代表性论著论文（近三年）

 

  1. 周光辉，张超  著（2019）《高端装备制造企业知识资源集成服务方法与应用》,  北京：科学出版社, 2019年1月 (学术专著)
  2. 张超，周光辉，李晶晶等(2022) 面向航空复杂零件智能工艺规划的孪生工艺模型构建与应用研究.机械工程学报
  3. 张超，周光辉，李晶晶等(2022) 新一代信息技术赋能的数字孪生制造单元系统关键技术及应用研究.机械工程学报
  4. Zhou GH*, Chen ZH, Zhang C and Chang FT (2022) “An adaptive ensemble deep forest based dynamic scheduling strategy for low carbon flexible job shop under recessive disturbance”, J. Cleaner Production. 337: 130541. DOI: 10.1016/j.jclepro.2022.130541 (SCI: 1W5RG; EI: 20220411491532; IF= 11.072)
  5. Lu FY, Zhou GH*, Liu Y and Zhang C (2022) “Ensemble transfer learning for cutting energy consumption prediction of aviation parts towards green manufacturing”, J. Cleaner Production. 331: 129920. DOI: 10.1016/j.jclepro.2021.129920. (SCI: ZW9XB; EI: 20215011302670; IF=11.072)
  6. Li JJ, Zhou GH*, Zhang C (2022) “A twin data-and knowledge-driven intelligent process planning framework of aviation parts”, Int. J. Production Research, 60(17): 5217-5234. DOI: 10.1080/00207543.2021.1951869.  (SCI: TL41G; EI: 20213010671250; IF=9.018)
  7. Chang FT, Zhou GH*, Zhang C, Ding K and Cheng W (2021) “A maintenance decision-making oriented collaborative cross-organization knowledge sharing blockchain network for complex multi-component system”, J. Cleaner Production. 282: 124541. DOI: 10.1016/j.jclepro.2020.124541. (SCI: PS7PZ; EI: 20204309382613; IF=11.072)
  8. Tian CL, Zhou GH*, Lu FY, Chen ZH, and Zou L (2021) “Cooperative optimization of cutting parameters, process routes and scheduling considering carbon emissions with analytic target cascading”, The Int. J. Advanced Manufacturing Technology. 114(1): 605-623. DOI: 10.1007/s00170-021-06755-7. (SCI: QZ3WD; EI: 2021131049458; IF=3.226)
  9. Zhang C, Zhou GH*, Li H, and Cao Y (2020) “Manufacturing blockchain of things for the configuration of a data- and knowledge-driven digital twin manufacturing cell”, IEEE Internet of Things Journal, 7(12): 11884-11894. DOI: 10.1109/JIOT.2020.3005729. (SCI: PD6HE; EI: 20205109665714; IF=10.238)
  10. Zhang C，Zhou GH*, Yang HD, Yang XJ, and Xiao ZD (2020) “View-based 3D CAD model retrieval with deep residual networks”, IEEE Transactions on Industrial Informatics, 16(4): 2335-2345. DOI: 10.1109/TII.2019.2943195. (SCI: KH8LB; EI: 2020050810633; IF= 11.648)
  11. Zhang C，Zhou GH*, Hu JS, and Li J (2020) “Deep learning-enabled intelligent process planning for digital twin manufacturing cell”, Knowledge-based System, 191: 105247. DOI: 10.1016/j.knosys.2019.105247. (SCI: KR5MT; EI: 20195107875103; IF= 8.139)
  12. Zhou GH*, Zhang C, Lu FY, and Zhang JJ (2020) “Integrated optimization of cutting parameters and tool path for cavity milling considering carbon emissions”, J. Cleaner Production, 250: 119454. DOI: 10.1016/i.jclepro.2019.119454. (SCI: KE8WO; EI: 20195007829049; IF= 11.072)
  13. Zhang C, Zhou GH*, Chang FT, and Yang XJ (2020) “Learning domain ontologies from engineering documents for manufacturing knowledge reuse by a biologically inspired approach”, Int. J. Advanced Manufacturing Technology,106(5-6): 2535-2551. DOI: 10.1007/s00170-04772-1. (SCI: JY0SP; EI: 20200107969108; IF=3.226)
  14. Zhou GH*, Zhang C, Li Z, Ding K and Wang C (2020) “Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing”, Int. J. Production Research, 58(4): 1034-1051. DOI: 10.1080/00207543.2019.1607978. (SCI: IB9UQ; EI: 20191906892830; IF=9.018)
  15. Wang C, Zhou GH, and Zhu ZX (2020) “Service perspective based production control system for smart job shop under industry 4.0”, Robotics and Computer-Integrated Manufacturing, 65: 101954. DOI: 10.1016/j.rcim.2020.101954. (SCI: JY0SP; EI: 20201108289536; IF=10.103)
  16. Chai QF, Xiao ZD, and Zhou GH (2020) “Competitive strategies for original equipment manufacturers considering carbon cap and trade”, Transportation Research Part D: Transport and Environment, 78: 102193. DOI: 10.1016/j.trd.2019.11.022. (SCI: KM3IJ; EI: 20195007824350; IF=4.051)
