张艳飞,张永志,白格滔,王英军,刘孝,赵晓春
ZHANG Yanfei,ZHANG Yongzhi,BAI Getao,WANG Yingjun,LIU Xiao,ZHAO Xiaochun

• 1:内蒙古电力(集团)有限责任公司内蒙古电力科学研究院分公司
• 2:内蒙古农业大学机电工程学院

摘要(Abstract)：

针对火电机组用钢金相组织评级受人为因素影响，效率低、重复性差等问题，提出建立金相检验图像样本数据集，采用ConvNeXt-T卷积神经网络模型研究基于深度学习的火电机组用钢金相组织评级方法，同时用混淆矩阵对所建模型的性能进行评估，模型对铁素体+珠光体组织球化评级的准确率均值为98.7%、精确度均值为97.3%、灵敏度均值为97.2%、特异度均值为99.1%、F_(1-Score)均值为97.2%，表明该方法能够对火电机组用钢金相组织进行较为准确的评级，提升评级效率，为火电机组用钢金相组织智能评级提供一种新的方法，同时助力电力行业金相检验向数字化、智能化发展。
In response to the issues of susceptibility to human factors, low efficiency, and poor repeatability in metallographic structure rating, this paper uses metallographic images to establish a sample dataset, and uses ConvNeXt-T convolutional neural network model to study the deep learning based metallographic structure rating method for steel used in thermal power units. At the same time, the performance of the constructed model on the validation set is evaluated using a confusion matrix. The average accuracy rate, precision, sensitivity, specificity, and F_(1-Score) of the model for spheroidization rating of ferrite and pearlite structure are 98.7%, 97.3%, 97.2%, 99.1%, and 97.2%, respectively, which indicates that this method can accurately grade the metallographic structure of steel used in thermal power units, overcome human factors, improve rating efficiency, and form an objective evaluation, and provide a new method for intelligent grading of metallographic structure of steel used in thermal power units, and assist the power industry in moving towards digitalization and intelligence in metallographic examination.

关键词(KeyWords)： 金相组织;智能评级;深度学习;卷积神经网络
metallographic structure;intelligent rating;deep learning;convolutional neural network

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目“基于深度学习的火电厂的耐热钢显微组织金相图像分割方法研究”（52061037）;; 内蒙古电力（集团）有限责任公司内蒙古电力科学研究院分公司自筹科技项目“基于深度学习的金属监督设备金相组织智能评判研究”（2022-ZC-05）

作者(Author): 张艳飞,张永志,白格滔,王英军,刘孝,赵晓春
ZHANG Yanfei,ZHANG Yongzhi,BAI Getao,WANG Yingjun,LIU Xiao,ZHAO Xiaochun

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