使用深度学习CNN自动评估铜合金晶粒尺寸
Automated Copper Alloy Grain Size Evaluation Using a Deep-learning CNN
摘要：
穆格公司使用深度学习卷积神经网络（CNN）自动评估铜（Cu）合金晶粒尺寸。概念验证自动化图像采集和批量图像处理提供了显著减少劳动力、提高晶粒评估准确性和减少批准用于飞行关键飞机硬件的铜合金棒料的总体周转时间的潜力。对Cu合金试样的各个子图像的分类准确率达到91.1%。过程开发包括最小化所获取的图像颜色、亮度和分辨率的变化，以创建具有12300个子图像的数据集，然后使用统计实验设计（DoE）优化该数据集上的CNN超参数。在自动化Cu合金晶粒尺寸评估的开发过程中，基于将大的原始图像分解成许多较小的数据集子图像，通过经由检查来自各个较小子图像的分类结果来解释CNN整体图像输出的能力，实现了人工智能（XAI）输出中的一定程度的“可解释性”。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
