刘长青，博士，教授，博士生导师。江苏特聘教授，入选国家重大人才工程青年项目。主要研究领域为航空航天复杂结构件数字化制造与智能制造，面向航空航天大型复杂构件高精度高效率制造需求，探索人工智能技术与加工技术的深度融合，凝练智能数控加工中的关键科学问题，研究数据与物理知识融合的智能数控加工新原理与新方法，研发加工过程关键物理量监测与智能控制工艺装备。

受邀担任国际学术期刊英国工程技术学会期刊Collaborative Intelligent Manufacturing (IET CIM， EI、ESCI收录) Associate Editor。担任《机械科学与技术》和《Journal of Advanced Manufacturing Science and Technology》期刊编委，担任《Visual Computing for Industry, Biomedicine, and Art》（VCIBA，EI、ESCI收录）、《南京航空航天大学学报》（中英文两刊，英文EI收录）和《制造业自动化》青年编委。

担任高效精密加工与装备技术教育部工程研究中心副主任。

担任中国图学学会智能工厂专委会秘书长、中国图学学会青年工作委员会副主任委员、中国机械工程学会极端制造分会委员、中国机械工程学会机械工业自动化分会委员和中国自动化学会制造技术专业委员会委员等职务。

主持国家自然科学基金面上项目2项、国家重点研发计划项目课题1项、“高档数控机床与基础制造装备”国家科技重大专项子课题2项，主持智能制造专项子课题1项、民机专项子课题2项，主持校教学改革项目3项。

发表SCI收录学术论文共40余篇，获国家发明专利授权20余项，发布团体标准3项。

出版教材一部：《航空航天智能数控加工技术》。

江苏省优秀博士学位论文获得者。

2016年获国家技术发明二等奖1项，个人排名第三。

2019年获四川省科技进步一等奖1项，个人排名第五。

指导学生获“互联网+”创新创业大赛获全国金奖3项，个人获“互联网+”创新创业优秀导师奖。

代表性论文：

[1]      Changqing Liu, Yingguang Li*, Jiaqi Hua, Zhiwei Zhao, James Gao. A causal based method for denoising non-homologous noises in time series manufacturing monitoring data[J]. Journal of Manufacturing Systems, 2024, 76: 92-102.(SCI, EI)

[2]      Changqing Liu, Yingguang Li*, Chong Huang, Yujie Zhao, Zhiwei Zhao. A meta-reinforcement learning method by incorporating simulation and real data for machining deformation control of finishing process[J]. International Journal of Production Research, 2022: 1-15. (SCI, EI)

[3]      Changqing Liu, Yingguang Li*, Jingjing Li, Jiaqi Hua. A meta-invariant feature space method for accurate tool wear prediction under cross-conditions[J]. IEEE Transactions on Industrial Informatics, 2022, 18(2): 922-931. (SCI, EI)

[4]      Changqing Liu*, Jincheng Ni, Peng Wan. An accurate prediction method of multiple deterioration forms of tool based on multitask learning with low rank tensor constraint[J]. Journal of Manufacturing Systems, 2021, 58(193-204). (SCI, EI)

[5]      Changqing Liu, Yingguang Li*, Qiang Wang, WenpingMou. A synchronous association approach of geometry, process and monitoring information for intelligent manufacturing[J]. Robotics and Computer-Integrated Manufacturing, 2019, 58: 120-129. (SCI, EI)

[6]      Changqing Liu, Yingguang Li*, Zhongyu Li. A machining feature definition approach by using two-times unsupervised clustering based on historical data for process knowledge reuse[J]. Journal of manufacturing systems, 2018, 49: 16-24. (SCI, EI)

[7]     Zhiwei Zhao, Changqing Liu, Yingguang Li*, James Gao. A New Method for Inferencing and Representing a Workpiece Residual Stress Field Using Monitored Deformation Force Data[J]. Engineering, 2022. (SCI, EI)

[8]   JiaQi Hua, Yingguang Li *, Changqing Liu, Peng Wan, Xu Liu. Physics-Informed Neural Networks With Weighted Losses by Uncertainty Evaluation for Accurate and Stable Prediction of Manufacturing Systems[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023. (SCI, EI)

[9]   Yingguang Li*, Changqing Liu, Xiaozhong Hao, James X. Gao, Paul G. Maropoulos. Responsive fixture design using dynamic product inspection and monitoring technologies for the precision machining of large-scale aerospace parts[J]. CIRP Annals, 2015, 64(1): 173-176. (SCI, EI)