Manufacturing Quality Control Assistant
Scenario Overview
A manufacturing company needs an AI system to help quality control engineers troubleshoot production issues and maintain quality standards. The system must be able to:
- Handle complex manufacturing terminology and processes
- Provide systematic troubleshooting approaches
- Learn from resolved cases to improve future responses
- Support different product lines and manufacturing processes
- Maintain quality standards and compliance requirements
User Journey
1. Quality Engineer Query
User: "Why are we getting surface defects on batch #12345 of aluminum parts?"
2. System Response Flow
Step 1: Manufacturing Query Understanding
# Manufacturing query expansion with process terminology
expanded_query = manufacturing_expander.expand(
query="Why are we getting surface defects on batch #12345 of aluminum parts?",
domain="manufacturing",
context={
"user_role": "quality_engineer",
"product_line": "aluminum_parts",
"issue_type": "surface_defects",
"batch_id": "12345"
}
)
# Results in:
# - "surface defects" → "surface imperfections", "finish issues", "cosmetic defects"
# - "aluminum parts" → "aluminum components", "aluminum products", "aluminum castings"
# - "batch #12345" → "production batch", "lot number", "manufacturing batch"
Step 2: Quality Control Document Retrieval
# Search quality control procedures and defect databases
retrieval_results = hybrid_retriever.retrieve(
query=expanded_query,
k=25, # More candidates for comprehensive troubleshooting
filters={
"product_line": "aluminum_parts",
"issue_type": "surface_defects",
"document_type": "quality_procedures",
"priority": "high"
}
)
Step 3: Manufacturing Reranking
# Rerank based on manufacturing relevance and case similarity
reranked_results = manufacturing_reranker.rerank(
query=expanded_query,
documents=retrieval_results,
context={
"product_line": "aluminum_parts",
"issue_type": "surface_defects",
"manufacturing_process": "machining"
},
top_k=10
)
Step 4: Quality Control Response Generation
# Generate systematic troubleshooting response
response = quality_generator.generate(
query=expanded_query,
context=reranked_results,
format="troubleshooting_guide"
)
3. System Response
# Surface Defects Troubleshooting Guide - Batch #12345
## Issue Analysis
**Product**: Aluminum parts (Batch #12345)
**Defect Type**: Surface defects
**Severity**: High (affecting product quality)
**Date Reported**: 2024-01-15
## Systematic Troubleshooting Approach
### 1. **Immediate Inspection**
- **Visual Inspection**: Check for specific defect patterns
- Scratches, gouges, or tool marks
- Discoloration or oxidation
- Rough surface finish
- Inconsistent texture
- **Measurement Verification**: Confirm dimensional accuracy
- Surface roughness measurements
- Dimensional tolerances
- Geometric specifications
### 2. **Process Parameter Review**
- **Machining Parameters**:
- Cutting speed: Check if within recommended range
- Feed rate: Verify optimal feed rate for aluminum
- Depth of cut: Ensure appropriate cutting depth
- Coolant flow: Verify adequate coolant supply
- **Tool Condition**:
- Tool wear: Check for excessive wear
- Tool geometry: Verify proper tool angles
- Tool material: Ensure correct tool grade
- Tool setup: Check tool alignment and positioning
### 3. **Material Analysis**
- **Aluminum Grade**: Verify correct alloy specification
- **Material Properties**: Check hardness and machinability
- **Surface Preparation**: Ensure proper cleaning and preparation
- **Contamination**: Check for foreign material or debris
### 4. **Equipment Inspection**
- **Machine Condition**: Check for machine vibration or misalignment
- **Fixture Setup**: Verify proper part clamping and positioning
- **Coolant System**: Ensure proper coolant concentration and flow
- **Environmental Factors**: Check temperature and humidity
## Common Causes and Solutions
### **Tool-Related Issues**
- **Problem**: Dull or damaged cutting tools
- **Solution**: Replace tools, check tool geometry
- **Prevention**: Implement tool life monitoring
### **Process Parameter Issues**
- **Problem**: Incorrect cutting speeds or feeds
- **Solution**: Adjust parameters per material specifications
- **Prevention**: Use process parameter monitoring
### **Material Issues**
- **Problem**: Incorrect aluminum grade or contamination
- **Solution**: Verify material specifications, clean material
- **Prevention**: Implement material inspection procedures
### **Equipment Issues**
- **Problem**: Machine vibration or misalignment
- **Solution**: Check machine condition, realign if necessary
- **Prevention**: Regular machine maintenance
## Recommended Actions
1. **Immediate**: Stop production of affected batch
2. **Inspection**: Complete detailed defect analysis
3. **Corrective Action**: Implement identified solutions
4. **Verification**: Test corrected process with small batch
5. **Prevention**: Update procedures and training
## Quality Control Documentation
- **Defect Report**: Document all findings and actions
- **Corrective Action Plan**: Implement systematic improvements
- **Preventive Measures**: Update procedures and training
- **Follow-up**: Monitor subsequent batches for recurrence
## Escalation Requirements
- **Immediate Escalation**: If defects affect safety or compliance
- **Management Notification**: For production delays or cost impact
- **Customer Communication**: If defects affect customer deliveries
- **Supplier Notification**: If defects are material-related
## Next Steps
1. **Complete Inspection**: Follow systematic troubleshooting approach
2. **Document Findings**: Record all observations and measurements
3. **Implement Solutions**: Apply identified corrective actions
4. **Verify Results**: Test corrected process
5. **Update Procedures**: Improve processes based on learnings
