论文标题
计算机视觉算法，用于预测其微观结构的摩擦搅拌焊接铜接头的焊接效率
Computer Vision Algorithm for Predicting the Welding Efficiency of Friction Stir Welded Copper Joints from its Microstructures
论文作者
论文摘要
摩擦搅拌焊接是一个强大的连接过程，并且在该领域开发了许多基于AI的算法，以增强机械和微结构性能。卷积神经网络（CNN）是使用图像数据作为输入的人工神经网络。它们与人工神经网络相同，由在整个学习过程中确定的权重，神经元（激活功能）和目标（损失函数）组成。 CNN用于多种应用中，包括图像识别，语义分割，图像识别和本地化。当前工作利用3000张微观结构图片的培训和300张微观结构照片的新测试，研究了使用微观结构图像的摩擦搅拌焊接关节有效性的预测。
Friction Stir Welding is a robust joining process, and numerous AI-based algorithms are being developed in this field to enhance mechanical and microstructure properties. Convolutional Neural Networks (CNNs) are Artificial Neural Networks that use image data as input. Identical to Artificial Neural Networks, they are composed of weights that are determined throughout learning, neurons (activated functions), and a goal (loss function). CNN is utilized in a variety of applications, including image recognition, semantic segmentation, image recognition, and localization. Utilizing training on 3000 microstructure pictures and new tests on 300 microstructure photographs, the current work investigates the predictions of Friction Stir Welded joint effectiveness using microstructure images.
