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彭翀 Chong Peng

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彭翀  Chong Peng

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

 pc1123@126.com

传真


个人主页

 

 

 

◆学习经历

2002-2006年 北京航空航天大学 机械制造及其自动化专业 博士

2000-2002年 北京航空航天大学 机械制造及其自动化专业 硕士

1992-1996年 南昌大学 工业电气自动化专业 学士

◆工作经历

2024.01至今 北京航空航天大学江西研究院

2007.03-2024.01 北京航空航天大学机械工程及自动化学院

2018.09-2020.06 北京航空航天大学发展规划部

2008.06-2010.05 北京航空航天大学人事处

1996.07-2000.08 江西南昌卷烟厂

◆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

◆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一等奖

l中国质量协会质量技术奖一等奖

◆开授课程

本科生课程:《制造工程基础——公差与互换性》

本科生课程:《制造技术及装备》

研究生课程:《加工动力学特性测试与铣削过程仿真优化实验》

◆代表论文和项目

lZhongyuan Che, Chong Peng, etc. KAN-Based tool wear modeling with adaptive complexity and symbolic interpretability in CNC turning processes[J]. Applied Sciences, 2025, 15(14): 8035.

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

l张忠文, 彭翀, 车众元等. 多阶段退化数据融合的伺服驱动单元可靠性建模[J]. 北京航空航天大学学报, 2025, 51(2): 692-704.

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

主要项目:

l自然科学基金,基于多模态数据的数控系统功能性能测评研究

l地方科技发展资金,数控装备智能化技术研究

l自然科学基金,基于部件退化动态耦合关系模型的数控系统可靠性研究

l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

l制造专项,国产高档数控系统可靠性第三方测试及可靠性增长研究

l制造专项,数控机床互联互通协议标准数据项定义

◆学术与社会服务

全国自动化系统与集成标委会物理设备控制分会

中国图学学会高级会员


                                                         

彭翀  Chong Peng

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

 pc1123@126.com

 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

lJan. 2024 – present: Jiangxi Research Institute, Beihang University, China

lMar. 2007 – Jan.2024: School of Mechanical Engineering and Automation, Beihang University, China

lSep. 2018 – Jun. 2020: Development Planning Office, Beihang University, China

lJun. 2008 – Jun. 2010: Human Resource Department, Beihang University, China

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

◆Research Interest

lManufacturing Equipment Reliability Test Technology

lmanufacturing process optimization modeling and application

◆Honours and Awards

lSupervisor of Outstanding ME Internship Achievement Awardee

lFirst Prize, CMISTA

lFirst Prize, CAQ Quality Technology Award‌‌

◆Teaching

lDynamics Testing of CNC Machine and Simulation of Milling Process

lFundamentals of Manufacturing Engineering — Geometric Tolerance‌

lProcessing Technology and Equipment

◆Publications

lZhongyuan Che, Chong Peng, etc. KAN-Based tool wear modeling with adaptive complexity and symbolic interpretability in CNC turning processes[J]. Applied Sciences, 2025, 15(14): 8035.

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.

lPENG C, ZHANG Z, LIU W, et al. Ranking of key components of CNC machine tools based on complex network[J]. Mathematical Problems in Engineering, 2022: 6031626.

lPENG C, CAI Y, LIU G, et al. Developing a reliability model of CNC system under limited sample data based on multisource information fusion[J]. Mathematical Problems in Engineering, 2020: 3645858.

lCHEN M, PENG C, WANG H, et al. Research on a new non-contact electromagnetic loading device and material performance of its load plate[C]//The 11th International Conference on Mathematical Methods in Reliability. Kowloon, Hong Kong: IEEE, 2019: 1-8.

lWU G, PENG C, LIAO T W. Research on edges immunization strategy for complex network based on SIS-CA model[J]. Procedia Manufacturing, 2018, 17: 1065-1072.

lPENG C, DU H, LIAO T W. A research on the cutting database system based on machining features and TOPSIS[J]. Robotics and Computer-Integrated Manufacturing, 2017, 43: 96-104

lLIU G, PENG C. Reliability modeling of repairable system based on a stochastic model[C]//Proceedings of 2016 Prognostics and System Health Management Conference (PHM 2016). Chengdu, China: IEEE, 2016: 1-7.

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

lPENG C, MENG Y. Empirical study of manufacturing enterprise collaboration network: Formation and characteristics[J]. Robotics and Computer-Integrated Manufacturing, 2016, 42: 49-62.

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, 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.

lChong Peng, Lun Wang, T W Liao. A prototype web based decision support system for cutting parameters selection based on machining features[C]//Proceedings of the 24th International Conference on Manufacturing and Enterprise Transformation (FAIM 2014). San Antonio, Texas, USA: DEStech, 2014: 1-8.

lChong Peng,, Liyun Lan, Qiang Liu, et al. Research on soft fault of computer numerical control system[J]. Chemical Engineering Transactions, 2013(33): 973-978.

lPENG C, LIU Q, PANG T. Remote monitoring and fault diagnosis system based on the integration of MAS and LONWORKS technology and internet[C]//2011 Prognostics and System Health Management Conference (PHM-Shenzhen 2011). Shenzhen, China: IEEE, 2011: 1-6.