| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Advanced |
| Duration | 3 Weeks |
| Certification | NSTC e-Certification + e-Marksheet |
| Fee | ₹5499 / $59 |
| Tools | Additive Manufacturing AI in Manufacturing AI-Driven Innovation Autonomous Robots Cyber-Physical Systems |
About the AI in Manufacturing and Industry 4.0 Course
AI in Manufacturing and Industry 4.0 Course dives deep into Ai In Manufacturing And Industry 4.0. Gain comprehensive expertise through our structured curriculum and hands-on approach. This course bridges the gap between traditional industrial systems and intelligent automation, focusing on predictive maintenance, digital twins, and autonomous robotics.
Program Highlights
• Mentorship by industry experts and NSTC faculty
• Hands-on projects using Additive Manufacturing & Autonomous Robots
• Case studies on emerging AI innovations and smart factory trends
• Official e-Certification + e-Marksheet upon completion
Course Curriculum
Module 1 — Foundations of AI in Manufacturing and Industry 4.0
- Introduction to Industry 4.0 and smart manufacturing
- Evolution from conventional to intelligent production systems
- Role of AI in industrial transformation
- Core pillars: automation, connectivity, data, and intelligence
Module 2 — Industrial Data, IoT, and Connected Systems
- Industrial data sources (Sensors, PLCs, SCADA)
- IoT-enabled systems and data acquisition frameworks
- Data integration and communication frameworks
- Challenges in industrial data quality and real-time monitoring
Module 3 — AI and Machine Learning for Industrial Applications
- Fundamentals of AI, ML, and Deep Learning in industry
- Supervised, unsupervised, and reinforcement learning
- Classification, regression, and anomaly detection
- Model performance evaluation in manufacturing
Module 4 — Predictive Maintenance and Condition Monitoring
- Principles of predictive maintenance and asset health
- Sensor-driven fault detection and failure prediction
- AI models for maintenance planning and downtime reduction
- Applications in machinery and equipment systems
Module 5 — Computer Vision and Intelligent Quality Control
- Computer vision in inspection and defect detection
- Visual quality assurance and deep learning analysis
- Automated quality control in production environments
Module 6 — Process Optimization, Robotics, and Automation
- AI-driven process optimization and production planning
- Resource allocation, scheduling, and throughput enhancement
- Intelligent robotics and human-machine collaboration (Cobots)
Module 7 — Digital Twins, Edge AI, and Industrial Deployment
- Introduction to digital twins and edge computing
- Real-time industrial AI and cloud-edge integration
- Challenges: Scalability, latency, and cybersecurity
Module 8 — Applications, Case Studies, and Future Trends
- Case studies in predictive maintenance and quality inspection
- Smart factory examples and workflow optimization
- Future trends: Generative AI and sustainable manufacturing
Tools, Techniques, or Platforms Covered
Additive Manufacturing AI in Manufacturing AI-Driven Innovation Autonomous Robots Cyber-Physical Systems
Real-World Applications
- Zero-downtime maintenance using sensor-based failure prediction
- Automated visual inspection on high-speed production lines
- Optimizing supply chain logistics with AI-driven demand forecasting
- Digital twin modeling for factory floor layout optimization
- Collaborative robot (Cobot) integration for precision assembly
Who Should Attend & Prerequisites
- Designed for Professionals (Engineers, Production Managers, Industrial Designers)
- Designed for Students in Engineering or Technical fields
Certification
