出生年月

1974年11月

籍贯

江西樟树

职称

 教授

学历

博士

电话

办公室

3号楼335

系别

机械制造及其自动化

职务

江西研究院院长

电子信箱

 pch@buaa.edu.cn

传真

个人主页

◆学习经历

l2002-2006年 北京航空航天大学 机械制造及其自动化专业 博士

l2000-2002年  北京航空航天大学 机械制造及其自动化专业 硕士

l1992-1996年   南昌大学 工业电气自动化专业 学士

◆工作经历

l2007.03-至今 北京航空航天大学 机械工程及自动化学院

l2024.01-至今 北京航空航天大学 江西研究院

l2018.09-2020.06北京航空航天大学 发展规划部

l2008.06-2010.05 北京航空航天大学 人事处

◆研究领域

l数控装备可靠性技术

l制造过程优化建模及应用

l网络化制造服务系统

◆荣誉及奖励

l全国第三届“工程硕士实习实践优秀成果获得者”指导教师

l中国机械工业科学技术奖科技进步特等奖

l中国机械工业科学技术奖科技进步一等奖

l中国质量协会质量技术奖一等奖

◆开授课程

l本科生课程：《制造工程基础——公差与互换性》

l本科生课程：《先进加工技术及装备》

l留学生课程：《Advanced Processing Technology and Equipments》

l研究生课程： 《数控机床加工动力学特性测试与铣削过程仿真优化实验》

◆教学及科研成果

代表论文：

lChong Peng, Zhongwen Zhang, etc.. A hybrid approach for the dynamic flexible job shop scheduling problem considering machine failures. Journal of Scheduling, Volume:28, Issue4, 2025:407-424.

lZhongyuan Che, Chong Peng, etc.. A Novel Integrated TDLAVOA-XGBoost Model for Tool Wear Prediction in Lathe and Milling Operations. Results in Engineering. online 25 June 2025, 105984.

lZhongyuan Che, Chong Peng, etc.. Improving Milling Tool Wear Prediction through a Hybrid NCA-SMA-GRU Deep Learning Model. Expert Systems with Applications, 255(2024): 124556

lZhongyuan Che, Chong Peng, Chenxiao Yue. Optimizing LSTM with multi-strategy improved WOA for robust prediction of high-speed machine tests data. Chaos, Solitons & Fractals, 178, 2024: 114394

lChong Peng, Zhongyuan Che, etc.. Prediction using multi-objective slime mould algorithm optimized support vector regression model. Applied Soft Computing, 145, 2023: 110580

lXie Bin, Peng Chong, Wang Yanzhong. Combined relevance vector machine technique and subset simulation importance sampling for structural reliability. Applied Mathematical Modelling, 2023, 113: 129-143.

主要项目：

l自然科学基金面上项目，基于多模态数据与人工智能的数控系统功能性能测评研究（2026-2029）

l国家重大科研仪器研制项目，基于跨尺度宏微协同的超精密空间光机跟瞄仪（2023-2027）

l自然科学基金面上项目，基于部件退化动态耦合关系模型的数控系统多源信息融合可靠性研究（2019-2022）

l国家智能制造专项，数控装备故障信息数据字典标准研制及试验验证（2017-2020）

l国家科技重大专项，国产高档数控系统可靠性第三方测试及可靠性增长研究（2016-2019）

l国家智能制造专项，数控机床互联互通协议标准数据项定义（2016-2018）

◆学术与社会服务

l全国自动化系统与集成标准化技术委员会物理设备控制分技术委员会委员

Date of Birth

Nov 23, 1974

Place of Birth

Zhangshu, Jiangxi, China

Academic title

 Professor

Degree

Phd

Office

3#335

Department

Mechanical Manufacture and Automation

Email

 pch@buaa.edu.cn

 Homepage

◆Education

lSep. 2000 – Nov. 2006: Doctor of Engineering, in Mechanical Engineering, Beihang University, Beijing, China

lSep. 1992 – Jul. 1996: Bachelor of Engineering, in Electric Automation, Nanchang University, Nanchang, China

◆Work Experience

lMar. 2007 - present: School of Mechanical Engineering and Automation, Beihang University, China

lMar. 2007 – Mar. 2010: Human Resource Department, Beihang University, China

lJul. 1996 – Aug. 2000: Nanchang Cigarette Factory, China

◆Research Interest

lManufacturing Equipment Reliability Test Technology

lmanufacturing process optimization modeling and application

◆Honours and Awards

◆Teaching

lDynamics Testing of CNC Machine and Simulation of Milling Process （for graduate student）

lFundamentals of Manufacturing Engineering （for undergraduate student）

lAdvanced Processing Technology and Equipments （for undergraduate student and international student）

◆Publications

lChong Peng, Zhongwen Zhang, etc.. A hybrid approach for the dynamic flexible job shop scheduling problem considering machine failures. Journal of Scheduling, Volume:28, Issue4, 2025:407-424.

lZhongyuan Che, Chong Peng, etc.. A Novel Integrated TDLAVOA-XGBoost Model for Tool Wear Prediction in Lathe and Milling Operations. Results in Engineering. online 25 June 2025, 105984.

lZhongyuan Che, Chong Peng, etc.. Improving Milling Tool Wear Prediction through a Hybrid NCA-SMA-GRU Deep Learning Model. Expert Systems with Applications, 255(2024): 124556

lZhongyuan Che, Chong Peng, Chenxiao Yue. Optimizing LSTM with multi-strategy improved WOA for robust prediction of high-speed machine tests data. Chaos, Solitons & Fractals, 178, 2024: 114394

lChong Peng, Zhongyuan Che, etc.. Prediction using multi-objective slime mould algorithm optimized support vector regression model. Applied Soft Computing, 145, 2023: 110580

lXie Bin, Peng Chong, Wang Yanzhong. Combined relevance vector machine technique and subset simulation importance sampling for structural reliability. Applied Mathematical Modelling, 2023, 113: 129-143.

lChong Peng, YuJie Meng. Empirical study of manufacturing enterprise collaboration network: Formation and characteristics. Robotics and Computer-Integrated Manufacturing, 2016, 42: 49-62.

lChong Peng, Guangpeng Li, Lun Wang. Piecewise modelling and parameter estimation of repairable system failure rate, SpringerPlus, 2016, 5:1477

lChong Peng, Lun Wang, T. Warren Liao. A new method for the prediction of chatter stability lobes based on dynamic cutting force simulation model and support vector machine. Journal of Sound and Vibration, 2015, 354: 118-131.

lChong Peng, Hanheng Du, T. Warren Liao. A research on the cutting database system based on machining features and TOPSIS. Robotics and Computer-Integrated Manufacturing, http://dx.doi.org/10.1016/j.rcim.2015.10.011.

lChong Peng, Yujie Meng, Wei Guo. Influence of Laser Shock Processing on WC–Co Hardmetal. Materials and Manufacturing Processes, 2015,31(6): 794-801.

lChong Peng, Lun Wang, Zhongqun Li, Yiqing Yang. Time-domain simulation and experimental verification of dynamic cutting forces and chatter stability for circular corner milling. Part B: Journal of Engineering Manufacture, 2015, 229(6): 932-939.