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Mastering Composite Defect Detection with AddPath: Streamlining AFP Processes Through AI

Updated: May 17

I. Introduction

Automated Fiber Placement (AFP) is an additive composite manufacturing technique that has gained popularity in recent years due to its ability to produce complex geometries with high precision and efficiency. However, a pressing challenge facing this technology is defect detection and repair. Manual defect inspection is time-consuming and can be prone to errors, which can lead to costly rework or even part failure. To address this issue, researchers have been exploring automated methods of defect detection using machine learning techniques such as convolutional neural networks (CNNs). In this blog post, we will explore how CNNs can be used for automated defect detection in AFP and how the AddPath software's new defect detection feature can help streamline this process.

II. AddPath's Updated Defect Detection Feature

AddPath is a software platform that provides end-to-end solutions for composite manufacturing processes, including AFP. The software offers a range of features such as part design, toolpath generation, and process simulation. Recently, AddPath has updated its platform with a new feature that allows for automated defect detection in AFP using CNNs, incorporating both traditional and live mode options.

The updated defect detection feature in AddPath works by analyzing the AFP scan point cloud and images using a pre-trained CNN model. Users can either upload their scan data to the software or directly stream them from the AFP system running in live mode. The defect detection analysis can be run with just a few clicks, and the software then highlights any defects found in the scan point cloud, providing detailed information about their location, size, and type.

One of the key advantages of using AddPath's updated defect detection feature is its speed, accuracy, and real-time capabilities. The CNN model used by the software has been trained on large datasets of AFP scan images with labeled defects, which allows it to quickly and accurately identify defects in new scan images. This can save manufacturers significant time and resources compared to manual inspection methods. Moreover, the live mode option enables operators to monitor and address defects in near real-time, further enhancing the efficiency and quality of the manufacturing process.

III. Understanding Convolutional Neural Networks (CNNs) in AddPath

Convolutional Neural Networks (CNNs) are a type of deep learning algorithm that are commonly used for image processing and computer vision tasks. CNNs are designed to automatically learn and extract features from images, which makes them well-suited for tasks such as object reco