Implementation Code
1. Manufacturing Configuration
# config/manufacturing_config.py
from packages.rag import HybridRetriever, CrossEncoderReranker
from packages.agents import RAGAgentGraph, AgentConfig
from packages.rag.query_expansion import ManufacturingQueryExpander
from packages.observability import MetricsCollector, StructuredLogger
class ManufacturingConfig:
def __init__(self):
# Manufacturing query expansion
self.manufacturing_expander = ManufacturingQueryExpander(
domain="manufacturing",
manufacturing_terminology_file="data/manufacturing_terminology.json",
process_abbreviations_file="data/process_abbreviations.json"
)
# Hybrid retrieval with manufacturing focus
self.hybrid_retriever = HybridRetriever(
vector_retriever=VectorRetriever(
model_name="text-embedding-3-large",
vector_store=OpenSearchStore(
index_name="manufacturing_knowledge_base"
)
),
bm25_retriever=BM25Retriever(
index_path="data/manufacturing_bm25_index"
),
alpha=0.6 # Favor vector search for manufacturing terminology
)
# Manufacturing-specific reranking
self.manufacturing_reranker = ManufacturingReranker(
model_name="cross-encoder/ms-marco-MiniLM-L-6-v2",
case_similarity_weight=0.8,
process_relevance_weight=0.9,
solution_effectiveness_weight=0.7
)
# Agent configuration for manufacturing domain
self.agent_config = AgentConfig(
model_name="gpt-4-turbo-preview",
temperature=0.1,
max_steps=6,
retrieval_k=25,
rerank_k=10,
enable_web_search=False, # Disable web search for manufacturing accuracy
enable_escalation=True,
cost_limit=0.12
)
2. Manufacturing Knowledge Base
# data/manufacturing_knowledge_base.json
{
"documents": [
{
"id": "aluminum_surface_defects_001",
"title": "Aluminum Surface Defects Troubleshooting Guide",
"content": "Comprehensive guide to troubleshooting aluminum surface defects...",
"metadata": {
"product_line": "aluminum_parts",
"issue_type": "surface_defects",
"manufacturing_process": "machining",
"priority": "high",
"last_updated": "2024-01-10",
"source": "Quality Control Department",
"effectiveness_rating": 0.85
}
}
]
}
3. Manufacturing Agent Implementation
# agents/manufacturing_agent.py
import asyncio
from typing import Dict, Any, List
from packages.agents import RAGAgentGraph
from packages.observability import MetricsCollector, StructuredLogger
class ManufacturingAgent:
def __init__(self, config: ManufacturingConfig):
self.config = config
self.agent_graph = RAGAgentGraph(
config=config.agent_config,
tool_registry=config.tool_registry
)
self.metrics = config.metrics_collector
self.logger = StructuredLogger()
async def handle_quality_query(self, query: str, user_context: Dict[str, Any]) -> Dict[str, Any]:
"""Handle manufacturing quality query with full pipeline."""
start_time = time.time()
try:
# Step 1: Manufacturing query expansion
expanded_query = await self._expand_manufacturing_query(query, user_context)
# Step 2: Quality control document retrieval
retrieval_results = await self._retrieve_quality_documents(expanded_query, user_context)
# Step 3: Manufacturing reranking
reranked_results = await self._rerank_manufacturing_documents(expanded_query, retrieval_results, user_context)
# Step 4: Quality control response generation
response = await self.agent_graph.ainvoke({
"query": expanded_query,
"retrieved_docs": retrieval_results,
"reranked_docs": reranked_results,
"user_context": user_context,
"format": "troubleshooting_guide"
})
# Step 5: Quality insights
insights = await self._generate_quality_insights(response, user_context)
response["quality_insights"] = insights
# Step 6: Logging and metrics
await self._log_quality_interaction(query, response, user_context)
return response
except Exception as e:
self.logger.error(f"Quality query failed: {e}")
return await self._handle_quality_error(query, e, user_context)
async def _generate_quality_insights(self, response: Dict[str, Any], user_context: Dict[str, Any]) -> Dict[str, Any]:
"""Generate quality insights and recommendations."""
insights = {
"defect_patterns": await self._analyze_defect_patterns(response),
"process_improvements": await self._suggest_process_improvements(response, user_context),
"preventive_measures": await self._recommend_preventive_measures(response, user_context),
"training_needs": await self._identify_training_needs(response, user_context)
}
return insights
Features Demonstrated
1. Query Performance Analysis
- Optimizing for manufacturing terminology
- Process-specific query understanding
- Quality control context recognition
2. Deduplication
- Ensuring no conflicting quality procedures
- Consistent troubleshooting approaches
- Unified quality standards
3. Analytics & BI
- Quality trend analysis and pattern recognition
- Defect pattern identification
- Process improvement recommendations
4. Error Handling
- Graceful handling of incomplete quality data
- Fallback responses for unclear issues
- Escalation for complex quality problems
5. Rate Limiting
- Ensuring fair access during production issues
- Priority-based query processing
- Resource allocation for critical issues
6. Observability
- Comprehensive monitoring of quality assistance effectiveness
- Performance tracking and optimization
- Quality improvement metrics
Next Steps
- Deploy the manufacturing system with proper quality controls
- Ingest manufacturing knowledge base with proper metadata
- Configure quality analytics and trend monitoring
- Train quality staff on the new system
- Monitor quality effectiveness and continuous improvement
Related Stories
- IT Support Agent - Similar troubleshooting structure
- Medical Knowledge Assistant - Safety-focused implementation
- E-commerce Customer Support - Quality-focused customer service
Ready to implement? Start with the manufacturing knowledge base setup and work through each component step by step! 🏭