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Convolutional Autoencoders Pave the Way for Efficient AFP Inspection



Overcoming Data Limitations in Automated Fibre Placement Defect Detection

Automated Fibre Placement (AFP) is a cutting-edge manufacturing method used to produce high-quality composite parts for industries such as aerospace. However, ensuring the quality of AFP-manufactured components remains a significant challenge. Defects introduced during the layup process can severely impact the final product's structural integrity and performance.

Traditional defect detection methods in AFP rely heavily on manual inspection, which is time-consuming, labor-intensive, and prone to human error. To address this issue, researchers have been exploring the use of automated inspection systems using artificial intelligence (AI) and computer vision (CV) techniques.

One of the most promising approaches is supervised learning, where a model is trained on a labeled dataset to classify defects. However, supervised learning methods face a major limitation in the AFP industry: the scarcity of labeled defect data. Collecting and annotating a large enough dataset of defective samples is expensive, disruptive to production, and challenging due to the rarity of defects in real-world manufacturing.

  • Real-world defect data is scarce, as defects are rare in production

  • Collecting defect samples is expensive and disrupts production schedules

  • Defects can take many forms, making it difficult to establish a consistent labeling strategy

To overcome these data limitations, we propose an unsupervised anomaly detection framework that learns from normal, non-defective samples only. By leveraging the inherent structure and uniformity of AFP-manufactured composite parts, our approach can effectively identify anomalies without relying on a large labeled dataset of defects.


The Challenges of Manual Inspection and Supervised Learning in AFP Quality Control

Ensuring the quality of composite parts manufactured using Automated Fibre Placement (AFP) is crucial, particularly in industries such as aerospace, where component integrity is paramount. Traditional quality control methods in AFP rely heavily on manual inspection, which presents several challenges:

Time-consuming and labor-intensive

  • Manual inspection requires skilled technicians to examine each composite part layer by layer

  • Inspecting large, complex parts can take hours or even days

Prone to human error

  • Fatigue and subjectivity can lead to missed defects or inconsistent quality assessments

  • Human inspectors may struggle to identify subtle or small-scale defects consistently

Inconsistent defect classification

  • Different organizations and technicians may have varying standards for categorizing defects

  • Lack of a universal defect classification system hinders the development of automated inspection methods

To address these challenges, researchers have been investigating the use of supervised learning techniques for automated defect detection. However, supervised learning comes with its own set of limitations:

Requirement for large, labeled datasets

  • Supervised models need extensive datasets with clear examples of both defective and non-defective samples

  • Collecting and annotating such datasets is time-consuming, expensive, and disruptive to production

Limited generalization to unseen defect types

  • Models trained on specific defect types may struggle to identify novel or rare defects not represented in the training data

  • Adapting models to detect new defect types requires collecting additional labeled data and retraining


An Unsupervised Anomaly Detection Framework for Automated Fibre Placement Inspection

To address the challenges of manual inspection and supervised learning in Automated Fibre Placement (AFP) quality control, we propose an unsupervised anomaly detection framework. This approach leverages the inherent structure and uniformity of AFP-manufactured composite parts to identify defects without relying on a large labeled dataset.

The framework consists of the following key components:

Data preprocessing

  • Filtering noise from raw depth map images using a median filter

  • Normalizing depth values using min-max normalization to ensure consistent input data

Local sample extraction

  • Exploiting the uniformity along composite tows to generate a large dataset of local samples

  • Applying a sliding window approach to extract cropped regions along the center of each tow

Anomaly detection using a Convolutional Autoencoder (CAE)

  • Training the CAE on normal, non-defective samples to learn the inherent structure of AFP composite parts

  • Using reconstruction error as an anomaly score to identify potential defects

  • Generating an anomaly map by aggregating local anomaly scores across the entire composite part

Defect localization

  • Applying blob detection techniques, such as the Difference of Gaussian (DoG) method, on the anomaly map

  • Identifying the location and size of defects based on the detected blobs

The proposed framework offers several advantages over existing methods:

  • Ability to detect various types of defects without requiring labeled examples

  • Reduced reliance on large datasets, enabling effective learning from a limited number of non-defective samples

  • Improved generalization to unseen defect types, as the model learns the normal structure of AFP composite parts

  • Efficient defect localization through the use of anomaly maps and blob detection techniques


Effective Defect Detection and Localization in AFP Using Convolutional Autoencoders and Limited Training Data

The proposed unsupervised anomaly detection framework demonstrates effective performance in identifying and localizing defects in Automated Fibre Placement (AFP) manufactured composite parts. By leveraging Convolutional Autoencoders (CAEs) and a novel local sample extraction method, the framework achieves high accuracy and robustness despite the limited availability of training data.

