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Syllabus

Syllabus For

SCREWDRIVERS TECHNICAL CLUB (STC)

TECHNICAL CLUB

Sr.No

Course Contents

 

 Duration
1 Introduction to Vehicle Manufacturing
– Definition and Scope of Vehicle Manufacturing
– Types of Vehicles (2W, 3W, 4W, CVs, etc.)
– History and Evolution
– Basic Manufacturing Flow: Concept to Assembly Line
– Importance of Standardization and Regulations
– Career Paths in Manufacturing		  6 hrs
2 Automobile Design Fundamentals
– Vehicle Layouts and Classifications
– Chassis and Body Integration
– Ergonomics in Design
– Concept to CAD Model
– Styling vs. Engineering Constraints– Manual and Autonomous Mode of Operation

– Sensors in a Drone and Requirements

– Mission Definition and Planning

 6 hrs
3 Materials in Vehicle Manufacturing
– Overview of Automotive Materials (Steel, Aluminium, Composites, Plastics)
– Properties and Selection Criteria
– Corrosion Resistance and Surface Coating
– Sustainability in Materials		 5 hrs
4 Manufacturing Processes in Automobile Industry
– Casting, Forging, Machining, Welding
– Plastic Moulding and Sheet Metal Forming
– Joining Techniques: Bolting, Riveting, Spot Welding
– Introduction to Additive Manufacturing		 8 hrs
5 Powertrain Assembly and Testing
– Overview of IC Engine, Transmission, and Drivetrain
– Manufacturing and Assembly Steps
– Balancing and Tolerances
– Cold/Hot Testing Procedures		 6 hrs
6 Chassis and Suspension Manufacturing
– Types of Chassis
– Suspension System Components
– Assembly Techniques
– Frame Alignment and Testing		 5 hrs
7 Electricals and Electronics Integration
– Wiring Harness Design and Routing
– Lighting and Instrumentation
– ECU and Sensor Fitting
– Battery Mounting and Safety		 5 hrs
8 Vehicle Assembly Line Practices
– Layout of Assembly Line
– Conveyor Systems and Automation
– Quality Checks at Each Station
– Role of Humans vs. Robots		 6 hrs
9 Quality Assurance and Testing
– In-Process Quality Control
– Final Vehicle Inspection Checklist
– NVH and Road Testing
– Certifications and Compliance		 5 hrs

 

LIST OF PRACTICALS:

  1. Identification and study of vehicle components.
  2. Chassis and suspension mock assembly.
  3. Engine and transmission mounting practice.
  4. Material testing demo: hardness, tensile, corrosion.
  5. Spot welding and sheet metal fabrication practice.
  6. Wiring harness and battery fitment demo.
  7. CAD model to prototype demonstration.
  8. Quality check exercise using gauges and tools.

Course Outcomes:

After successful completion of the course, students will be able to:

  1. Explain the fundamental concepts and applications of Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI.
  2. Develop Python programs and utilize essential libraries for AI and ML
  3. Perform data preprocessing, analysis, and visualization using appropriate tools and
  4. Build and evaluate beginner-level Machine Learning and Deep Learning
  5. Apply Conversational AI, Prompt Engineering, and AI tools effectively for real-world

Design simple AI-based solutions and mini projects for multidisciplinary real-world applications.

Course Contents

Unit No I

Introduction to AI & Python Foundations

06 Hours

Introduction to Artificial Intelligence, AI vs Machine Learning vs Deep Learning, Applications of AI, Introduction to Python Programming, Variables and Data Types, Operators and Expressions, Conditional Statements and Loops, Functions in Python, Introduction to NumPy, Pandas and Matplotlib, Basic Python Programs, Data Visualization and Introduction to Google Colab.

Unit No II

Data Handling & Exploratory Data Analysis

06 Hours

Introduction to Data and Datasets, Structured and Unstructured Data, Data Collection and Dataset Handling, Data Cleaning and Preprocessing, Handling Missing Values, Introduction to Pandas DataFrames, Data Visualization using Matplotlib, Line Graph, Bar Chart, Histogram and Scatter Plot, Basic Statistical Concepts including Mean, Median, Mode and Correlation, Exploratory Data Analysis (EDA) using Real-world Datasets.

