Explainable Manufacturing Defect Investigation Using Knowledge Graphs, Neo4j GDS, MCP, and Agentic AI

A manufacturing investigation platform that combines Computer Vision outputs, Knowledge Graphs, Graph Data Science (GDS), Model Context Protocol (MCP), and Local LLM Agents to support defect analysis and engineering investigations.

The system extends traditional defect detection by providing contextual information, graph analytics, graph-based machine learning, and natural language access to manufacturing knowledge.

Overview

Traditional defect detection systems answer:

Is the product defective?

Quality engineers typically require additional context:

• Which machine produced it?

• Which supplier provided the materials?

• Have similar defects occurred before?

• Are there recurring defect patterns?

• What actions should be investigated?

This project combines manufacturing telemetry, traceability data, graph analytics, and LLM reasoning to generate explainable investigation reports.

System Architecture

AI4I Telemetry Dataset
            +
MVTec Transistor Dataset
            +
Synthetic Traceability Data
            ↓
Data Integration Pipeline
            ↓
Manufacturing Dataset
            ↓
Neo4j Knowledge Graph
            ↓
Neo4j Graph Data Science
            ↓
FastAPI + MCP Server
            ↓
LLM Investigation Agent
            ↓
Investigation Report

Core Components

Knowledge Graph

Manufacturing entities are represented as graph nodes.

Node Types

• ProductCase

• Machine

• Supplier

• Batch

• MaterialLot

• Operator

• Shift

• ProductionLine

• Defect

• FailureType

• Document

Relationships

(ProductCase)-[:PRODUCED_BY]->(Machine)
(ProductCase)-[:SUPPLIED_BY]->(Supplier)
(ProductCase)-[:BELONGS_TO_BATCH]->(Batch)
(ProductCase)-[:HAS_DEFECT]->(Defect)
(ProductCase)-[:EXHIBITS_FAILURE]->(FailureType)
(ProductCase)-[:WORKED_IN_SHIFT]->(Shift)
(ProductCase)-[:MENTIONED_IN]->(Document)

Graph Analytics

Implemented using Neo4j Graph Data Science.

Related MCP server: GrACE-MCP

PageRank Centrality

Identifies machines that are most influential within the manufacturing network.

Endpoint:

GET /analytics/centrality

Example Insight:

Machine M5 exhibits the highest centrality score.

Louvain Community Detection

Groups machines and suppliers with similar manufacturing behavior.

Endpoint:

GET /analytics/communities

Example Insight:

Machines and suppliers within the same community
share similar defect patterns.

Graph Machine Learning

Node Classification

Uses Neo4j GDS Node Classification Pipeline.

Workflow:

ProductCase Nodes
        ↓
FastRP Embeddings
        ↓
Logistic Regression
        ↓
Defect Classification

Training Endpoint:

POST /ml/train

Prediction Endpoint:

GET /ml/predict/{uid}

FastRP Embeddings

Generates graph embeddings for ProductCase nodes.

Endpoint:

POST /ml/embeddings

Embedding Size:

64 dimensions

Stored as:

pc.embedding

Similar Case Retrieval

Uses cosine similarity between FastRP embeddings.

Example:

MATCH (target:ProductCase)
MATCH (other:ProductCase)

WITH target, other,
gds.similarity.cosine(
    target.embedding,
    other.embedding
) AS similarity

Endpoint:

GET /ml/similar/{uid}

Purpose:

• Historical defect comparison

• Similar case retrieval

• Context-aware investigations

Text-to-Cypher

Natural language queries are converted into Cypher using a local LLM.

Example:

Which machines have the highest defect rates?

Generated Query:

MATCH ...
RETURN ...

Endpoint:

POST /text-to-cypher

Graph Builder

Documents can be ingested into the Knowledge Graph.

Supported Examples:

• Maintenance logs

• SOP documents

• Engineering reports

Endpoint:

POST /ingest-document

Generated Knowledge:

Machine
    ↓
MENTIONED_IN
    ↓
Document

MCP Investigation Agent

The MCP server exposes graph operations as tools.

Examples:

• get_case_context

• find_similar_cases

• calculate_machine_centrality

• detect_defect_communities

• text_to_cypher

The LLM agent invokes these tools to collect evidence and generate investigation reports.

Example Investigation Workflow

UID000001
      ↓
Retrieve Context
      ↓
Find Similar Cases
      ↓
Analyze Graph Patterns
      ↓
Collect Evidence
      ↓
Generate Investigation Report

Example Report Sections:

• Case Summary

• Evidence

• Hypothesis

• Recommendations

REST API

Data

POST /sync

Investigation

POST /investigate/{uid}

Graph Analytics

GET /analytics/centrality

GET /analytics/communities

Graph Machine Learning

POST /ml/train

POST /ml/embeddings

GET /ml/predict/{uid}

GET /ml/similar/{uid}

Knowledge Graph Querying

POST /text-to-cypher

Graph Builder

POST /ingest-document

Technology Stack

• FastAPI

• Neo4j

• Neo4j Graph Data Science

• MCP

• Ollama

• Qwen 2.5

• Python

• Docker

• PatchCore

• OpenCV

Project Goal

This project demonstrates how Knowledge Graphs, Graph Analytics, Graph Machine Learning, MCP, and LLM Agents can be combined to transform manufacturing defect detection into explainable manufacturing investigations.