陈龙鑫

个人信息

参与实验室科研项目

人机智能协同关键技术及其在智能制造中的应用

非可信智能驱动的可靠智造

学术成果

共撰写/参与撰写专利 2 项，录用/发表论文 2 篇，投出待录用论文0篇。

patent

1. 锡膏印刷机在线故障预测软件V1.0 赵云波, 陈龙鑫, 朱慧娟, 康宇, and 许镇义 2022 [Abs] [pdf]

   本软件系统用于锡膏印刷机的在线故障预测，通过 url 接口实时获取锡膏印刷机 的状态数据，调用事先训练好的机器学习模型进行故障预测，并将故障预测结果 展示在前端可视化界面，以辅助工程师及时进行维护。

2. 故障诊断模型的训练方法、装置、电子设备及存储介质 赵云波, 陈龙鑫, 刘斌琨, 朱慧娟, 许镇义, and 柏鹏 [Abs]

   本申请公开了一种故障诊断模型的训练方 法、装置、电子设备及存储介质，属于计算机技术 领域。所述方法包括：获取多个工况中各工况集 下的故障数据，分别作为源域数据，并获取目标 工况下的故障数据，作为目标域数据，所述工况 集包括所述多个工况中的至少一个工况，所述工 况集下的故障数据为已标记故障类别的数据，所 述目标工况下的故障数据为未标记故障类别的 数据；确定所述目标域数据与各所述源域数据之 间的目标分布差异；根据所述目标分布差异选取 源域数据作为训练数据；根据所述训练数据，对 所述目标工况的故障诊断模型进行训练。本申请 能够提高模型训练效果，进而提高对目标工况进 行故障诊断的准确率。

Conference Articles

1. A Reliable Ensemble Model Based on Hierarchical Component Features for Repair Label Prediction of Soldering Defects Longxin Chen, Yunbo Zhao, Binkun Liu, Shaojie Dong, Huijuan Zhu, and Peng Bai In 2024 [Abs] [pdf]

   Accurate prediction for repair labels of soldering defective printed circuit board (PCB) components can help lower labor costs, using solder paste inspection (SPI) and automated optical inspection (AOI) data. Existing research tries to pick out both the false defect components (actually good) and impossibleto-repair components among defective PCB components, using SPI and AOI data. However, it is inappropriate to pick out the false defect components from screened components using defective information of AOI data. So the problem setting of existing research is inappropriate, resulting in the algorithm’s performance not meeting actual requirements. To address this problem, we only care about the reliable prediction of impossibleto-repair components. We propose a hierarchical component feature extraction method that can comprehensively characterize the degree of component defects from multiple levels, including pin level and component level, and apply ensemble model based on XGBoost and TabNet to the reliable prediction of impossibleto-repair components by adjusting the probability threshold of components judged as impossible-to-repair category. Finally, we validated our method on real datasets and achieved the best experimental results compared to baseline methods, which can meet actual requirements.

2. Prediction of Yield in Functional Testing of Motherboards in Laptop Manufacturing Shao-Jie Dong, Yun-bo Zhao, Yu Kang, Kang-cheng Wang, Long-xin Chen, and Peng Bai In 2024 [Abs] [pdf]

   Functional testing stands as a pivotal quality control step in the production process of laptop motherboards, aiming to validate the proper functioning of various components. However, due to the multitude of functional modules involved on the motherboard, testing all of them requires a significant amount of time and resources. As a result, production line engineers often rely on empirical selection of modules with low yield rates for testing. However, such empirical yield estimation is often inaccurate. To address this challenge, this study proposes a hybrid model based on XGBoost and Long Short-Term Memory (LSTM) networks to predict the yield of each functional module. By harnessing the feature learning capability of XGBoost and the sequential modeling power of LSTM, this model efficiently explores the intricate correlations among motherboard functional modules, thereby accurately forecasting their yields. We extensively train and validate the model using historical production data and successfully deploy it on real laptop motherboard production lines. Experimental results demonstrate that our hybrid model accurately predicts the yield of each functional module, providing crucial guidance for the functional testing process. Through in-depth analysis of the predicted yield results, engineers can systematically choose testing projects to save time and resources. This research offers a novel approach and pathway for enhancing motherboard production efficiency and quality.