Key results and findings:

Optimal latent space dimensionality

  • Experiments with different latent space dimensions (2, 16, and 128) reveal that a 16-dimensional latent space provides the best balance between reconstruction accuracy and anomaly detection performance

  • The CAE with a 16-dimensional latent space achieves a classification accuracy of 98.7% on the test set

Effective defect localization

  • The proposed blob detection method, based on the Difference of Gaussian (DoG) approach, accurately identifies the location and size of defects on the anomaly map

  • The detected bounding boxes achieve an average Intersection over Union (IoU) of 0.708 compared to the ground truth annotations

Advantages over existing methods

  • The unsupervised approach enables the detection of all types of surface anomalies, without the need for labeled defect examples

  • The framework requires fewer composite scans for training compared to supervised learning methods

  • The local sample extraction method allows for effective learning from a limited number of non-defective samples

The proposed framework addresses the key challenges faced by the AFP industry in terms of defect detection and quality control. By providing an accurate, efficient, and data-efficient solution, this approach has the potential to significantly improve the quality and reliability of AFP-manufactured composite parts.

Future research directions include:

  • Investigating data augmentation and synthetic data generation techniques to further improve the framework's performance

  • Integrating a defect classification module to categorize detected anomalies based on their type and severity

  • Adapting the framework to other industries with similar structured and uniform manufacturing processes



let's express our gratitude to the authors of the research paper for their valuable contributions that made this blog post possible:

We would like to extend our sincere thanks to Assef Ghamisi, Todd Charter, Li Ji, Maxime Rivard, Gil Lund, and Homayoun Najjaran for their groundbreaking work on unsupervised anomaly detection in Automated Fibre Placement. Their research, titled "Anomaly detection in automated fibre placement: learning with data limitations," has provided valuable insights and innovative solutions to address the challenges faced by the AFP industry in defect detection and quality control.

The authors' dedication and expertise have led to the development of a novel, end-to-end framework that effectively detects and localizes defects in AFP-manufactured composite parts, despite the limitations in available training data. Their work has the potential to revolutionize quality assurance processes in the AFP industry, ultimately improving the reliability and performance of composite components.

We are grateful for their significant contributions to the field and for sharing their knowledge through this research paper. Their work has inspired and informed the content of this blog post, and we hope that it will help disseminate their findings to a wider audience.

Once again, thank you Assef Ghamisi, Todd Charter, Li Ji, Maxime Rivard, Gil Lund, and Homayoun Najjaran for your outstanding research and your commitment to advancing the field of automated composite manufacturing.


What's Next!

Discover the future of composite manufacturing with Addcomposites! Here's how you can get involved:

  1. Stay Informed: Subscribe to our newsletter to receive the latest updates, news, and developments in AFP systems and services. Knowledge is power, and by staying informed, you'll always have the upper hand. Subscribe Now

  2. Experience Our Technology: Try our cutting-edge simulation software for a firsthand experience of the versatility and capability of our AFP systems. You'll see how our technology can transform your production line. Try Simulation 

  3. Join the Collaboration: Engage with us and other technical centers across various industries. By joining this collaborative platform, you'll get to share ideas, innovate, and influence the future of AFP. Join Collaboration 

  4. Get Hands-On: Avail our educational rentals for university projects or semester-long programs. Experience how our AFP systems bring about a revolution in composite manufacturing and leverage this opportunity for academic and research pursuits. Request for Educational Rental

  5. Take the Next Step: Request a quotation for our AFP systems. Whether you're interested in the AFP-XS, AFP-X, or SCF3D, we are committed to offering cost-effective solutions tailored to your needs. Take the plunge and prepare your production line for the next generation of composite manufacturing. Request Quotation

At Addcomposites, we are dedicated to revolutionizing composite manufacturing. Our AFP systems and comprehensive support services are waiting for you to harness. So, don't wait – get started on your journey to the future of manufacturing today!


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