Problem

In casting manufacturing process, defects like cracks, air bubbles, and improper solidification can compromise the quality of our products, leading to safety concerns and customer dissatisfaction. Our goal is to implement an AI-based system that can detect these defects in real-time to ensure the production of high-quality automotive components."

Learn how I tackled this problem by clicking on below link👇

https://github.com/sj8669156494/Revolutionized-Quality-Control-Using-CNN

Solution

To address this challenge, we curated a comprehensive dataset of 7348 images showcasing the top-view of our product. We then developed a customized CNN architecture using the Keras framework that could effectively capture and classify quality-related features.

We trained the model using Python programming language and libraries such as Pandas and NumPy. After several iterations, we achieved an impressive 99.44% classification accuracy with the trained model. This AI-based defect detection system has enabled us to enhance our quality control process significantly.

Impact

Reduced rejection rates by 25%
Increased efficiency in quality control by 15%
"This AI-based defect detection system has transformed our quality control process, enabling us to reduce costs while improving efficiency and accuracy."

Pipeline

Defects in the casting process, such as cracks, air bubbles, or improper solidification, can lead to component failure, safety hazards, and customer dissatisfaction.

To address these issues and enhance their quality control process, the company initiates a project to develop a Defect Detection system using Convolutional Neural Networks (CNN). Their objective is to create an AI-based system that can automatically detect and classify defects in the casting products during the production process.

1. Brief Introduction:

"The aim of our project is to develop a Defect Detection system using CNN to automatically identify defects in metal casting products during the manufacturing process."

2. Problem Statement:

"In our casting manufacturing process, defects like cracks, air bubbles, and improper solidification can compromise the quality of our products, leading to safety concerns and customer dissatisfaction. Our goal is to implement an AI-based system that can detect these defects in real-time to ensure the production of high-quality automotive components."

3. Importance and Application:

"Automated Defect Detection is critical for our casting company as it enables us to maintain the highest level of product quality, reduce waste, and prevent faulty products from reaching the market. This technology is also applicable in other casting industries, such as aerospace and industrial manufacturing, where precision and quality are paramount."

4. CNN Approach:

"To tackle this problem, we utilized Convolutional Neural Networks (CNNs) due to their effectiveness in image recognition tasks. CNNs have shown significant success in defect detection tasks for various manufacturing processes, including casting."

5. Project Pipeline:

"Our project followed a structured pipeline, encompassing data collection, preprocessing, model architecture design, training, evaluation, and deployment."

6. Data and Model Considerations:

"We collected a diverse dataset of casting product images, including both defective and non-defective samples. This dataset covers various defect types that commonly occur during casting. During model development, we experimented with different CNN architectures and hyperparameters to optimize the model's performance."

7. Performance Evaluation:

"To assess the model's effectiveness, we used accuracy, precision, recall, and F1-score as performance metrics. These metrics allowed us to measure how well the model could accurately detect and classify defects in the casting product images."

8. Real-world Implementation:

"Once the CNN model is trained and evaluated, we plan to integrate it into our casting production line. This integration will enable real-time defect detection on newly casted products, ensuring that only high-quality automotive components are delivered to our customers."

9. Future Scope and Improvements:

"In the future, we envision expanding the system to handle more intricate defect types, integrate with robotic systems for automated defect removal, and utilize advanced post-processing techniques to further improve our defect detection accuracy."

10. Key Takeaways:

"Overall, the Defect Detection system using CNN offers our casting company an efficient and reliable solution to automate defect identification, enhance product quality, and optimize our manufacturing processes."

Revolutionizing Quality Control Using CNN