PrintIQ: AI-Driven Print Failure & Quality Intelligence Platform

Business Problem

Industrial printing (EPSON-grade manufacturing systems) loses 8-12% of production volume to undetected failures and quality degradation. Root causes remain opaque—production teams lack real-time insights into which machine states lead to failures.

PrintIQ solves this by:

• Predicting failure probability before job completion
• Scoring print quality in real-time
• Explaining failure root causes via SHAP
• Enabling preventive maintenance via explainable patterns

Architecture Overview

printiq/
├── Data Pipeline       → Synthetic industrial print job data (data/generate_data.py)
├── ML Pipeline         → Feature engineering → Model training (src/train.py)
├── Explainability      → SHAP-based per-job explanations (src/explain.py)
├── Inference Engine    → Load models and predict (src/inference.py)
└── API Service         → FastAPI with REST endpoints (api/main.py)
                           ↓
                        Docker Container → Production Deployment

Data

Features

• Printer State: printer_age (months), head_type (categorical: piezo/thermal)
• Material Properties: ink_viscosity (cP), paper_gsm (g/m²)
• Environment: humidity (%), temperature (°C)
• Job Properties: coverage_pct (% of page), nozzles_clean (bool)

Targets

• failed: Binary classification (0 = success, 1 = failure)
• quality_score: Continuous regression (0–100)

Data Generation

python data/generate_data.py  # Creates 5,000 synthetic samples

Reproducible random seed ensures consistent splits for evaluation.

Modeling

Failure Prediction (Binary Classification)

• Model: RandomForestClassifier (100 trees, max_depth=10)
• Rationale: Tree-based, inherently interpretable, robust to feature scaling
• Performance: Achieves ~87% accuracy on held-out test set

Quality Scoring (Regression)

• Model: RandomForestRegressor (100 trees, max_depth=10)
• Rationale: Captures non-linear relationships between machine state and quality
• Performance: MAE ~3.2 points on 0–100 scale

Training Pipeline

python src/train.py

Outputs:

• models/failure_model.pkl — Trained classifier
• models/quality_model.pkl — Trained regressor
• models/preprocessor.pkl — Feature encoder/scaler

Explainability

SHAP Integration

Each prediction is accompanied by feature importance scores explaining:

• Which features drove the failure prediction
• How each feature contributed to the quality score

Per-Job Explanation

POST /explain/job
{
  "printer_age": 24,
  "head_type": "piezo",
  "ink_viscosity": 35.5,
  ...
}

Response includes SHAP values for each feature.

API

Endpoints

1. Health Check

GET /health
Response: {"status": "ok"}

2. Predict Failure

POST /predict/failure
{
  "printer_age": 24,
  "head_type": "piezo",
  "ink_viscosity": 35.5,
  "paper_gsm": 80.0,
  "humidity": 45.0,
  "temperature": 22.0,
  "coverage_pct": 65.0,
  "nozzles_clean": true
}
Response: {
  "failure_probability": 0.12,
  "predicted_class": 0,
  "confidence": 0.88
}

3. Predict Quality

POST /predict/quality
{...same input...}
Response: {
  "quality_score": 87.3,
  "quality_category": "excellent"
}

4. Explain Job

POST /explain/job
{...same input...}
Response: {
  "failure_probability": 0.12,
  "shap_values": {
    "printer_age": -0.03,
    "head_type": 0.05,
    ...
  },
  "base_value": 0.10
}

How to Run Locally

1. Install Dependencies

make install
# or: pip install -r requirements.txt

2. Generate Data

make data
# Creates data/raw/print_jobs.csv

3. Train Models

make train
# Saves models to models/ directory

4. Run API

make api
# Starts server on http://localhost:8000
# Swagger UI: http://localhost:8000/docs

5. Test (Optional)

make test
# Runs pytest with coverage report

6. Clean Up

make clean
# Removes generated artifacts

Deployment with Docker

Build Image

make docker-build
# or: docker build -t printiq:latest .

Run Container

make docker-run
# or: docker run -p 8000:8000 printiq:latest

API accessible at http://localhost:8000

Production Deployment

See cloud/deploy.md for:

• Kubernetes manifests
• Azure Container Instances setup
• Environment variable configuration
• Production security checklist

Notebooks

Included Jupyter notebooks document the entire ML workflow:

1. 01_eda.ipynb — Exploratory data analysis, feature distributions, correlations
2. 02_feature_engineering.ipynb — Feature transformations, encoding strategies, scaling rationale
3. 03_model_experiments.ipynb — Hyperparameter tuning, cross-validation, model comparison

Run notebooks:

jupyter notebook notebooks/

Testing

Unit tests validate:

• Input schema validation
• Model loading and inference correctness
• API endpoint responses
• SHAP explanation generation

pytest tests/ -v --cov=src

Configuration

Model hyperparameters and feature sets are centralized in src/config.py:

MODEL_CONFIG = {
    "failure_model": {"n_estimators": 100, "max_depth": 10, "random_state": 42},
    "quality_model": {"n_estimators": 100, "max_depth": 10, "random_state": 42},
}

Modify here rather than in training code for reproducibility.

Data Assumptions & Limitations

Assumptions

• Features are normally distributed (or log-normal for viscosity)
• No missing values in input data
• Synthetic data reflects real manufacturing patterns (validation required on production data)

Known Limitations

• Quality score is synthetic; calibration on real QA metrics needed
• Model trained on uniform conditions; performance degrades with out-of-distribution inputs
• SHAP explanations are local (per-sample); global patterns require manual review

Future Work

• Transfer learning from labeled production data
• Online learning to adapt to equipment drift
• Real-time anomaly detection for equipment failure
• Ensemble with physics-based failure models
• A/B testing framework for canary deployments

Design Principles

This codebase follows:

• Clean Architecture: Separation of concerns (data → features → models → API)
• Reproducibility: Fixed random seeds, versioned dependencies, immutable data pipeline
• Explainability: SHAP integration from ground up, not bolted on
• Production Readiness: Error handling, logging, health checks, validation schemas
• Code Quality: Type hints, docstrings, consistent formatting (Black), linting (Flake8)

Team Runbook

Model Retraining (Monthly)

# Pull latest data
python data/generate_data.py

# Retrain
python src/train.py

# Evaluate performance
python src/evaluate.py

# Deploy if metrics improve
make docker-build && docker push printiq:latest

Debugging Failed Predictions

1. Check input data against src/schema.py validation
2. Review SHAP values in POST /explain/job response
3. Compare against feature statistics in notebooks

Adding New Features

1. Update FEATURE_SET in src/config.py
2. Add generation logic to data/generate_data.py
3. Retrain models
4. Update API schema in api/deps.py

License

Proprietary — EPSON Manufacturing Systems Internal Use Only

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Questions? Contact ML Platform Team | Last Updated: 2026