  17. Chang FT, Zhou GH*, Cheng W, Zhang C and Tian CL (2019) “A service-oriented multi-player maintenance grouping strategy for complex multi-component system based on game theory”, Advances Engineering Informatics, 42: 100970. DOI: 10.1016/j.aei.2019.100970. (SCI: JU0SH; EI: 20193407333079; IF=3.772)
  18. Chang FT, Zhou GH*, Zhang C and Xiao ZD (2019) “A service-oriented dynamic multi-level maintenance grouping strategy based on prediction information of multi-component systems”, J. Manufacturing Systems, 53: 49-61. DOI: 10.1016/j.jmsy.2019.09.005. (SCI: JS3PO; EI: 20193907464640; IF=3.642)
  19. Tian CL，Zhou GH*, Lu Q, Xiao ZD, Zhang JJ, and Wang R (2019) “An integrated decision-making approach on cutting tools and cutting parameters for machining features considering carbon emissions”, Int. J. Computer Integrated Manufacturing, 32(7): 629-641. DOI: 10.1080/0951192X.2019.1610575. (SCI: ID1QW; EI: 20192006921140; IF=2.09)
  20. Zhou GH*, Yang XJ, Zhang C, Li Z and Xiao ZD (2019) “Deep learning enabled cutting tool selection for special-shaped machining features of complex products”, Advances in Engineering Software, 133: 1-11. DOI: 10.1016/j.advengsoft.2019.04.007. (SCI: HZ31F; EI: 20191706813747; IF=4.194)
  21. Tian CL，Zhou GH*, Zhang JJ, and Zhang C (2019) “Optimization of cutting parameters considering tool wear condition in low-carbon manufacturing environment”, J. Cleaner Production, 226: 706-719. DOI: 10.1016/j.jclepro.2019.04.113. (SCI: IA9UA; EI: 20191706813279; IF=6.395)
  22. Cheng W*, Chu YP, Chen XF, Zhou GH, Gao L, et al. (2019) “A combined crosstalk cancellation method based on wavelet packet denoising and welch method for operational transfer path analysis”, Measurement Science and Technology, 30(6): 065011. DOI: 10.1088/1361-6501/ab0787. (SCI: HX9DB; EI: 20192607088273; IF=1.861)
  23. Lu Q，Zhou GH*, Zhao F, Ren YP, Li L, Luan XN and Sutherland John W. (2019) “Topology optimization of oilstone components considering carbon emissions”, J. Cleaner Production, 225: 181-195. DOI: 10.1016/j.jclepro.2019.03.273. (SCI: HZ2WL; EI: 20191606793864; IF=6.395)
  24. Ding K*, Chan FTS, Zhang XD, Zhou GH, Zhang FQ, (2019) “Defining a digital twin-based cyber-physical production system for autonomous manufacturing in smart shop floor”, Int. J. Production Research, Published online. DOI: 10.1080/00207543.2019.1566661. (SCI: ***; EI: 20190406407530; IF=3.199)
  25. Lu Q，Zhou GH*, Zhao F, Li L, and Sutherland John W. (2019) “Determination of the shape and distribution parameters for abrasive grains to reduce carbon emissions”, J. Manufacturing Science and Engineering -Transactions of the ASME, 141: (2): 021008-021008-14. DOI: 10.1115/1.4041481. (SCI: HG8AU; EI: 20190206347726; IF=2.578)
  26. Zhou GH*, Lu Q, Xiao ZD, Zhou C, and Tian CL (2019) “Cutting parameter optimization for machining operations consider carbon emissions”, J. Cleaner Production, 208: 937-950. DOI: 10.1016/j.jclepro.2018.10.191. (SCI: HB8SY; EI: 20184506049698; IF=6.395)
  27. Zhang C，Zhou GH* (2019) “A view-based 3D CAD model reuse framework enabling product lifecycle reuse”, Advances in Engineering Software, 127: 82-89. DOI: 10.1016/j.advengsoft.2018.09.001. (SCI: HD1XZ; EI: 20184706089283; IF=4.194)
  28. Gao JZ, Xiao ZD*, Wei HX, and Zhou GH (2018) “Active or passive? Sustainable manufacturing in the direct-channel green supply chain: A perspective of two types of green product designs”, Transportation Research Part D – Transport and Environment, 65: 332-354. DOI: 10.1016/j.trd.2018.09.007. (SCI: HE7OB; EI: 20183805845721; IF=4.051)
  29. Chang FT, Zhou GH*, Xiao XP, Tian CL and Zhang C (2018) “A function availability-based integrated product-service network model for high-end manufacturing equipment”, Computers & Industrial Engineering, 126: 302-316. DOI: 10.1016/j.cie.2018.09.043 (SCI: HA6GE; EI: 20184005904320; IF=3.518)
