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Faculty

| 材料数智化设计与加工研究室

科研团队

付华栋

研究室成员
  • 张洪涛

    系所:材料加工与控制工程系

    职称: 副教授/副研究员/高级工程师

    E-mail: zht@ustb.edu.cn

    办公地点:主楼411

  • 贵云玮

    系所:材料加工与控制工程系

    职称: 副教授/副研究员/高级工程师

    E-mail: guiyunwei@ustb.edu.cn

    办公地点:主楼411

  • 张永军

    系所:材料加工与控制工程系

    职称: 副教授/副研究员/高级工程师

    E-mail: zhangyj@mater.ustb.edu.cn

    办公地点:主楼404

  • 刘靖

    系所:材料加工与控制工程系

    职称: 副教授/副研究员/高级工程师

    E-mail: liujing@mater.ustb.edu.cn

    办公地点:主楼203

  • 周成

    系所:材料加工与控制工程系

    职称: 教授/研究员

    E-mail: zhouc@ustb.edu.cn

    办公地点:科技楼107

  • 吴春京

    系所:材料加工与控制工程系

    职称: 教授/研究员

    E-mail: cjwu@mater.ustb.edu.cn

    办公地点:主楼412

  • 项目介绍

    系所:

    职称: 教授/研究员

    E-mail:

    办公地点:

  • 主要研究方向
  • 代表性科研项目
  • 代表性科研成果
  • 专利
  • 研究室现有教师7名,教授3名,副教授4名,其中,国家杰青、优青、教育部海外引进人才、博士后创新人才、北京市科技新星各1人。承担国家重点研发计划、国家“863”计划、国家自然科学基金杰出青年项目、国家自然科学基金优秀青年项目、国家自然科学基金面上项目、军工保密项目、北京市科技新星计划等课题30余项;在Advanced MaterialsActa Materialia等国际知名期刊发表高水平论文300余篇;授权国家发明专利、软件著作权50余项;获国家技术发明二等奖、国家科技进步二等奖、中国有色金属工业科学技术一等奖等奖项。

    主要研究方向包括:

    1)数据驱动的金属材料理性设计

    2)金属材料智能化制备加工

    3)金属材料成形过程数字孪生技术

    4)金属增材制造过程组织性能调控

    5)高温合金的控制凝固与控制成形

    6)高性能铜合金的短流程高效制备加工


  • 1.       国家自然科学基金杰青项目:关键金属材料智能设计与加工

    2.       重点新材料研发及应用国家科技重大专项(科技创新2030重大项目)(项目负责人,2970万元)

    3.       国家自然科学基金优青项目:高性能合金理性设计与短流程制备加工

    4.       国家自然科学基金面上项目:高端引线框架铜合金构效关系深度挖掘及数据-知识协同驱动研发

    5.       国家自然科学基金面上项目:Cu-Ni-Al合金中高密度孪晶-共格析出相结构调控及其对力-电性能的影响机制

    6.       国家自然科学基金青年科学基金项目:柱状晶组织铜铬锆合金的变形行为及形变-时效强化机制

    7.       国家自然科学基金青年科学基金项目:电子束选区熔化TiAl合金的数据驱动冶金缺陷调控与强韧化机理

    8.       国家自然科学基金青年科学基金项目:引线框架铜合金的高强高导化机制与智能设计

    9.       国家重点研发计划“揭榜挂帅”项目:高端集成电路引线框架铜合金材料研发与应用(课题负责人)

    10.    航空发动机及燃气轮机重大专项基础研究项目(课题负责人,1000万元)

    11.    博士后创新人才支持计划:组织信息嵌入的铜合金成分和工艺一体化智能设计


  • 1.        Zhang H, Fu H, Li W, et al. Empowering the Sustainable Development of High‐End Alloys via Interpretive Machine Learning[J]. Advanced Materials, 2024: 2404478.

    2.        Zhang H T, Fu H D, Zhu S C, et al. Machine learning assisted composition effective design for precipitation strengthened copper alloys[J]. Acta Materialia, 2021, 215: 117118.

    3.        Jiang L, Fu H, Zhang Z, et al. Synchronously enhancing the strength, toughness, and stress corrosion resistance of high-end aluminum alloys via interpretable machine learning[J]. Acta Materialia, 2024, 270: 119873.

