基于深度学习的镍基高温合金叶片磨削烧伤识别研究

Research on Grinding Burn Identification of Nickel-based Superalloy Blades Based on Deep Learning

针对目视检查镍基高温合金叶片磨削烧伤时易出现误检、漏检等问题,提出一种基于深度学习的镍基高温合金叶片磨削烧伤识别分类模型(Tenon Grinding Burn Net,TenonG-BNet).以K4125镍基高温合金叶片为研究对象,通过磨削烧伤试验和试件组织检测获得不同烧伤程度的叶片榫齿磨削烧伤分级标准和对应的图片集;将ODConv动态卷积融合Inception V2模块和Coordinate Attention注意力机制,保证模型轻量化的同时提高模型的特征提取能力;使用全连接层进行识别分类.结果表明,与其他4个经典分类模型相比,TenonGBNet在具有较小的模型复杂度和参数量的同时保持了96.50%的平均分类准确率,且各烧伤等级的分类准确率均超过95%.

A deep learning-based recognition and classification model named Tenon Grinding Burn Net(TenonGBNet)is proposed to address the issues of misdiagnosis and missed detection in visual inspection of grinding burns on nickel-based Superalloy blades.The K4125 nickel-based superalloy blades are chosen as the target,and through grinding burn tests and specimen organization inspection,a set of classification standards and corresponding image collection for different degrees of blade tenon grinding burns are obtained.Then,ODConv dynamic convolution is employed to fuse Inception V2 modules and the Coordinate Attention mechanism to enhance the model's feature extraction capability while ensuring the model is lightweight.Finally,a fully connected layer is employed for identification and classification.Experimental results indicate that,compared with four other classical classification models,TenonGBNet achieves an average classification accuracy of 96.50%while maintaining a minor model complexity and parameter count.Additionally,the classification accuracy for each burn level exceeds 95%.