Add Cognitive Manufacturing Environment #109
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A comprehensive manufacturing control environment with 30 tools for AI agents. Features: - Multi-machine production line simulation - Database persistence (PostgreSQL/SQLite support) - ML-powered analytics (predictive maintenance, anomaly detection, quality prediction) - Reinforcement learning optimization - Quality and inventory management - Energy monitoring and optimization - Scenario simulation and schedule optimization Architecture: - Physics-based simulator with temperature, vibration, and wear dynamics - Multi-objective reward system - 30 specialized tools across manufacturing operations - Complete OpenEnv compliance Author: Mohammad Mowas Dependencies: sqlalchemy, sentence-transformers, pandas, scikit-learn
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Pull Request Overview
This PR introduces a comprehensive Cognitive Manufacturing Environment for the OpenEnv framework. The environment simulates a production facility with realistic physics-based machine dynamics, providing 30 specialized tools for AI agents to manage manufacturing operations across quality control, inventory management, energy optimization, and predictive analytics.
Key Changes:
- Complete manufacturing simulation with single-machine and 4-machine production line modes
- 30 specialized tools organized in 5 phases (basic control, production line, data management, ML analytics, advanced management)
- Multi-objective reward system balancing safety, throughput, quality, cost, and sustainability
- Optional database persistence (PostgreSQL/SQLite), ML-powered analytics, and RL optimization
Reviewed Changes
Copilot reviewed 48 out of 48 changed files in this pull request and generated 24 comments.
Show a summary per file
| File | Description |
|---|---|
src/envs/cognitive_manufacturing/tools/*.py |
30 tool implementations for manufacturing control, data management, ML analytics, and advanced operations |
src/envs/cognitive_manufacturing/server/environment.py |
Core environment implementation with tool orchestration and reward computation |
src/envs/cognitive_manufacturing/server/simulator.py |
Physics-based machine simulator with temperature, vibration, and wear dynamics |
src/envs/cognitive_manufacturing/server/production_line.py |
4-machine production line with material flow and buffers |
src/envs/cognitive_manufacturing/server/database.py |
Database manager for persistent storage with SQLAlchemy |
src/envs/cognitive_manufacturing/server/ml_models.py |
ML service with predictive maintenance, anomaly detection, quality prediction, RL, and demand forecasting |
src/envs/cognitive_manufacturing/server/rewards.py |
Multi-objective reward calculation system |
src/envs/cognitive_manufacturing/models.py |
Data models for actions, observations, and state |
src/envs/cognitive_manufacturing/client.py |
HTTP client for remote environment access |
src/envs/cognitive_manufacturing/README.md |
Comprehensive documentation |
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| Returns: | ||
| List of sensor reading dicts | ||
| """ | ||
| session = self.Session() |
There was a problem hiding this comment.
Incorrect session creation method. The attribute is SessionLocal (defined in __init__ at line 163), not Session. This will cause an AttributeError when calling get_sensor_readings().
| session = self.Session() | |
| session = self.SessionLocal() |
| Returns: | ||
| List of production unit dicts | ||
| """ | ||
| session = self.Session() |
There was a problem hiding this comment.
Incorrect session creation method. The attribute is SessionLocal (defined in __init__ at line 163), not Session. This will cause an AttributeError when calling get_production_units().
| # Throughput rate (units/hour) - based on M4 output | ||
| if dt > 0: | ||
| m4_output = self.machines["M4"].units_produced | ||
| self.metrics.throughput_rate = m4_output / dt if dt > 0 else 0.0 |
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Test is always true, because of this condition.
| self.metrics.throughput_rate = m4_output / dt if dt > 0 else 0.0 | |
| self.metrics.throughput_rate = m4_output / dt |
| ) | ||
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| forecast_horizon = parameters.get("forecast_horizon", 168) | ||
| confidence_level = parameters.get("confidence_level", 0.95) |
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Variable confidence_level is not used.
| if self.anomaly_detector is not None: | ||
| # Use trained model | ||
| predictions = self.anomaly_detector.predict(X) | ||
| scores = self.anomaly_detector.score_samples(X) |
There was a problem hiding this comment.
Variable scores is not used.
| scores = self.anomaly_detector.score_samples(X) |
| try: | ||
| forecast = env.ml_service.forecast_demand(horizon=time_horizon) | ||
| forecast_demand = [f["demand"] for f in forecast] | ||
| except: |
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Except block directly handles BaseException.
| event_type="material_order", | ||
| data=order | ||
| ) | ||
| except Exception: |
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'except' clause does nothing but pass and there is no explanatory comment.
| "effects": effects, | ||
| } | ||
| ) | ||
| except Exception: |
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'except' clause does nothing but pass and there is no explanatory comment.
| event_type="inventory_update", | ||
| data=transaction | ||
| ) | ||
| except Exception: |
There was a problem hiding this comment.
'except' clause does nothing but pass and there is no explanatory comment.
| "affected_machines": affected_machines, | ||
| } | ||
| ) | ||
| except Exception: |
There was a problem hiding this comment.
'except' clause does nothing but pass and there is no explanatory comment.
2 participants
A comprehensive manufacturing control environment with 30 tools for AI agents.
Features:
Architecture:
Author: Mohammad Mowas
Dependencies: sqlalchemy, sentence-transformers, pandas, scikit-learn