    4.        Jiang L, Zhang Z, Hu H, et al. A rapid and effective method for alloy materials design via sample data transfer machine learning[J]. npj Computational Materials, 2023, 9: 26.

    5.        Feng S, Fu H D, Zhou H Y, et al. A general and transferable deep learning framework for predicting phase formation in materials [J]. npj Computational Materials, 2021, 7: 10.

    6.        Zhang H T, Fu H D, He X Q, et al. Dramatically enhanced combination of ultimate tensile strength and electric conductivity of alloys via machine learning screening[J]. Acta Materialia, 2020, 200: 803-810.

    7.        Jiang L, Fu H, Zhang H, et al. Physical mechanism interpretation of polycrystalline metals’ yield strength via a data-driven method: a novel Hall–Petch relationship[J]. Acta Materialia, 2022, 231: 117868.

    8.        Wang C S, Fu H D, Jiang L, et al. A property-oriented design strategy for high performance copper alloys via machine learning[J]. npj Computational Materials, 2019, 5: 87.

    9.        Zhou X Z, Fu H D, Xue F, et al. Abnormal precipitation of the μ phase during solution treatment of γ′-strengthened Co-Ni-Al-W-based superalloys[J]. Scripta Materialia, 2020, 181: 30-34.

    10.     Xuan D P, Zhou C, Zhou Y, Jiang T L, Zhu B J, Fan W H. Effect of test temperature on tensile behavior of Fe-6.5wt.%Si alloy as-cast strip[J]. Journal of Magnetism and Magnetic Materials, 2022, 559: 169540.

    11.     Gui Y W, Aoyagi K, Bian H K, et al. Detection, classification and prediction of internal defects from surface morphology data of metal parts fabricated by powder bed fusion type additive manufacturing using an electron beam[J]. Additive Manufacturing, 2022, 54: 102736.

    12.     Gui Y W, Aoyagi K, Chiba A. Development of macro-defect-free PBF-EB-processed Ti-6Al-4V alloys with superior plasticity using PREP-synthesized powder and machine learning-assisted process optimization[J]. Materials Science and Engineering: A, 2023, 864: 144595.

    13.     Gui Y W, Aoyagi K, Bian H K, et al. Machine-Learning-Assisted Development of Carbon Steel with Superior Strength and Ductility Manufactured by Electron Beam Powder Bed Fusion[J]. Metallurgical and Materials Transactions A, 2024, 55: 320-334.

    14.     Gui Y W, Li Q A, Xue Y B, et al. Twin-twin geometric structure effect on the twinning behavior of an Mg-4Y-3Nd-2Sm-0.5Zr alloy traced by quasi-in-situ EBSD, Journal of Magnesium and Alloys, 2023, 11: 1381-1392.

    15.     Gui Y W, Ouyang L X, Cui Y J, et al. Grain refinement and weak-textured structures based on the dynamic recrystallization of Mg-9.80Gd-3.78Y-1.12Sm-0.48Zr alloy[J]. Journal of Magnesium and Alloys, 2020, 9: 456-466.

    16.     Gui Y W, Ouyang L X, Xue Y B, et al. Effect of thermo-mechanical processing parameters on the dynamic restoration mechanism in an Mg-4Y-2Nd-1Sm-0.5Zr alloy during hot compression[J]. Journal of Materials Science & Technology, 2021, 90: 205-224.

    17.     Gui Y W, Cui Y J, Bian H K, et al. Deformation behavior of Mg–5Y–2Nd–0.5Zr alloys with different Sm additions[J]. Journal of Alloys and Compounds, 2021, 856: 158201.

    18.     Gui Y W, Aoyagi K, Bian H K, et al. Machine-Learning-Assisted Development of Carbon Steel with Superior Strength and Ductility Manufactured by Electron Beam Powder Bed Fusion[J]. Metallurgical and Materials Transactions A, 2024, 55: 320-334.

    19.     Xuan D P, Zhou C, Zhou Y, Jiang T L, Zhu B J, Fan W H, Jia Y G. Numerical simulation of the top side-pouring twin-roll casting of 6.5 wt.% Si steel process[J]. The International Journal of Advanced Manufacturing Technology, 2021, 119(3):2355-2368.

