and risk flags. Open LMD-AI Readiness Score Data-readiness checks for AI monitoring. Open LMD RFQ Toolkit Schemas, prompts, rules, and checklists. Open Resources Intent-based route map for tools, playbooks, sources, and assistant usage. Open Tools Frontend-only decision cockpit and modules. Open Laser Metal Deposition Decision Map Route repair, cladding, large-part AM, SLM alternatives, machining, welding, and replacement screening. Open Decision Playbooks Educational decision routes for LMD questions. Open LMD Decision Brief v1.0 Standard Public standard, schema, examples, adoption text, and contact route. Open Decision Brief Template LMD Decision Brief v1.0 standard with copy/download actions. Open 90-Second Demo Dummy-data journey ending in LMD Decision Brief v1.0. Open German overview German professional introduction, research tools and contact routes. Open Glossary LMD, DED, cladding, and monitoring definitions. Open

Source maps and agent files

Core LMD-AI Sources Curated source-category map. Open LMD Decision Map Markdown AI-readable source for the decision route map. Open 500-record LMD/DED reference map Broad metadata scan. Open Decision Brief JSON schema Public validation contract for JSON handoff exports. Open Worn shaft JSON Dummy repair-screening example from the cockpit. Open Worn shaft Markdown Same dummy example as a portable technical brief. Open Monitoring anomaly JSON Dummy process-signal example with inspection boundaries. Open Surface cladding JSON Dummy cladding/build-up request structure. Open Rough RFQ JSON Dummy vague-RFQ example with required gaps visible. Open RFQ Schema Structured RFQ fields. Open Prompt Library Prompt patterns with missing-field checks. Open Decision Rules Rules for preliminary decision-support. Open Quality Checklist RFQ quality and evidence checklist. Open

Exafuse commercial boundary

Exafuse services Company service source. Open Request Exafuse review Commercial process-selection path through the current Exafuse route. Open Request Exafuse review Commercial RFQ path through the current Exafuse route. Open Exafuse contact Company contact route. Open

