Industry 4.0 Iii: Advanced Computational Technologies

AI, Machine Learning, and Vision Systems for Next-Generation Manufacturing

What you'll learn

tudents will gain a solid understanding of fundamental machine learning concepts, including supervised and unsupervised learning.

Students will learn to implement machine learning models for fault detection in rotating and moving parts.

Students will understand the role of computer vision in industry, focusing on image processing techniques such as edge detection.

Students will differentiate between AI, machine learning, and deep learning, and understand their roles in industrial applications.

Requirements

B.S or graduate students, Mechanical engineering, Manufacturing Engineering, Aerospace Engineering, Electronics Engineering, Software/Computer Engineering, Technicians with industry experience.

Description

This course provides an introduction to machine learning and artificial intelligence (AI) concepts, specifically tailored for industrial applications. Students will gain foundational knowledge in supervised and unsupervised learning techniques, including linear regression, classification, decision trees, and clustering methods like k-means and DBSCAN. Through practical examples, students will learn how these techniques are applied for fault detection, predictive maintenance, and process optimization in mechanical systems.A key component of the course focuses on AI's role in industry, including the integration of machine learning models for real-world applications such as gear wear prediction and bearing failure analysis. Students will also explore the intersection of AI and computer vision in industrial systems, learning about convolution operations, image processing techniques like edge detection, and advanced object recognition methods like YOLO and Faster R-CNN, all of which are essential for quality control and automation in manufacturing.The course delves into the distinctions between AI, machine learning, and deep learning, equipping students with the knowledge to leverage these technologies effectively in industrial settings. Additionally, students will explore reinforcement learning, particularly in the context of cobots (collaborative robots) that autonomously optimize assembly paths. By the end of the course, students will have a comprehensive understanding of how AI and machine learning can drive innovation and efficiency in modern manufacturing environments.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Course Structure & Syllabus

Lecture 3 Specialization Options

Section 2: Fundamentals of Machine Learning for Industrial Applications

Lecture 4 Introduction to Machine Learning

Lecture 5 Supervised learning & Linear regression

Lecture 6 Supervised Learning: Classification

Lecture 7 Supervised Learning: Decision Trees

Lecture 8 Unsupervised Learning: Clustering (k-means, DBSCAN)

Section 3: AI and Machine Learning for Industrial Applications

Lecture 9 Machine Learning Models for Fault Detection in Rotating and Moving Parts

Lecture 10 Real-world examples: Gear wear prediction, bearing failure analysis.

Lecture 11 CAD Generative Design

Section 4: Introduction to Computer Vision in Industrial Systems

Lecture 12 Convolution operations

Lecture 13 Fundamentals of Image Processing: Edge Detection

Lecture 14 Feature extraction (Fourier transform)

Lecture 15 Object detection and recognition: YOLO, Faster R-CNN.

Section 5: Introduction to Artificial Intelligence in Industry

Lecture 16 Differentiating AI, ML, and deep learning.

Lecture 17 Knowledge representation: Graphs, logic-based systems.

Lecture 18 Reinforcement Learning in Automation

Lecture 19 Applications: Cobots learning optimal assembly paths.

Section 6: Closing

Lecture 20 Closing

Engineers, senior or grad students. Entrepreneurs and Innovators, designers, manufacturing professionals (with our without a college degree). Overall, Professionals Seeking Career Growth