个人资料
个人简介副教授,硕士生导师,工程机械学院工程机制系系秘书,国家自然科学基金评议人。2004年在浙江大学机械与能源工程学院工业工程专业学习,获学士学位;2008年保送攻读博士,2014年取得浙江大学机械制造及其自动化专业博士学位。2016-2017年在西北工业大学机电学院进行博士后研究,2018年在长安大学工作至今。2017年在瑞典麦拉达伦大学做访问学者,2018-2019年在英国卡迪夫大学进行博士后研究。 先后主持了国家自然科学基金青年项目、陕西省青年基金项目、中央高校基本科研业务费专项资金项目等,参与国家重点研发计划等国家重点项目。发表学术论文30余篇,其中SCI检索30篇,授权专利10余项。2017年入选国家留学基金委首届“国际清洁能源拔尖创新人才培养项目”。 每年招收硕士研究生4名,欢迎对智能装备控制、工业软件、制造工艺领域等感兴趣的同学报考。 社会职务担任 Applied Energy、Sustainability客座编辑,担任IEEE Transactions on Industrial Informatics、IEEE Transactions on Automation Science and Engineering、Applied Energy、Journal of Cleaner Production、Chinese Journal of Mechanical Engineering、Journal of Industrial and Production Engineering、Mechanics of Materials、 ASME Journal of Computing and Information Science in Engineering、International Journal of Computer Integrated Manufacturing、Science of the Total Environment、Advanced Engineering Informatics等国际期刊审稿人。 研究领域1. 智能制造,包括软件开发,设备控制。在软件开发方面,研究碳排放管理软件和减碳路径决策支持优化软件,以及智能设备控制软件、工艺数据处理软件、核心器件驱动软件开发等。在设备控制方面,研究设备运动控制、喷墨打印在线质量控制,用于实现按需喷墨、多轴联动运动控制以及在线质量管理。 2. 增材制造,面向制造领域战略前沿技术,研究增材制造装备与工艺技术,抢抓新兴制造技术高点,助力航空、航天、电子等行业制造技术创新与产业变革。以选择性激光烧结、光固化成型、熔融沉积成型、喷墨打印、微笔直写等装备和工艺为研究对象,通过实验测试、材料微观结构表征、有限元仿真、机器学习等方法,研究成型机理、精度控制、工艺优化、装备设计等。 3. 低碳制造,服务国家“双碳”战略,研究制造企业节能减碳理论、方法和技术,助力制造企业节能降碳改造升级,推动制造企业绿色低碳高质量发展。以机械加工、陶瓷制造、增材制造等工业过程为研究对象,应用能耗监测、大数据分析、人工智能等新兴技术手段,研究制造企业能量建模和优化,生产设备和能源系统协同优化控制,高能耗企业低碳转型路径分析等。 开授课程1、机械制造技术基础 32学时(本科教学); 2、机械制造技术基础(双语)32学时(本科教学); 3、数控机床与编程(留学生)40学时(本科教学); 4、机械CAD/CAM Ⅱ 32学时(本科教学); 5、工业生产自动化 24学时(本科教学); 6、机械制造生产实习指导教师3周(本科教学)。 7、智能制造信息学 36学时(研究生课程)。 8、机器人学 36学时(研究生课程)。 科研项目主持的科研项目: [1] 企业委托项目,220225240171,曲面多层电路喷墨打印技术开发,2024/01-2024/12,20万元,主持。 [2] 陕西省自然科学基础研究计划一般项目(青年),2020JQ-380,金属选区激光熔化成型质量建模及工艺优化研究,2020/01-2021/12,3万元,主持。 [3] 中央高校基本科研业务费优秀博士项目,300102250303,机理-数据混合驱动的增材制造零件高质低碳加工工艺参数优化研究,2020/01-2021/12,8万元,主持。 [4] 国家自然科学基金青年项目,51705428,建筑陶瓷生产机械设备能耗演化机理及其低碳运行优化方法研究,2018/01-2020/12,20万元,主持。 参与的科研项目: [1]西安市秦创原创新驱动平台专项建设项目,21ZCZZHXJS-QCY6-0013,曲面多层电路板增材制造技术研究与平台开发,2022/01-2023/12,参与。 [2]国家重点研发计划“网络协同制造和智能工厂”专项项目,2018YFB1702800,网络协同制造系统集成共性技术研究与应用,2019-05至2022-04,参与。 [3]国家重点研发计划“工业软件”专项项目,2021YFB3301702,大规模制造产业工业互联平台研发与应用,2021/12-2024/11,参与。 论文[1] Kai Ding, Anjie Li, Jingxiang Lv*, Fu Gu*, Decarbonizing ceramic industry: Technological routes and cost assessment[J]. Journal of Cleaner Production, 2023. [2] Jizhuang Hui, Hao Zhang, Jingxiang Lv*, ChulHee Lee, et al. Investigation and prediction of nano-silver lines quality upon various process parameters in inkjet printing process based on an experimental method[J]. 