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Brake Pad Quality Control with Artificial Intelligence

Brake-Pad-Quality-Control-with-Artificial-Intelligence
Table of contents

Brake Pad Quality Control with Artificial Intelligence

How are brake pads made?

AI for Quality Inspection

Conclusion

Reference

Brake Pad Quality Control with Artificial Intelligence

Brake systems are one of the most critical components of a vehicle, as they ensure safety by stopping the car and preventing accidents. It is essential to prevent low-quality brake pads from entering the market to ensure reliability and maintain performance over time.

In this blog, we will explore how AI is being utilized for the quality control of brake pads. By utilizing deep learning and image processing, defects in brake pads can be quickly and accurately identified and defective items removed from the production line. The ultimate goal is to assist businesses in meeting consumer demands for high-quality products.

How are brake pads made?

The brake pad is one of the most challenging components of a vehicle to engineer and produce. Let’s review its manufacturing process.

  1. Raw material: Friction materials are made by mixing over a dozen types of raw materials, with each factory having its unique formula.
Friction materials
Figure 1. Friction materials. Source

 

  1. Steel Back Plate: The back plate is cleaned to ensure a seamless application of glue and proper adhesion of the friction materials.
Back-plate
Figure 2. Back plate. Source

 

  1. Press: The friction material is attached to the backing plate through pressure and heat.
Press friction materials
Figure 3. Press friction materials to back plate. Source

 

  1. Grooving and Chamfering: These processes shape the brake pad to fit the brake system.
Grooving and chamfering
Figure 4. Grooving and chamfering. Source

 

  1. Spraying the powder: This helps to bond the friction material to the plate.
Spraying the powder
Figure 5. Spraying the powder. Source

 

  1. Installing Wear Indicator: A device that indicates when the brake pads need to be replaced
Install Indicator
Figure 6. Install Indicator. Source

 

  1. Quality Control: Careful inspection of the final product.
Quality control
Figure 7. Quality control. Source

 

During the manufacturing process, several defects can occur such as impurities in the friction materials, cracks in the friction material:

  • Impurities in the friction material
  • Cracks on the friction material
  • Corrosion on the friction material
  • Abnormalities on the steel back plate
  • improper punching of the steel back plate
defects in brake pads
Figure 8. 1) Impurities in friction materials 2) crack in the friction material 3) Corrosion on the friction material 4) abnormality on steel sack plate 5) incorrect punches

 

Ensuring that defective products are quickly identified and removed from the production line is a crucial responsibility for any factory. However, traditional methods of quality control, which rely on human labor, can be both time-consuming and costly. With the advent of artificial intelligence, the quality control process can now be completed with greater speed and accuracy. AI-based systems can quickly and efficiently identify defects in brake pads, reducing the need for manual inspection and minimizing the risk of defective products reaching the consumer.

AI for Quality Inspection

Artificial intelligence has revolutionized the industry, providing new opportunities for flexible and efficient production, even when it comes to complex and customized products.

Manufacturers are now using smart cameras and AI models to enhance their quality inspection processes compared to traditional methods.

Image processing methods include:

Classification

This model aims to specify the class of each entity, for example, classifying defects into categories such as minor defects, major defects, or OK and NOT OK.

Object Detection

An object detection model finds objects in an image and specifies their location with a bounding box, they can be used to detect features such as detecting cracks or corrosion in brake pads.

Segmentation

A segmentation model aims to specify the object in a more precise way by selecting the pixels containing the object.

The AIEX platform provides all the necessary tools to train models for classification, object detection, and segmentation.

Please reach out to us if you need more information.

AIEX is an industry solution platform created by computer vision (CV) experts and powered by deep learning aiming to provide cutting-edge solutions to various industries.

defect detection using ai
Figure 9. 1) Classification model output 2) object detection model output 3) segmentation model output

Conclusion

Image processing and deep learning have revolutionized the quality control process, allowing for quick and accurate detection of defects in any part of the production line. The rate of defect detection with deep learning solutions is significantly higher than that of human inspection.
AI-based methods can be used to identify defects in a wide range of products, reducing the need for human intervention and automating and optimizing quality processes. Businesses can leverage these technologies to increase productivity, cut expenses, reduce waste, and enhance customer satisfaction.

Reference

[1] https://www.powertech-auto.com/how-brake-pad-manufactured-process/

[2] https://www.mk-kashiyama.com/en/brake-tips/manufacturing-process

[3] https://machinelearningmastery.com/object-recognition-with-deep-learning/

[4] https://postindustria.com/how-ai-for-quality-control-enhances-yield-in-ml-manufacturing/

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