Quality Control (Pilot)

Type of course:

Digital learning, Path

Duration:

2 hours

Workload:

2 hours

Proficiency:

Intermediate

Target:

Manager, Professionals, Workers

This training program focuses on equipping professionals in the manufacturing industry with the knowledge and skills to apply data science techniques for quality control processes. The course will guide learners through solving a real-world quality control problem using a defect detection in fabric production use case. The training emphasizes hands-on experience, enabling participants to design and implement data science projects tailored to their own factory settings.

Learning outcomes

By the end of this course, students will be able to explain relevant data science techniques and tools to process and analyze manufacturing data in solving a quality control problem for the detection of defects, reducing errors, and ensuring higher product quality.
By the end of this course, students will be able to abstract the quality control problem and formulate it as a set of appropriate data science and machine learning problems.
By the end of this lesson, students will be able to apply relevant data science techniques and tools to process and analyze manufacturing data in solving a quality control problem for the detection of defects, reducing errors, and ensuring higher product quality.

LessonData Driven Quality Control (Pilot)

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INTRO

Introduction

MODULES

Introduction to Quality Control in Manufacturing 3 Unities

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Definition of Quality Control (QC)

Traditional Quality Control Methods

Importance of Transitioning to Data-Driven QC

Understanding Data-Driven Quality Control 2 Unities

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Introduction to Data-Driven Quality Control

CRISP-DM Framework in Data-Driven QC

Potentials and Limits of Data-Driven QC 3 Unities

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Benefits of Data-Driven QC

Challenges and Limitations of Data-Driven QC

Mitigating Challenges

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LessonData for Quality Control (Pilot)

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INTRO

Introduction

MODULES

Introduction to Data-Driven Quality Control

Key Data Types for Quality Control and Their Impact 5 Unities

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Identifying Data from Manufacturing Sources 7 Unities

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Sensors and IoT Devices

Cameras and Vision Systems

PLC (Programmable Logic Controller) Systems

Operator Logs and Maintenance Records

Enterprise Resource Planning (ERP) Systems

Environmental Monitoring Equipment

Best Practices for Data Collection

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LessonDefects and Methods for Quality Control (Pilot)

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INTRO

Introduction

MODULES

Understanding Manufacturing Defect Types and Detection Challenges 2 Unities

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The Importance of Defect Detection

Common Types of Defects in Manufacturing

Mapping Defect Types to Suitable Machine Learning Models 4 Unities

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Surface Defects

Dimensional Inaccuracies

Material Defects

Assembly Errors

Evaluating ML Model Strengths and Limitations for Defect Detection 5 Unities

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Convolutional Neural Networks (CNNs)

Random Forests

Support Vector Machines (SVMs)

Autoencoders

Isolation Forests

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LessonUse Case: Meet Katty Fashion (Pilot)

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INTRO

Introduction

MODULES

Company Overview 3 Unities

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Vision and Mission

Collaborations and Recognitions

Commitment to Sustainability and Ethics

Innovation and R&D

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LessonUse Case: Traditional Quality Control at Katty Fashion (Pilot)

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INTRO

Introduction

MODULES

Raw Material Quality Control

Finalized Product Quality Control

END

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LessonUse Case: Data Driven Quality Control at Katty Fashion (Pilot)

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INTRO

Introduction

MODULES

Automated Visual Inspection 2 Unities

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At the Fabric level

At the garment level

Predictive Analytics for Quality Control

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LessonVisual Data Collection for Quality Control (Pilot)

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INTRO

Introduction

MODULES

Sensor Properties and Specifications 4 Unities

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Sensor Quality

Resolution

Frame Rate

Monochrome vs. Color Sensors

Practical Considerations 4 Unities

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Lighting Conditions and Environmental Factors

Data Size and Processing Power

Real-Time Processing and Latency Constraints

Cost-Performance Trade-Offs

Balancing Sensor Specifications with Data Science Methods 5 Unities

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LessonVisual Dataset Size for Quality Control (Pilot)

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INTRO

Introduction

MODULES

Relationship Between Dataset Size and Model Performance 2 Unities

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How dataset size affects machine learning models

Case study illustrating the impact of dataset size on model accuracy

Determining the Ideal Dataset Size 4 Unities

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Nature of defects

Variability in production processes

Specific goals of the machine learning model

Methods for estimating required dataset size

Best Practices for Dataset Curation 2 Unities

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Strategies for collecting high-quality visual data

Ensuring representativeness and diversity in datasets

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LessonOptimal Sensor Placement for Quality Control (Pilot)