3D Printing and Additive Manufacturing, 2023. [3] Hao Zhang, Jizhuang Hui, Jingxiang Lv*, ChulHee Lee, et al. An innovative method combining fused deposition modelling and inkjet printing in manufacturing multifunctional parts for aerospace application[J]. Journal of Materials Research and Technology, 2023, 24: 4405-4426. [4] Zhiqiang Yan, Jizhuang Hui, Jingxiang Lv, Huisingh Donald, et al. A hybrid mechanism-based and data-driven approach to forecast energy consumption of fused deposition modelling, Journal of Cleaner Production, 2023, 413: 137500. [5] Zhiqiang Yan, Jian Huang, Jingxiang Lv*, Jizhuang Hui, Yongsheng Liu, Hao Zhang, Enhuai Yin, Qingtao Liu, A New Method of Predicting the Energy Consumption of Additive Manufacturing considering the Component Working State, Sustainability, 2022, 14(7): 3757. [7] Jingxiang Lv, Shun Jia, Huifeng Wang, Kai Ding, Felix T.S. Chan, Comparison of different approaches for predicting material removal power in milling process, Int. J. of Advanced Manufacturing Technology, 2021, 116(1-2): 213-227. [8] Jingxiang Lv, Peng Tao*; Zhang Yingfeng* ; Wang Yuchang, A novel method to forecast energy consumption of selective laser melting processes, International Journal of Production Research, 2021, 59(8): 2375-2391. [9] Tao Peng, Jingxiang Lv,Arfan Majeed, Xihui Liang. An experimental investigation on energy-effective additive manufacturing of aluminum parts via process parameter selection [J]. J Clean Prod, 2021, 279: 123609. [10] 吕景祥*, 唐任仲,郑军,数据驱动的车削和钻削加工能耗预测, 计算机集成制造系统, 2020. 26(8): 2073-2082. [11] Jingxiang Lv, Zhiguo Wang * , Shuaiyin Ma (2020), “Calculation method and its application for energy consumption of ball mills in ceramic industry based on power feature deployment”, Advances in Applied Ceramics, 119(4):183-94. [12] Shuaiyin Ma, Yingfeng Zhang*, Jingxiang Lv**, Yuntian Ge, Haidong Yang, Lin Li. Big data driven predictive production planning for energy-intensive manufacturing industries [J]. Energy, 2020, 211: 118320. [13]Arfan Majeed*, Altaf Ahmed, Jingxiang Lv**, Tao Peng, Muhammad Muzamil. A state-of-the-art review on energy consumption and quality characteristics in metal additive manufacturing processes [J]. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2020, 42(5): 249. [14]Jingxiang Lv, Gu, F., Zhang W.J, et al. (2019) “Life cycle assessment and life cycle costing of sanitary ware manufacturing: A case study in China”, J Clean Prod., 2019: 117938. [15]Jingxiang Lv, Tang, R.Z., Tang, W.C.J, Jia, S., Liu, Y., Cao, Y.L. (2018) “An investigation into methods for predicting material removal energy consumption in turning”, J Clean Prod., 193: 128-139. [16]Jingxiang Lv, Peng, T., Tang, R.Z. (2018) “Energy modeling and a method for reducing energy loss due to cutting load during machining operations”, Proc Inst Mech Eng Part B J Eng Manuf., 233(3):699-710. [17]Jingxiang Lv, Tang, R.Z., Tang, W.C.J., Liu, Y., Zhang, Y.F., Jia, S. (2017) “An investigation into reducing the spindle acceleration energy consumption of machine tools”, J Clean Prod., 143: 794-803. [18]Jingxiang Lv, Tang, R.Z., Jia, S., Liu, Y. (2016) “Experimental study on energy consumption of computer numerical control machine tools”, J Clean Prod., 112, Part 5: 3864-3874. [19]Majeed, A.,Iqbal, A., Jingxiang Lv.* (2018) “Enhancement of tool life in drilling of hardened AISI 4340 steel using 3D FEM modeling”, Int. J. of Advanced Manufacturing Technology, 95(5-8): 1875-1889. [20]Majeed, A.,Jingxiang Lv*, Peng, T.