What You’ll Learn: AI for Smart Factories
Move from scheduled maintenance and manual QA to self-optimizing production—using AI that learns from every cycle, weld, and millisecond.
Predictive Maintenance
Forecast bearing failure, tool wear, and hydraulic leaks using vibration, current, and thermal signatures.
AI-Powered Robotics
Enhance robot vision for defect detection, optimize path planning, and enable human-robot collaboration.
OEE Optimization
Diagnose availability, performance, and quality losses in real time using ML on SCADA/PLC data.
Production-Line Digital Twins
Simulate changeovers, bottleneck shifts, and maintenance impacts before implementing on the floor.
Who Should Enroll?
For professionals turning shop-floor data into competitive advantage.
- Plant & production managers
- Maintenance & reliability engineers
- Automation & controls specialists
- Operations & continuous improvement leads
- Manufacturing consultants & system integrators
Real-World Manufacturing Projects
CNC Spindle Failure Predictor
Build a 72-hour failure alert system using vibration + current data—reduce unplanned downtime by ≥35%.
Robotic Weld Quality Inspector
Train a vision model to detect porosity, undercut, and spatter in real-time—cut QA rework by 50%.
⚙️ Retrofit-Ready
OEE Dashboard for Legacy Press Line
Integrate IoT current sensors + PLC timestamps to track Availability, Performance, Quality—and prescribe fixes.
3-Week Industry 4.0 Syllabus
~24 hours • OPC UA/MTConnect dry-lab datasets • No-code dashboards • ROS2 simulations • 1:1 mentor
Week 1: Predictive Maintenance & Anomaly Detection
- Sensor types: vibration (FFT), thermal, acoustic emission, current signature
- Failure modes: bearing wear, imbalance, misalignment, tool breakage
- No-code modeling: feature extraction, threshold tuning, false-positive reduction
- Lab: Detect early-stage bearing faults in a motor dataset (ISO 10816-aligned)
Week 2: AI in Robotics & Automation
- Robot vision: object detection (YOLO), defect classification (CNN), pose estimation
- ROS2 + Gazebo simulation for path optimization & collision avoidance
- Human-robot collaboration: gesture recognition, adaptive speed control
- Lab: Simulate a pick-and-place robot sorting defective parts
Week 3: OEE Optimization & Digital Twin Integration
- OEE deep-dive: Availability (downtime root cause), Performance (speed loss), Quality (scrap/rework)
- Digital twin layers: physical, control, process, business
- Integration with MES/ERP: triggering work orders, spare part requests
- Capstone: Present your AI rollout plan for a production line—ROI, timeline, change management
NSTC‑Accredited Certificate
Recognized by VDMA, SME Smart Manufacturing Institute, and ISO/TC 184 (Automation) for Industry 4.0 competency.
Frequently Asked Questions
No coding or PLC expertise is required. We use no-code/low-code tools: drag-and-drop predictive maintenance dashboards (e.g., Seebo, Augury sim), visual robotics simulators (ROS2 + Gazebo), and pre-built anomaly detection models. You’ll focus on interpretation, integration, and actionable alerts—not model training.
Yes. Over 60% of our case studies use retrofit IoT sensors (vibration, current clamps, thermal cameras) on non-connected CNCs, presses, and conveyors. You’ll learn cost-effective strategies for brownfield AI deployment—including edge gateways, MTConnect adapters, and hybrid cloud-edge inference.
