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DI05000261 · Semester 5 · 5G scheme

Artificial Intelligence in Automation

Notes and solutions following the Gujarat Technological University (GTU) syllabus for DI05000261. Independent material — not issued by the university.

Automation & RoboticsInstrumentation & Control Engineering

Materials

Syllabus

The official syllabus this material follows.

  • Syllabus

    7 pages

    FreeRead

Complete Book

The whole syllabus, unit by unit, written for the exam.

Syllabus units

Unit 1: Introduction to AI in Automation
  • 1.1 Overview of Industry 4.0: Transition from traditional to AI-based Automation.
  • 1.2 Intelligent Agents: Structure of agents in industrial environments (PLC/SCADA integration); Perception-Action cycle in automated systems.
  • 1.3 AI Search Algorithms: Path planning for AGVs (Automated Guided Vehicles) and AMRs (Autonomous Mobile Robots);
Unit 2: Machine Learning for Industry
  • Linear and Logistic Regression for sensor calibration;
  • Support Vector Machines (SVM) for equipment state reduction.
Unit 3: Neural Networks and Deep Learning
  • 3.1 Artificial Neural Networks (ANN): Biological vs. Artificial Neuron; Multilayer Perceptrons (MLP) and Backpropagation.
  • 3.2 Deep Learning for Automation: Introduction to CNN (Convolutional Neural Networks) for visual inspection; Convolution, Pooling, and Padding layers.
  • 3.3 Time-Series Forecasting: RNN(Recurrent Neural Networks) and LSTM (Long Short-Term Memory) for predicting Remaining Useful Life (RUL) of machinery.
Unit 4: Fuzzy Logic and Expert Systems
  • 4.1 Fuzzy Logic Fundamentals: Fuzzy sets and Membership functions; Fuzzification and Defuzzification methods.
  • 4.2 Industrial Fuzzy Control: Design of Fuzzy PID controllers; Rule-based expert systems for industrial troubleshooting.
  • 4.3 Applications: Case studies in temperature and pressure control using fuzzy logic. AI Implementation and Case Studies
Unit 5: AI Implementation and Case Studies
  • 5.2 Industrial Vision Systems: Automated sorting and defect detection using OpenCV

About this course

In the era of Industry 4.0, traditional automation is evolving into "Intelligent Automation." Modern industries require systems that can predict failures, optimize energy consumption, and adapt to changing production needs without human intervention. This course is designed to equip diploma engineers with the skills to implement machine learning, fuzzy logic, and computer vision within industrial frameworks to improve efficiency, safety, and productivity.