Programme Parameters
Advanced Postgrad
6 Weeks (60 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Advanced manufacturing is not a single technology. It is a system-level shift that integrates automation, sensing, connectivity, and data analytics into production environments. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Advanced manufacturing is not a single technology. It is a system-level shift that integrates automation, sensing, connectivity, and data analytics into production environments.
📋 Course Objectives
1. Apply AI Enablement methods to authentic research and industry problems.
2. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• Tangible, reproducible AI Enablement work to show supervisors or employers.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
The System-Level Shift, Not a Single Tool
• Cyber-physical systems and the field → edge → cloud layering of a modern plant
• Industry 4.0 and 5.0 claims separated from the parts that are genuinely new
• Why most "smart factory" failures are integration failures, not technology gaps
Instrumenting the Shop Floor
• Sensors, PLCs and gateways, and the OPC UA and MQTT protocols that move their data
• Brownfield retrofits — extracting data from machines never designed to share it
• OT/IT convergence and the security exposure it creates on the production network
Turning Machine Data Into Decisions
• Time-series condition monitoring and predictive maintenance on vibration and current signals
• The cost asymmetry between a false alarm and a missed failure, and how it sets thresholds
• Baseline drift as equipment ages, and why a model tuned at commissioning decays
Modelling the Line Before Changing It
• Physics-based and data-driven twins, and what each can and cannot represent
• Synchronising a twin with live data and the latency budget that keeps it useful
• Simulation for line balancing and what-if analysis instead of trial on real production
Robots, Cobots and Flexible Cells
• Collaborative-robot operating modes and the safety requirements of ISO/TS 15066
• Speed-and-separation monitoring and power-and-force limiting in a real cell
• Allocating tasks between human and machine on capability and variability, not labour cost alone
One Use Case, End to End
• Scoping a predictive-maintenance or automated-inspection case with a measurable payback
• Tracing the path from a raw sensor signal to an operator-facing decision
• Presenting the business case honestly, including where the data or ROI does not yet support it
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