AI Image Classification for Metal Processing Defects｜RapidMiner

【 Background 】

       During metal processing, various types of surface defects often occur. If these defects are not detected and classified in time, they may affect product quality and increase costs. Traditional manual classification is time-consuming and error-prone, so we introduced AI technology to solve this problem.

Defect classification data consolidated as training material for AI

【 Results 】

By using Altair RapidMiner, we successfully built a highly efficient AI-based image classification system. After training and validation, the results are as follows:

1. The trained AI model can accurately classify surface defects in metal processing, with a verified accuracy rate of over 90%.

2. In real-world use, when classifying 24 new photos, the accuracy reached 91.67%, significantly improving classification efficiency and reducing labor costs.

​​​​
 

Using AI to automatically classify machining defects

【 Technical Features 】

1. Data collection and preprocessing:We collected 1,558 images of metal surface defects and categorized them into five types—crescent gaps, streak spots, water marks, weld lines, etc.—as training data.
    

2. AI learning and training workflow: In Altair AI Studio, we built an AI training workflow. The AI first reads the images and extracts features, then learns the relationships between images and their classes, and finally saves the trained model.
    

3. Fast and accurate: This case demonstrates the great potential of AI in industrial applications. It effectively addresses the pain points of traditional manual classification and enables intelligent quality inspection and control.