Unit No III

Machine Learning Fundamentals

06 Hours

Introduction to Machine Learning, Machine Learning Workflow, Types of Machine Learning, Supervised, Unsupervised and Reinforcement Learning, Linear Regression, Logistic Regression, K-Nearest Neighbors (KNN), Model Training and Testing, Accuracy and Performance Evaluation, Introduction to Scikit-learn Library, Industry Applications of Machine Learning including Predictive Maintenance, Safety Monitoring,

Production Forecasting, Load Forecasting and Disease Prediction Models.

Unit No IV

Deep Learning & Computer Vision Basics

06 Hours

Introduction to Deep Learning, Artificial Neurons and Neural Networks, Deep Learning Applications, Introduction to TensorFlow and Keras, Basics of Computer Vision, Image Processing Fundamentals, Introduction to Convolutional Neural Networks (CNN), Image Classification Concepts, Object Detection

Concepts, Hands-on Image Recognition Demonstration using Pre-trained Models.

Unit No V

Generative AI, NLP & AI Tools

06 Hours

Introduction to Generative AI, Overview of ChatGPT, Gemini and AI Assistants, Basics of Natural Language Processing (NLP), Text Processing and Sentiment Analysis, Prompt Engineering Techniques, AI Chatbots and Virtual Assistants, Introduction to Retrieval-Augmented Generation (RAG), Voice-enabled AI Systems, AI Agents and Automation Workflows, AI Ethics, Bias and Responsible AI, Introduction to No-

Code AI Platforms, Teachable Machine and Hugging Face Tools, AI-based Demonstration Activities.

Unit No VI

AI Projects, Innovation & Emerging Trends

06 Hours
 

AI Project Development Lifecycle, Problem Identification and Data Collection, Model Building and Testing, AI Applications in Healthcare, Energy, Power Systems, Oil & Gas, IT, E&TC and Automobile Engineering, Introduction to AI-based Mini Projects, Career Opportunities in AI and ML, Introduction to Kaggle and GitHub, Open-Source Contribution Activities, AI Hackathons, Industry Case Studies, Research Paper Reading Sessions, Innovation Challenges, Team-based Mini Project Presentation and Emerging

Trends in Artificial Intelligence.

Reference Books :

1.     Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Pearson Education, ISBN: 978-0134610993

2.     Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow, O’Reilly Media, ISBN: 978-1492032649

3.     François Chollet, Deep Learning with Python, Manning Publications, ISBN: 978-1617296864

4.     Andreas C. Müller and Sarah Guido, Introduction to Machine Learning with Python, O’Reilly Media, ISBN: 978-1449369415

5.     Oliver Theobald, Machine Learning for Absolute Beginners, Scatterplot Press, ISBN: 978-1730977626

6.     Andriy Burkov, The Hundred-Page Machine Learning Book, Andriy Burkov Publications, ISBN: 978-1999579500

e- Books :

·       https://github.com/ageron/handson-ml2/blob/master/README.md

·       https://www.deeplearningbook.org/

·       https://www.cs.cmu.edu/~tom/mlbook.html

·       https://d2l.ai/

·       https://developers.google.com/machine-learning/crash-course

MOOCs Courses link:

  1. Machine Learning Specialization – Coursera https://www.coursera.org/specializations/machine-learning-introduction
  2. AI For Everyone – Coursera https://www.coursera.org/learn/ai-for-everyone
  3. Python for Everybody – Coursera https://www.coursera.org/specializations/python
  4. Deep Learning Specialization – Coursera https://www.coursera.org/specializations/deep-learning
  5. Introduction to Artificial Intelligence – NPTEL https://nptel.ac.in/courses/106102220
  6. Python for Data Science – NPTEL https://onlinecourses.nptel.ac.in/noc23_cs99/preview