Advanced Manufacturing and Smart Factories: Hands-On Mini Project Series

A 4-day, mentor-led virtual series where you program CNC machining, design robot cells, build a digital twin with IoT data, and train a predictive-maintenance model using Fusion 360, RoboDK, and Python/Jupyter—ending with four portfolio-ready mini projects.

Enable participants to build an end-to-end smart-factory pipeline—CNC, robotics, digital twins, and predictive maintenance—using Fusion 360, RoboDK, Python/Jupyter, and IoT tools, culminating in four portfolio-ready mini projects.

• Map the end-to-end smart-factory workflow (CNC → Robotics → Twin → Analytics).

• Program & simulate CNC toolpaths in Fusion 360.

• Design & validate a robotic cell in RoboDK (reach, safety, cycle time).

• Build a basic digital twin with simulated IoT data and compute KPIs (OEE/throughput).

• Train and interpret a predictive-maintenance model in Python.

• Create a simple dashboard and package portfolio-ready artifacts.

📅 Day 1 – CNC Precision Machining Project

• Overview of CNC Machining in Smart Factories
• Introduction to G-Code Syntax and Workflow
• Setting up a simulation environment in Fusion 360
• Toolpath optimization and error correction techniques
• Live Demo: Programming a milling path for a precision part
• 🧩 Mini Project: CNC Precision Machining

📅 Day 2 – Robot Cell Design Project

• Basics of Industrial and Collaborative Robots (Cobots)
• Robotic cell design principles (layout, reach, safety zones)
• Introduction to RoboDK / Visual Components for simulation
• Live Demo: Building a robotic pick-and-place system
• 🧩 Mini Project: Robot Cell Design

📅 Day 3 – Digital Twin Simulation Project

• Introduction to Digital Twin Technology and Industry 4.0 Concepts
• Data integration with IoT sensors and SCADA interfaces
• Live Demo: Creating a virtual factory model and simulating live data inputs
• Visualizing production metrics through dashboards
• 🧩 Mini Project: Digital Twin Simulation

📅 Day 4 – Predictive Maintenance Analytics

• Basics of Predictive Maintenance and Industrial Data Analytics
• Overview of sensor data and machine logs
• Introduction to Python, Pandas, and Scikit-learn for analytics
• Live Demo: Building a simple ML model for CNC maintenance prediction
• 🧩 Mini Project: Predictive Maintenance Model

• UG/PG students in Mechanical, Manufacturing, Mechatronics, Industrial, EE, or CS.

• Early-career engineers/technicians in CNC, robotics, automation, or OT.

• Plant operations, maintenance & reliability engineers; production managers.

• Data/AI engineers (or aspirants) exploring Industry 4.0 use cases.

• Faculty/trainers and startup founders building smart-manufacturing solutions.

• Program CNC (G-code) and simulate in Fusion 360

• Design & validate a robotic cell in RoboDK

• Build a simple digital twin with IoT data sync

• Train a predictive-maintenance model in Python

• Create dashboards + four portfolio-ready artifacts

• Receive graded feedback and an e-certificate