论文标题
使用深度学习的CNN自动化铜合金粒度评估
Automated Copper Alloy Grain Size Evaluation Using a Deep-learning CNN
论文作者
论文摘要
Moog Inc.使用深度学习卷积神经网络（CNN）自动化了铜（CU）合金晶粒尺寸的评估。概念验证自动化图像采集和批处理图像处理可显着减少人工，提高谷物评估的准确性，并减少整体周转时间，以批准用于飞行临界飞机硬件的Cu Alloy Bar Stock。获得了CU合金优惠券的单个子图像的分类精度为91.1％。过程开发包括最大程度地减少获得的图像颜色，亮度和分辨率的变化，以创建具有12300个子图像的数据集，然后使用实验的统计设计（DOE）优化该数据集上的CNN超参数。 在自动化铜合金晶粒尺寸评估的开发中，基于大型原始图像将大型原始图像分解为许多较小的数据集子图像，通过从单个较小的子图中的检查结果来解释CNN集合图像输出，实现了人工智能（XAI）输出的一定程度的“解释性”。
Moog Inc. has automated the evaluation of copper (Cu) alloy grain size using a deep-learning convolutional neural network (CNN). The proof-of-concept automated image acquisition and batch-wise image processing offers the potential for significantly reduced labor, improved accuracy of grain evaluation, and decreased overall turnaround times for approving Cu alloy bar stock for use in flight critical aircraft hardware. A classification accuracy of 91.1% on individual sub-images of the Cu alloy coupons was achieved. Process development included minimizing the variation in acquired image color, brightness, and resolution to create a dataset with 12300 sub-images, and then optimizing the CNN hyperparameters on this dataset using statistical design of experiments (DoE). Over the development of the automated Cu alloy grain size evaluation, a degree of "explainability" in the artificial intelligence (XAI) output was realized, based on the decomposition of the large raw images into many smaller dataset sub-images, through the ability to explain the CNN ensemble image output via inspection of the classification results from the individual smaller sub-images.
