Advanced Features, Simple Workflow

AI INSPECT software uses modern deep learning convolutional networks to learn the visual differences between normal and defect pixels in any set of correlated images. The intuitive, guided workflow consists of the following six steps:

Step 1

1) Import and Classify Images

User imports defects and nominal images into the application to create a project model

Step 2

2) Defect Marking

User highlights image defects with easy-to-use marking tools

Step 3

3) Training Setup

User is guided through the training setup process requiring no AI knowledge

Step 4

4) Training

The AI inspection model is automatically created and optimized

Step 5

5) Results and Thresholds

User reviews AI inspection results and adjusts suggested thresholds as desired

Step 6

6) Export and Deploy

An AI model is optimized to detect defects on your parts and is ready for integration into your inspection system

“Why should I care about a cool new technology until it’s solving any of my problems?”—This was the exact conversation I had with the executive of a water purification plant over a warm cup of coffee.

What started as idle chit chat ended up evolving into an insightful and meaningful discussion about  AI technologies for defect detection and quality control. This conversation significantly changed my vision and approach.

The goal of this article is to describe how to implement AI-based visual inspection for defect detection. It’s not about the coolness of artificial intelligence—no, it’s about practical solutions to specific problems.

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Concept of AI-Powered Visual Inspection

Artificial intelligence in manufacturing is a trendy term. When it comes to describing AI-based defect detection solutions, it’s often about some kind of visual inspection technology that bases on deep learning and computer vision.

The Concept of Computer Vision

WHAT IS DEEP LEARNING IN A VISUAL INSPECTION?

Deep learning is an aspect of machine learning technology that’s powered by artificial neural networks. The operating principle of deep learning technology is teaching machines to learn by example. By providing a neural network with labeled examples of specific types of data, it’s possible to extract common patterns between those examples, and then transform it into a math equation. This helps to classify future pieces of information.

With visual inspection technology, integration of deep learning algorithms allows differentiating parts, anomalies, and characters, which imitate a human visual inspection while running a computerized system.

So, what does this mean exactly AR glasses ? Let’s use an example.

If you were to create a visual inspection software for automotive manufacturing, you should develop a deep learning-based algorithm and train it with examples of defects it must detect. With enough data, the neural network will eventually detect defects without any additional instructions.

Deep learning-based visual inspection systems are good at detecting defects that are complex in nature. They not only address complex surfaces and cosmetic flaws—but also generalize and conceptualize the parts’ surfaces.