    20.     Xuan D P, Zhou C, Zhou Y, Jiang T L, Fan W H, Mao Y. Effect of cooling rate on the order degree, residual stress, and room temperature mechanical properties of Fe-6.5wt.%Si alloy[J]. Journal of Magnetism and Magnetic Materials, 2023, 571: 170550.

    21.     Xuan D P, Zhou C, Zhou Y, Jiang T L, Zhu B J, Fan W H, Jia Y G. Comparison of the casting process of 3.0% Si steel between the top side-pouring twin-roll casting and twin-roll strip casting[J]. The International Journal of Advanced Manufacturing Technology, 2021, 119(11-12):7751-7764.

    22.     Wei Zhaohui, Wu Chunjing. A new analytical model to predict the profile and stress distribution of tube in three-roll continuous retained mandrel rolling[J]. Journal of Materials Processing Technology, 2022, 302: 117491.

    23.     Jun Liang, Ming Wang, Chunjing Wu, Weizhong Tang, Hang Ping. Fabrication process and properties of Cu-coated carbon fiber reinforced Al matrix composite[J]. Functional Materials, 2020, 27: 125-135.

    24.     Luo Xian, Ren Xueping, Qu Haitao, et al. Research on influence of deep cryogenic treatment and ultrasonic rolling on improving surface integrity of Ti6Al4V alloy[J]. Materials Science & Engineering: A, 2022, 843:143142.

    25.     Xu Xianglai, Ren Xueping, Hou Hongliang, et al. Effects of cryogenic and annealing treatment on microstructure and properties of friction stir welded TA15 joints[J]. Materials Science & Engineering: A, 2021, 804:140750.

    26.     Zhang Li, Ren Xueping, Li Jingkun, et al. Microstructure and mechanical properties of spark plasma sintered SiC-B4C gradient ceramics with Al additive[J]. Ceramics International, 2021, 47: 30844-30851.

    27.     Li Jingkun, Ren Xueping, Gao Xiaodan. Effect of superplastic deformation on microstructure evolution of 3207 duplex stainless steel[J]. Materials Characterization, 2020, 164: 110320.

    28.     Jia Li, Ren Xueping, Hou Hongliang, et al. Microstructural evolution and superplastic deformation mechanisms of as-rolled 2A97 alloy at low-temperature[J]. Materials Science and Engineering: A, 2019, 759: 19-29.

    29.     Zhang H, He J, Zhu J, et al. Effect of Mg microalloying on the microstructure and properties of conductive Cu-Ni-Al alloy with ultra-high strength[J]. Journal of Alloys and Compounds, 2024, 1005: 176186.

    30.     Zhang H, He J, Yun P, et al. Effect of Ni/Al atomic ratio on the microstructure and properties of Cu-Ni-Al alloys[J]. Materials Science and Engineering: A, 2024, 908: 146718.


  • [1]        付华栋,谢建新,张洪涛,一种低钴含量高强中导Cu-Ni-Co-Si系合金及其制备工艺,专利号:CN202110330510.5

    [2]        付华栋,谢建新,王长胜,基于机器学习并面向性能要求的多组元合金成分设计方法,专利号:CN201910252935.1

    [3]        付华栋,谢建新,张毅,一种低密度、高组织稳定性的钴基高温合金及其制备方法,专利号:CN201810301030.4

    [4]        付华栋,谢建新,周晓舟,等,一种低钨含量γ′相强化钴基高温合金及其制备工艺,专利号:CN201910073112.2

    [5]        付华栋,谢建新,李伟,等,一种高强高导Cu-Cr-Zr合金棒材的非真空熔炼水平连铸生产工艺,专利号:CN201611124063.3

    [6]        付华栋,谢建新,李伟,一种铜铬系合金水平连铸工艺,专利号:CN201611129758.0

    [7]        付华栋,谢建新,李伟,一种石墨复合铸型及铜铬系合金水平连铸工艺,专利号:CN201611129758.0

    [8]        付华栋,谢建新,李伟,等,一种高强高导Cu‑Cr‑Zr合金棒材的非真空熔炼水平连铸生产工艺,专利号:CN201611124063.3

    [9]        付华栋,张志豪,谢建新,等,一种短流程高成材率制备高硅电工钢带材的方法,专利号:CN201310199428.9

    [10]     付华栋,张志豪,谢建新,等,一种纯铁/柱状晶高硅电工钢复合板坯的制备方法,专利号:CN201310198770.7

     


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