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INTRO

Introduction

MODULES

Principles of Sensor Placement 2 Unities

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Objectives of Sensor Placement

Key Considerations

Strategies for Optimal Camera Placement for Quality Control 4 Unities

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Camera Placement for End-Product Inspection

Lighting and Background

Lens and Resolution Selection

Avoiding Overlapping Fields

Case Study in Industrial Manufacturing

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LessonVisual Data Augmentation for Quality Control (Pilot)

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INTRO

Introduction

MODULES

Fundamentals of Data Augmentation Techniques 4 Unities

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Basic Transformations: Flipping, Rotation, Scaling

Color and Lighting Adjustments: Brightness, Contrast, and Saturation

Cropping and Zooming: Focus on Product Detail

Blurring and Noise: Simulating Environmental Variations

Advanced Augmentation Techniques for Anomaly Detection 3 Unities

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Synthetic Defect Simulation: Scratches, Cracks, and Other Anomalies

Random Erasing and Masking Techniques

Combining Augmentations: Building Realistic Training Sets

Practical Implementation of Data Augmentation

Evaluating the Impact of Data Augmentation on Model Performance 2 Unities

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Model Performance Metrics and How to Measure Improvement

Comparing Models with and without Augmentation

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LessonData Collection Frequency for Quality Control (Pilot)

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INTRO

Introduction

MODULES

Data Collection Goals 3 Unities

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Detecting All Defects

Random Sampling

Trend Analysis

How to Decide? 3 Unities

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Understand the Nature of the Process

Assess the Risk of Quality Issues

Cost-Benefit Consideration

Real-World Case Study 2 Unities | 1 Quiz

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Manufacturing Quality Control

Long-Term Product Testing

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LessonVisual Data Annotation for Quality Control (Pilot)

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INTRO

Introduction

MODULES

Annotation Techniques for Anomaly Detection 3 Unities

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Bounding Boxes for Anomaly Detection

Pixel-Level Segmentation for Precise Defect Identification

Key Points and Polygons for Complex Shapes and Anomalies

Tools and Platforms for Visual Data Annotation

Evaluating Annotation Quality 2 Unities

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Quality Control Methods for Annotation

Linking Annotation Quality to Model Performance

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LessonStorage Solution for Sensor Data (Pilot)

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INTRO

Introduction

MODULES

Data in Industrial Manufacturing 2 Unities

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Data Granularity and Retention Needs

Frequency and Volume of Data Collection

Data Storage Solutions 4 Unities

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On-Premises Storage Solutions

Cloud Storage Solutions

Hybrid Solutions

Edge Computing in Industrial Environments

Data Storage Formats and Optimization for Sensor Data 3 Unities

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Common Storage Formats for Sensor Data

Data Compression Techniques

Best Practices in Data Partitioning and Indexing

Cost Optimization Strategies for Data Storage 4 Unities

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Data Lifecycle Management

Choosing Cost-Effective Storage Tiers

Storage and Query Cost Considerations

Minimizing Data Redundancy

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LessonSecurity of Data Collected for Quality Control (Pilot)

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INTRO

Introduction

MODULES

Data Privacy Regulations and Compliance

Data Security Risks and Threats

Security Architectures for Data Sharing

Implementing Secure Data Connections

Collaborating with External Experts

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LessonExploratory Data Analysis on Image Data (Pilot)

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INTRO

Introduction

MODULES

Exploratory Data Analysis

Understand the underlying structure of the data 5 Unities

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Image Size/Shape Distribution

Image Channels (RGB, Grayscale)

Distribution of Pixel Values

Statistical Measures of Images

PCA (Principal Component Analysis) on Image Data

Detect issues 4 Unities

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Data Imbalance (Class Distribution)

Visualizing Samples from the Dataset

Identifying Noisy Images

Checking for Image Metadata/Annotations

Decide on the preprocessing steps 3 Unities

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Data Augmentation Exploration

Preprocessing Checks (Resizing, Normalization)

Edge Detection and Texture Analysis

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LessonTask: Exploratory Data Analysis on Image Data (Pilot)

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INTRO

Instructions and Related Files

MODULES

Dataset Presentation

Demonstration Video

END

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LessonPreprocessing for Convolutional Neural Network (Pilot)

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INTRO

Introduction

MODULES

Image Resizing and Scaling for CNN Consistency 2 Unities

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Techniques for Resizing Images

