Artificial Intelligence In Manufacturing: Quality 4.0 Intro

Yellow Belt Training: Enhance Your Strategic Decision Making Around the Use of Key AI Technologies.

What you'll learn

How AI tools are applied to solve complex problems

Understand the challenges posed by manufacturing big data.

Learn to develop Cyber-Physical Systems to solve engineering intractable problems

How to drive innovation in manufacturing

Limitations of quality control systems

Advantages of learning Quality 4.0

Requirements

No programming or machine learning background is required

Description

In the manufacturing industry, the transformative potential of artificial intelligence (AI) is widely acknowledged. The current era witnesses the pervasive industrialization of AI, with a particular emphasis on its role in enhancing quality control, a focal point across industries.This course centers around pivotal AI technologies, notably machine learning and deep learning, aiming to empower managers in crafting a visionary outlook for Quality 4.0 and assisting engineers in devising and executing intelligent quality systems. Rooted in theory, empirical evidence, a diverse array of techniques, and the author's extensive experience studying complex manufacturing systems, the course content is meticulously curated.Our deliberate approach ensures that mathematical and coding complexities remain accessible to engineers, enabling a sharper focus on practical problem-solving through AI. Engage in an immersive exploration encompassing key technology introductions, profound manufacturing insights, compelling case examples, and the opportunity to undertake a business-oriented project.Embark on a journey that illuminates the essence of central Quality 4.0 today and its potential to catalyze innovation to solve a whole new range of engineering intractable problems. The course encompasses a broad spectrum of knowledge areas, including programming, smart manufacturing, quality control, statistics, optimization, machine learning, and a novel 5-step problem-solving strategy tailored for Quality 4.0 deployment.

Overview

Section 1: Ch. 1 - Introduction

Lecture 1 Introduction: Innovation Importance of Quality 4.0

Lecture 2 About the Creator and Curriculum of Quality 4.0 Institute

Lecture 3 Meet the Team

Lecture 4 About the Program

Lecture 5 Belts Levels Overview

Lecture 6 Yellow Belt Curricula

Lecture 7 Project and Algorithms Courses Sequence

Lecture 8 Candidates and Aspects

Lecture 9 Course Structure

Section 2: Importance of Quality and Learning Quality Control

Lecture 10 Yellow Belt Certification Process

Lecture 11 Did You Know? Artificial Intelligence and Quality

Lecture 12 Q4I Bot Introduction

Lecture 13 Learning Quality Control - Overview

Lecture 14 LQC Evolution of Statistical Process Control

Lecture 15 Learning Quality Control - Explained

Lecture 16 Dimension Definition

Lecture 17 Manufacturing Importance

Lecture 18 Manufacturing Quality Importance

Lecture 19 Quality Management Systems

Lecture 20 LQC: AI and the Zero Defects Vision

Section 3: Smart Manufacturing

Lecture 21 Smart Manufacturing Introduction

Lecture 22 Smart Manufacturing Economical Impact

Lecture 23 Smart Manufacturing Technologies

Lecture 24 Smart Manufacturing Cyber Physical Systems

Lecture 25 Smart Manufacturing: Activity Remarks

Lecture 26 Smart Manufacturing Data Driven Approaches

Lecture 27 New Generation of Data Driven Approaches

Lecture 28 Data Driven Modeling

Lecture 29 Real Time Iterations

Lecture 30 Self Learning Adaptations and Executions

Lecture 31 Smart Manufacturing Remarks

Section 4: Modern Quality Movement

Lecture 32 Problem Solving Strategy Defined

Lecture 33 Evolution of the Manufacturing Quality Control Movement

Lecture 34 Plateau of Six Sigma and the Rise of Quality 4.0

Section 5: Breakdown of Traditional Quality Control

Lecture 35 Breakdown of Traditional Quality control

Lecture 36 Six Sigma Decline

Lecture 37 Limitation 1. ANN

Lecture 38 Limiitation 2. Curse of Dimensionality

Lecture 39 Lost Key in Dimensionality

Lecture 40 Limitation 3. Computation Time

Lecture 41 Limitation 4. Vision Systems

Lecture 42 Limitation 5. Control Charts

Section 6: The Rise of Quality 4.0

Lecture 43 Rise of Quality 4.0 Defined

Lecture 44 Douglas Montogomery Recommnedation

Lecture 45 Remarks Rise of Quality 4.0

Lecture 46 Areas of Knowledge

Lecture 47 Beyond Six Sigma

Lecture 48 Implementing Quality 4.0

Section 7: Ch. 2 - Quality 4.0 Technologies

Lecture 49 Intro: Quality 4.0 Technologies Overview

Section 8: Artificial Intelligence

Lecture 50 Narrow AI vs. General AI

Lecture 51 History & Statistics

Lecture 52 AI Machine Learning

Lecture 53 Machine Learning Techniques

Lecture 54 Supervised Learning

Lecture 55 Classification

Lecture 56 Machine Learning Projects Characteristics

Lecture 57 Machine Learning Ill Conceived Projects

Lecture 58 AI Business Impact

Section 9: ChatGPT

Lecture 59 ChatGPT

Lecture 60 ChatGPT Activity

Lecture 61 Results - ChatGPT Activity

Section 10: Cloud Storage and Computing

Lecture 62 Cloud Storage and Computing

Lecture 63 Cloud Storage

Lecture 64 Cloud Computing

Lecture 65 CSC Statistics & Benefits

Lecture 66 Framework for Quality 4.0

Lecture 67 Online Deployment

Lecture 68 Fog Computing

Lecture 69 Edge Computing

Section 11: Industrial Internet of Things

Lecture 70 Internet of Things

Lecture 71 Industrial Internet of Things

Lecture 72 IIot Manufacturing Things

Lecture 73 Smart Sensors

Lecture 74 Actuators

Section 12: Cyber Physical Systems

Lecture 75 Cyber Physical System Development

Lecture 76 Cyber Physical System Components

Lecture 77 Cyber Physical System Cognition

Lecture 78 Cyber Physical System Control

Lecture 79 Cyber Physical System Examples

Section 13: Big Data

Lecture 80 Big Data Defined

Lecture 81 Structured Data

Lecture 82 Unstructured Data

Lecture 83 Industrial Big Data

Section 14: Manufacturing Data

Lecture 84 Manufacturing Big Data Defined

Lecture 85 Manufacturing Big Data Statistics

Lecture 86 Manufacturing Big Data Considerations

Lecture 87 10 V's of Manufacturing Big Data

Section 15: Transforming Big Data into Learning Data

Lecture 88 Data Frames

Lecture 89 Learning Data Sets

Lecture 90 Model Development

Lecture 91 Data Splits & Hyper-parameters

Lecture 92 Data Types

Section 16: Binary Classification of Quality

Lecture 93 Data Sets

Lecture 94 Data Labeling

Lecture 95 Data Annotations

Lecture 96 Data Characteristics

Lecture 97 Data Sets Examples Unstructured Data

Lecture 98 Data Set Examples Strucutred Data

Lecture 99 BCoQ Recap

Lecture 100 Project Selection Introduction

Lecture 101 Yellow Belt Knowledge Acquired

Lecture 102 Going the Distance

Lecture 103 The Green Belt

Lecture 104 Thank You Yellow Belt Completion

Manufacturing technicians, engineers, managers and directors,Professionals interested in smart manufacturing,Professionals interested in learning how machine learning can be applied to drive innovation.