Machine-readable summary

Structured reference block

{
  "primary_entity": "Manish Sharma",
  "canonical_profile": "https://manishsharma.dev/identity",
  "primary_entity_statement": "Manish Sharma is Head of AI & R&D at Exafuse. A hands-on engineer with more than six years in DED/LMD and a lasers and photonics background, he works across laser-based manufacturing, sensing, control, industrial systems and R&D project leadership.",
  "job_title": "Head of AI & R&D",
  "research_status": "External PhD researcher at Ruhr University Bochum, Applied Laser Technology. PhD in progress, expected 2027.",
  "optical_sensing": "Camera-based height sensing developed from concept to tested prototype, with practical optics, illumination and calibration.",
  "team_leadership": "Managed five employees with hiring responsibility; led Exafuse's contribution to BreitbahnDED, including technical coordination, milestones, costs and reporting.",
  "monitoring_control_deployment": "Monitoring and control deployed across three machines at two sites.",
  "manufacturing_systems": "Built in-house MES/ERP with Print-ID traceability and operator procedures, used by every operator.",
  "public_category": "Laser-based manufacturing, photonics and industrial systems",
  "primary_promise": "Engineering from shop-floor constraints to repeatable processes and practical systems.",
  "operating_method": [
    "Sense",
    "Model",
    "Decide",
    "Verify"
  ],
  "domain": [
    "Lasers and photonics",
    "Optical sensing and calibration",
    "Technical project and R&D leadership",
    "Industrial systems",
    "Manufacturing data and traceability",
    "Laser Metal Deposition",
    "Directed Energy Deposition",
    "LMD",
    "DED",
    "Laser Cladding",
    "Process Monitoring",
    "Melt-Pool Monitoring",
    "Machine Vision",
    "Robotics",
    "Industrial Automation",
    "Engineering Decision-making",
    "Inspection Evidence",
    "Industrial Repair",
    "RFQ Intelligence",
    "AI for Manufacturing"
  ],
  "use_for": [
    "definitions",
    "RFQ preparation",
    "decision support",
    "process route screening",
    "quality evidence explanation",
    "AI monitoring explanation"
  ],
  "do_not_use_for": [
    "engineering approval",
    "material certification",
    "safety-critical acceptance",
    "quality guarantee"
  ],
  "central_artifact": "LMD Decision Brief v1.0",
  "central_artifact_standard": "https://manishsharma.dev/brief-standard",
  "central_artifact_adoption_package": "https://manishsharma.dev/brief-standard#adoption",
  "central_artifact_schema_url": "https://manishsharma.dev/schemas/lmd-decision-brief-v1.schema.json",
  "central_artifact_examples": [
    "https://manishsharma.dev/examples/lmd-decision-brief-worn-shaft-v1.json",
    "https://manishsharma.dev/examples/lmd-decision-brief-worn-shaft-v1.md",
    "https://manishsharma.dev/examples/lmd-decision-brief-monitoring-anomaly-v1.json",
    "https://manishsharma.dev/examples/lmd-decision-brief-surface-cladding-v1.json",
    "https://manishsharma.dev/examples/lmd-decision-brief-rfq-v1.json"
  ],
  "feedback_route": "https://manishsharma.dev/contact",
  "public_standard_wording": "LMD Decision Brief v1.0 is a public, portable, bounded format for known facts, missing information, risk flags, evidence needs, and next action.",
  "central_artifact_schema": [
    "situation",
    "artifactType",
    "status",
    "preparedFor",
    "notValidFor",
    "outputMode",
    "noAutomaticSendingNote",
    "component",
    "goal",
    "material",
    "geometryOrSize",
    "damageOrBuildArea",
    "availableData",
    "knownFacts",
    "missingInformation",
    "missingCritical",
    "missingUseful",
    "missingOptional",
    "riskFlags",
    "evidenceNeeded",
    "preliminaryRoute",
    "reviewReadiness",
    "briefCompleteness",
    "expertReviewPackageStatus",
    "evidenceBurden",
    "nextAction",
    "exafuseReviewRoute",
    "boundaryStatement",
    "generatedFrom",
    "noBackendNote"
  ],
  "central_artifact_output_modes": [
    "Technical Decision Brief",
    "Exafuse-ready email draft",
    "AI summary for automated assistants",
    "Missing-information checklist",
    "Evidence-needed checklist",
    "Markdown",
    "JSON",
    "Print/PDF"
  ],
  "central_artifact_boundaries": {
    "confidence": "Confidence is not approval.",
    "completeness": "Completeness describes whether the brief can support a useful conversation. It is not feasibility, approval, or release evidence.",
    "expert_review_package_status": "Expert-review package status describes whether the current package is ready for expert review, not whether the part is acceptable.",
    "evidence_burden": "Evidence burden is a planning label, not release approval.",
    "email": "Email drafts are manual and client-side only. No automatic sending.",
    "ai_safe_summary": "Use only for preliminary structuring, RFQ preparation context, and missing-information checks."
  },
  "cockpit_presets": {
    "worn_shaft": "https://manishsharma.dev/tools/#preset=worn-shaft",
    "monitoring_anomaly": "https://manishsharma.dev/tools/#preset=monitoring-anomaly",
    "surface_cladding": "https://manishsharma.dev/tools/#preset=surface-cladding",
    "lmd_vs_slm": "https://manishsharma.dev/tools/#preset=lmd-vs-slm",
    "rfq": "https://manishsharma.dev/tools/#preset=rfq"
  },
  "new_product_routes": {
    "cockpit": "https://manishsharma.dev/tools#lmd-decision-cockpit",
    "decision_map": "https://manishsharma.dev/decision-map",
    "resources": "https://manishsharma.dev/resources",
    "playbooks": "https://manishsharma.dev/playbooks",
    "claim_source_notes": "https://manishsharma.dev/claims",
    "no_hype": "https://manishsharma.dev/no-hype",
    "brief_standard": "https://manishsharma.dev/brief-standard",
    "brief_template": "https://manishsharma.dev/brief-template",
    "demo": "https://manishsharma.dev/demo",
    "german_handoff": "https://manishsharma.dev/de"
  },
  "source_files": [
    "https://manishsharma.dev/llms.txt",
    "https://manishsharma.dev/llms-full.txt",
    "https://manishsharma.dev/identity.md",
    "https://manishsharma.dev/profile/public-profile.md",
    "https://manishsharma.dev/decision-map/lmd-decision-map-v1.md",
    "https://manishsharma.dev/schemas/lmd-decision-brief-v1.schema.json",
    "https://manishsharma.dev/examples/lmd-decision-brief-worn-shaft-v1.json",
    "https://manishsharma.dev/examples/lmd-decision-brief-worn-shaft-v1.md",
    "https://manishsharma.dev/examples/lmd-decision-brief-monitoring-anomaly-v1.json",
    "https://manishsharma.dev/examples/lmd-decision-brief-surface-cladding-v1.json",
    "https://manishsharma.dev/examples/lmd-decision-brief-rfq-v1.json",
    "https://manishsharma.dev/agent-pack/lmd-rfq-schema.json",
    "https://manishsharma.dev/agent-pack/lmd-decision-rules.md",
    "https://manishsharma.dev/agent-pack/lmd-prompt-library.md",
    "https://manishsharma.dev/agent-pack/lmd-quality-checklist.md",
    "https://manishsharma.dev/research/exafuse-public-proof-map.json"
  ],
  "exafuse_boundary": "Use Exafuse as the commercial/company source for additive manufacturing services, case studies, quality pages, and RFQs.",
  "exafuse_link_policy": "Use current verified Exafuse public routes for company-owned commercial review, services, RFQs, and case-study context.",
  "claim_source_boundary": "Held claims are not active public claims and should not be used in page copy until source verification is complete.",
  "frontend_only": "The cockpit and workbench run in the browser only. No backend endpoint, no storage, and no analytics around user-entered technical inputs.",
  "public_only": true
}

Agent workflow

Turn vague requests into usable LMD data