Best Practices for Image Resizing and Scaling

Noise Reduction Techniques for Industrial Visual Data 2 Unities

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Noise Reduction Techniques

Best Practices for Noise Reduction

Contrast Enhancement for Better Defect Detection 2 Unities

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Techniques for Contrast Enhancement

Best Practices for Contrast Enhancement

Color Space Conversion for Simplified Feature Detection 2 Unities

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Commonly Used Color Spaces and Their Applications

Best Practices for Color Space Conversion

Normalization and Standardization for Data Consistency 2 Unities

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Techniques for Normalization and Standardization

Best Practices for Normalization and Standardization

Splitting and Organizing Data for Effective CNN Training 6 Unities

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Standard Data Splitting Ratios

Organizing Data for CNN Training

Handling Class Imbalance in Industrial Datasets

Ensuring Data Variability for Better Generalization

Best Practices for Splitting and Organizing Data

Handling Class Imbalance in Industrial Datasets

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LessonTask: Preprocessing for Convolutional Neural Network (Pilot)

Course Content

INTRO

Instructions and Related Files

MODULES

Dataset Presentation

Demonstration Video

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LessonConvolutional Neural Network for Quality Control (Pilot)

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INTRO

Introduction

MODULES

High-Level Architecture of CNNs 1 Unity

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Example

Training Parameters of CNNs

Training and Evaluating the Model

Best Practices for Training CNNs with Visual Datasets

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LessonTask: Convolutional Neural Network for Quality Control (Pilot)

Course Content

INTRO

Instructions and Related Files

MODULES

Dataset Presentation

Demonstration Video

END

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LessonFine-Tuning Convolutional Neural Network for Quality Control (Pilot)

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INTRO

Introduction

MODULES

Introduction to Model Optimization

Fine-Tuning 2 Unities

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The Fine-Tuning Process

Best Practices for Fine-Tuning

Hyperparameter Optimization 3 Unities

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Key Hyperparameters in CNNs

Optimization Techniques

Best Practices for Hyperparameter Optimization

Regularization Techniques 2 Unities

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Key Regularization Techniques

Best Practices for Regularization

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LessonTask: Fine-Tuning Convolutional Neural Network for Quality Control (Pilot)

Course Content

INTRO

Instructions and Related Files

MODULES

Dataset Presentation

Optimization of the CNN

END

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LessonCourse Test - Quality Control (Pilot)

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INTRO

Introduction

MODULES

Course Test 1 Quiz

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Test

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Topics

Digital Transformation, Machine Learning, Artificial Intelligence (AI), Computer Vision

Provided by

University of Groningen

Content created in 2024

+31 enrolled

Free

Take the next step toward your learning goals

Course Includes

15 Quizzes

Course Filter

Quality Control (Pilot)

Type of course:

Duration:

Workload:

Proficiency:

Target:

Learning outcomes

LessonData Driven Quality Control (Pilot)

Course Content

LessonData for Quality Control (Pilot)

Course Content

LessonDefects and Methods for Quality Control (Pilot)

Course Content

LessonUse Case: Meet Katty Fashion (Pilot)

Course Content

LessonUse Case: Traditional Quality Control at Katty Fashion (Pilot)

Course Content

LessonUse Case: Data Driven Quality Control at Katty Fashion (Pilot)

Course Content

LessonVisual Data Collection for Quality Control (Pilot)

Course Content

LessonVisual Dataset Size for Quality Control (Pilot)

Course Content

LessonOptimal Sensor Placement for Quality Control (Pilot)

Course Content

LessonVisual Data Augmentation for Quality Control (Pilot)

Course Content

LessonData Collection Frequency for Quality Control (Pilot)

Course Content

LessonVisual Data Annotation for Quality Control (Pilot)

Course Content

LessonStorage Solution for Sensor Data (Pilot)

Course Content

LessonSecurity of Data Collected for Quality Control (Pilot)

Course Content

LessonExploratory Data Analysis on Image Data (Pilot)

Course Content

LessonTask: Exploratory Data Analysis on Image Data (Pilot)

Course Content

LessonPreprocessing for Convolutional Neural Network (Pilot)

Course Content

LessonTask: Preprocessing for Convolutional Neural Network (Pilot)

Course Content

LessonConvolutional Neural Network for Quality Control (Pilot)

Course Content

LessonTask: Convolutional Neural Network for Quality Control (Pilot)

Course Content

LessonFine-Tuning Convolutional Neural Network for Quality Control (Pilot)

Course Content

LessonTask: Fine-Tuning Convolutional Neural Network for Quality Control (Pilot)

Course Content

LessonCourse Test - Quality Control (Pilot)

Course Content

Topics

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