  30. Chai QF, Xiao ZD*, Lai KH, and Zhou GH (2018) “Can carbon cap and trade mechanism be beneficial for remanufacturing?”, Int. J. Production Economics, 203: 311-321. DOI: 10.1016/ j.ijpe.2018.07.004. (SCI: GT2UZ; EI: 20183805825516; IF=4.998)
  31. Zhou GH*, Yuan SZ, Lu Q, and Xiao XP (2018) “A carbon emission quantitation model and experimental evaluation for machining process considering tool wear condition”, Int. J. Advanced Manufacturing Technology, 98(1-4): 565-577. DOI: 10.1007/s00170-018-2281-6. (SCI: GR4LX; EI: 20182505330729; IF=2.495)
  32. Zhang C, Zhou GH*, Lu Q, Chang FT (2018) “Generating significant subassemblies from 3D assembly models for design reuse”, Int. J. Production Research, 56(14): 4744-4761. DOI: 10.1080/00207543.2018.1465608. (SCI: GS7MG; EI: 20181805119640; IF=3.199)
  33. Zhou GH*, Chang FT, Tian CL and Lu Q (2018) “A reliability optimization allocation method for hydraulic system of CNC honing machine considering operating condition information”, Proceedings of the Institution of Mechanical Engineers, Part C: J. Mechanical Engineering Science, 232(12): 2244-2264. DOI: 10.1177/0954406217719475 (SCI: GI7SW; EI: 20181605032837; IF=1.359)
  34. Zhou GH*, Tian CL, Lu Q, Chang TF, and Zhou C (2018) “Multi-objective process route optimization considering carbon emissions”, Int. J. Advanced Manufacturing Technology, 96(1-4): 1195-1213. DOI: 10.1007/s00170-018-1646-1 (SCI: GB1FV; EI: 20180704791001; IF=2.496)
  35. Lu Q, Zhou GH*, Zhou C and Xiao ZD (2018) “A selection methodology of key parts based on the characteristic of carbon emissions for low-carbon design”, Int. J. Advanced Manufacturing Technology, 94(9-12): 3359-3373. DOI: 10.1007/s00170-017-0522-8 (SCI: FW8NM; EI: 20172203701936; IF=2.496)
  36. Zhou GH*, Zhou C, Lu Q, Tian CL and Xiao ZD (2018) “Feature-based carbon emission quantitation strategy for the part machining process”, Int. J. Computer Integrated Manufacturing, 31(4-5): 406-425. DOI: 10.1080/0951192X.2017.1328561 (SCI: FU3NO; EI: 20172203703423; IF=2.09)
  37. Zhang C, Zhou GH*, Lu Q, Chang FT (2017) “Graph-based knowledge reuse for supporting knowledge-driven decision making in new product development”, Int. J. Production Research, 55(23): 7187-7203. DOI: 10.1080/00207543.2017.1351643 (SCI: FT4OJ; EI: 20172903953244; IF=2.623)
  38. Zhou GH*, Lu Q, Xiao ZD, Zhou C, Yuan SZ and Zhang C (2017) “Ontology-based cutting tool configuration considering carbon emissions”, Int. J. Precision Engineering and Manufacturing, 18(11): 1641-1657. DOI: 10.1007/s12541-017-0193-2 (SCI: FM6UA; IF=1.661)
  39. Lu Q, Zhou GH*, Zhou C and Xiao ZD (2017) “A carbon emissions allocation method based on temperature field for products in the usage stage”, Int. J. Advanced Manufacturing Technology, 91(1-4): 917-929. DOI: 10.1007/s00170-016-9799-2 (SCI: EW1OH; EI: 20164903098248; IF=2.601)
  40. Xiao ZD, Cao BB, Zhou GH* and Sun JN (2017) “The monitoring and research of unstable locations in eco-industrial networks”, Computers & Industrial Engineering, 105: 234-246. DOI: 10.1016/j.cie.2017.01.011 (SCI: EP4TD; EI: 20170403280014; IF=3.195)
  41. Zhou GH*, Lu Q, Xiao ZD and Zhu JK (2016) “Core enterprise-suppliers stackelberg game for outsourcing orders in service-oriented manufacturing”, Proceedings of the Institution of Mechanical Engineers, Part B: J. Engineering Manufacture, 230(9): 1738-1750. DOI: 10.1177/ 0954405416662081 (SCI: DV5OF; EI: 20163702802818; IF=1.445)
  42. Xiao ZD, Cao BB, Sun JN, Zhou GH*(2016) “Culture of the stability in an eco-industrial system centered on complex network theory”, J. Cleaner Production, 113: 730-742. DOI: 10.1016/j.jclepro.2015.11.096 (SCI: DE9XM; EI: 20160101754724; IF=5.715)