Curriculum
- 5 Sections
- 33 Lessons
- 1 Day
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- Understanding Industry 4.0 in the Tyre Context5
- 1.1Evolution: Automation → Digitization → Integration → Intelligence
- 1.2Cyber-Physical Systems (CPS) and Connected Factory Models
- 1.3Data pipelines: Sensors → Edge → Cloud → Analytics
- 1.49 Pillars of Industry 4.0 (AI, IoT, Big Data, Robotics, AR/VR, Cloud, Additive Manufacturing, Cybersecurity, Simulation)
- 1.5Exercise: Digital Maturity self-assessment – participants map their plant or line on a 4.0 readiness matrix.
- Smart Sensors, IoT Networks & Data Acquisition7
- 2.1IoT sensor architectures – temperature, torque, pressure, vibration, cure cycle sensors
- 2.2Connectivity protocols – OPC UA, Modbus, MQTT
- 2.3Edge Analytics & Condition Monitoring
- 2.4Real-Time OEE dashboards and Data Lakes
- 2.5Predictive Maintenance using ML models
- 2.6Industry Examples: Apollo Tyres – IoT-driven curing process optimization Bridgestone – condition-based maintenance and data-driven fault prediction
- 2.7Exercise: Design an IoT architecture for a tyre mixing or curing line — define sensors, data flows, KPIs, and alert logic
- Automation, Robotics & AI Integration7
- 3.1Robotic material handling (AGVs, AMRs, vision-guided automation)
- 3.2Autonomous curing and inspection (“lights-out” operations)
- 3.3PLC-SCADA integration for closed-loop control
- 3.4AI/ML for fault prediction, batch consistency, and energy optimization
- 3.5MES & ERP integration for end-to-end traceability
- 3.6Industry Examples: Automated inspection systems; AI-assisted process control
- 3.7Interactive Element: Analyze process data to predict a potential defect trend using simplified AI model outputs.
- Digital Twins, Simulation & Augmented Reality6
- 4.1Concept of Digital Twins – virtual replicas of machines, lines, or entire plants
- 4.2Process simulation for mixing, curing, and material flow
- 4.3AR/VR applications for operator training and maintenance support
- 4.4Integration with PLM, MES, and SCADA data streams
- 4.5Global Examples: XYZ’s digital twin initiative for curing optimization; XYZ’s AR-based quality training
- 4.6Exercise: Build a digital twin storyboard for a tyre building machine showing inputs, analytics, and predicted outcomes
- Cybersecurity & Data Governance in Smart Factories8
- 5.1Industrial Cybersecurity (IEC 62443 framework)
- 5.2Network segmentation and OT/IT integration
- 5.3Data classification, encryption, and access control
- 5.4Role-based analytics dashboards and governance policies
- 5.5Exercise: Respond to a simulated incident — participants plan containment actions for a ransomware attack on an automated production line
- 5.6Activity: “Smart Factory Blueprint” simulation — teams design a digital roadmap for one process area using 3–4 Industry 4.0 tools
- 5.7Key quick-wins and technology priorities
- 5.8Pilot roadmap with measurable KPIs (OEE, defect reduction, energy savings)