
Advanced Manufacturing and Smart Factories: Hands-On Mini Project Series
Design. Simulate. Predict. Your smart factory starts here.
Skills you will gain:
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.
Aim: 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.
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Map the end-to-end smart-factory workflow (CNC → Robotics → Twin → Analytics).
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Program & simulate CNC toolpaths in Fusion 360.
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Design & validate a robotic cell in RoboDK (reach, safety, cycle time).
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Build a basic digital twin with simulated IoT data and compute KPIs (OEE/throughput).
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Train and interpret a predictive-maintenance model in Python.
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Create a simple dashboard and package portfolio-ready artifacts.
What you will learn?
📅 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
Intended For :
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UG/PG students in Mechanical, Manufacturing, Mechatronics, Industrial, EE, or CS.
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Early-career engineers/technicians in CNC, robotics, automation, or OT.
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Plant operations, maintenance & reliability engineers; production managers.
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Data/AI engineers (or aspirants) exploring Industry 4.0 use cases.
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Faculty/trainers and startup founders building smart-manufacturing solutions.
Career Supporting Skills