(2018) “A framework for big data driven process analysis and optimization for additive manufacturing”, Rapid Prototyping Journal, Article ID: RPJ-04-2017-0075, accepted. [21]Zhang, Y.F., Zhu, Z.F., Jingxiang Lv(2018) “CPS-Based Smart Control Model for Shopfloor Material Handling”, IEEE Transactions on Industrial Informatics, 14(4): 1764-1775. [22]Zhang, Y.F., Qian, C., Jingxiang Lv, Liu Y.(2017) “Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor”, IEEE Transactions on Industrial Informatics, 13 (2): 737-747. [23]Jia, S., Yuan, Q.H., Jingxiang Lv, Liu, Y., Ren D., Zhang Z.W. (2017) “Therblig-embedded value stream mapping method for lean energy machining”, Energy, 138: 1081-1098. [24]Jia, S., Tang, R.Z., Jingxiang Lv, Yuan, Q.H., Peng, T. (2017) “Energy consumption modeling of machining transient states based on finite state machine”, Int. J. ofAdvanced Manufacturing Technology, 88(5-8): 2305-2320. [25]Jia, S., Tang, R.Z., Jingxiang Lv, Zhang, Z.W., Yuan, Q.H. (2016) “Energy modeling for variable material removal rate machining process: an end face turning case”, Int. J. of Advanced Manufacturing Technology, 85(9): 2805-2818. [26]Jia, S., Tang, R.Z., Jingxiang Lv. (2016) “Machining activity extraction and energy attributes inheritance method to support intelligent energy estimation of machining process”, Journal of Intelligent Manufacturing, 27(3): 595-616. [27]Zhong, Q.Q., Tang, R.Z., Jingxiang Lv, Jia, S., Jin, M.Z. (2016)“Evaluation on models of calculating energy consumption in metal cutting processes: a case of external turning process”, Int. J. ofAdvanced Manufacturing Technology, 82(9-12): 2087-2099. [28]Jia, S., Tang, R.Z., Jingxiang Lv.(2014) “Therblig-based energy demand modeling methodology of machining process to support intelligent manufacturing”, Journal of Intelligent Manufacturing,25(5): 913-931. [29]Hu L.K., Liu Y., Lohse, N., Tang. R.Z.,Jingxiang Lv, Peng, C., Evans, S. (2017) “Sequencing the features to minimise the non-cutting energy consumption in machining considering the change of spindle rotation speed”, Energy, 139: 935-946. [30]Jingxiang Lv, Renzhong Tang*, Shun Jia. Therblig-based energy supply modeling of computer numerical control machine tools [J]. J Clean Prod, 2014, 65(0): 168-177. 科技成果发明专利: [1]吕景祥,彭涛. 一种选择性激光熔化工艺过程能耗预测及节能控制方法, 2020.11.24, 中国, ZL201811238146.4,发明,授权。 软件著作权: [1]吕景祥,惠记庄,黄健,阎志强. 增材制造能耗预测系统V1.0, 2021.5.25, 中国, 登记号:2021SR0759698。 [2]吕景祥,丁凯,李安杰. 高能耗企业能效管理平台V1.0, 2021.6.2, 中国, 登记号:2021SR0823167。 [3]吕景祥,谌利川,何海燕. 霆欧物资仓储管理系统V1.0, 2021.5.26, 中国, 登记号:2021SR0772770。 [4]吕景祥,谌利川,何海燕. 霆欧生产能力平衡系统V1.0, 2021.5.26, 中国, 登记号:2021SR0772764。 荣誉奖励2017年入选国家留学基金委首届“国际清洁能源拔尖创新人才培养项目”。 工作经历2021.01-至今,长安大学工程机械学院,机械工程,副教授。 2018.09-2020.12,长安大学工程机械学院,机械工程,讲师。 2018.09-2019.11,英国卡迪夫大学(Cardiff University), 博士后。 2017.09-2017.12,瑞典麦拉达伦大学(Mälardalen University), 访问学者。 2015.12-2018.08,西北工业大学机电学院,机械工程,博士后。 |