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| 1 | 薄壁件铣削过程加工变形研究进展显示文摘机床加工性能和刀具切削性能的发展使得薄壁件的高效率和高精密加工成为可能,也使得薄壁件在航空航天领域得到更广泛应用。薄壁零件结构复杂、刚度低,在铣削过程中易发生变形,因此精准预测与控制薄壁件的加工变形是机加工领域亟需解决的工艺难题。通过对薄壁件分类以及加工工艺分析,归纳总结引起薄壁件加工变形的因素,对加工变形影响最为关键的铣削力计算模型进行简述;结合国内外薄壁件变形预测与控制方法的研究,以弹塑性和数值模拟方法对薄壁件加工变形进行预测,通过加工工艺优化、辅助支撑技术、高速切削技术和数控补偿技术等方法对薄壁件加工过程的变形量进行控制;基于数据驱动数字孪生体的更新迭代,实现薄壁件实际加工过程的孪生及薄壁件变形预测与控制,构建了以数字孪生为平台的薄壁件加工变形预测与控制理论框架;最后对数字孪生在薄壁件加工变形预测及控制的发展与应用提出展望。 | 岳彩旭 张俊涛 刘献礼 陈志涛 Steven Y.LIANG Lihui WANG | 2022 | 航空学报2022，43，4： | 22 |
| 2 | A review of chatter vibration research in milling显示文摘Chatter is a self-excited vibration of parts in machining systems. It is widely present across a range of cutting processes, and has an impact upon both efficiency and quality in production processing. A great deal of research has been dedicated to the development of technologies that are able to predict and detect chatter. The purpose of these technologies is to facilitate the avoidance of chatter during cutting processes, which leads to better surface precision, higher productivity,and longer tool life. This paper summarizes the current state of the art in research regarding the problems of how to arrive at stable chatter prediction, chatter identification, and chatter control/-suppression, with a focus on milling processes. Particular focus is placed on the theoretical relationship between cutting chatter and process damping, tool runout, and gyroscopic effect, as well as the importance of this for chatter prediction. The paper concludes with some reflections regarding possible directions for future research in this field. | Caixu YUE Haining GAO Xianli LIU Steven Y.LIANG Lihui WANG | 2019 | Chinese Journal of Aeronautics2019，32，2： | 12 |
| 3 | 干切削温度场的数学物理建模与预测验证显示文摘针对正交干切削加工过程,建立了刀具与切屑接触区域温度场数学物理模型。模型采用绝热半无限介质热源叠加的方法,计算出切屑剪切变形区和'刀具-切屑'摩擦区叠加影响下刀具与切屑在接触区的温度场,分析了最高温度产生位置及其机理。基于该模型,对锋利PCBN刀具硬车削轴承钢过程的温度场进行预测,得到的刀具与切屑接触面温度在多个切削速度条件下误差均小于8%,说明该模型能够实现对锋利切削加工温度场的精确预测。 | 康征 季霞 张雪萍 Steven Y.Liang | 2011 | 机械设计与研究2011，27，3： | 6 |
| 4 | 双转台五轴数控机床主轴热误差测量与建模显示文摘为了测量数控机床实际切削加工过程中主轴的热误差,并优化热误差模型的输出,提出利用热测试件测量机床主轴热误差的方法,并利用误差特性分离出热误差。针对机床热误差建模中温度测点优化选择的问题,提出基于K-means++算法和相关系数法相结合的方法选取温度敏感点,采用K-means++算法对所有温度测点进行聚类,相关系数法计算各个温度变量与主轴热误差之间的相关性,从而确定温度敏感点,结合分离出的热误差建立主轴热误差多元线性回归模型。在VMC-C50双转台五轴数控机床上对该方法进行试验验证,结果表明,温度测点的数量由8个减少为2个,模型的预测精度及鲁棒性得到有效提升。 | 刘献礼 宋厚旺 吴石 岳彩旭 Steven Y.Liang 李荣义 | 2019 | 哈尔滨理工大学学报2019，24，6： | 5 |
| 5 | Feeding a Saccharomyces cerevisiae fermentation product improves udder health and immune response to a Streptococcus uberis mastitis challenge in mid-lactation dairy cows显示文摘Background:We aimed to characterize the protective effects and the molecular mechanisms of action of a Saccharomyces cerevisiae fermentation product(NTK)in response to a mastitis challenge.Eighteen mid-lactation multiparous Holstein cows(n=9/group)were fed the control diet(CON)or CON supplemented with 19 g/d NTK for 45 d(phase 1,P1)and then infected in the right rear quarter with 2500 CFU of Streptococcus uberis(phase 2,P2).After 36-h,mammary gland and liver biopsies were collected and antibiotic treatment started until the end of P2(9 d post challenge).Cows were then followed until day 75(phase 3,P3).Milk yield(MY)and dry matter intake(DMI)were recorded daily.Milk samples for somatic cell score were collected,and rectal and udder temperature,heart and respiration rate were recorded during the challenge period(P2)together with blood samples for metabolite and immune function analyses.Data were analyzed by phase using the PROC MIXED procedure in SAS.Biopsies were used for transcriptomic analysis via RNA-sequencing,followed by pathway analysis.Results:DMI and MY were not affected by diet in P1,but an interaction with time was recorded in P2 indicating a better recovery from the challenge in NTK compared with CON.NTK reduced rectal temperature,somatic cell score,and temperature of the infected quarter during the challenge.Transcriptome data supported these findings,as NTK supplementation upregulated mammary genes related to immune cell antibacterial function(e.g.,CATHL4,NOS2),epithelial tissue protection(e.g.IL17C),and anti-inflammatory activity(e.g.,ATF3,BAG3,IER3,G-CSF,GRO1,ZFAND2A).Pathway analysis indicated upregulation of tumor necrosis factorα,heat shock protein response,and p21 related pathways in the response to mastitis in NTK cows.Other pathways for detoxification and cytoprotection functions along with the tight junction pathway were also upregulated in NTK-fed cows.Conclusions:Overall,results highlighted molecular networks involved in the protective effect of NTK prophylactic supplementation on udder health during a subclinical mastitic event. | M.Vailati-Riboni D.N.Coleman V.Lopreiato A.Alharthi R.E.Bucktrout E.Abdel-Hamied I.Martinez-Cortes Y.Liang E.Trevisi I.Yoon J.J.Loor | 2021 | Journal of Animal Science and Biotechnology2021，12，4： | 4 |
| 6 | Methods for a blind analysis of isobar data collected by the STAR collaboration显示文摘In 2018,the STAR collaboration collected data from^(96)_(44)Ru+^(96)_(44)Ru and^(96)_(40)Zr+^(96)_(40)Zr at√^(S)NN=200 Ge V to search for the presence of the chiral magnetic effect in collisions of nuclei.The isobar collision species alternated frequently between 9644 Ru+^(96)_(44)Ru and^(96)_(40)Zr+^(96)_(40)Zr.In order to conduct blind analyses of studies related to the chiral magnetic effect in these isobar data,STAR developed a three-step blind analysis procedure.Analysts are initially provided a'reference sample'of data,comprised of a mix of events from the two species,the order of which respects time-dependent changes in run conditions.After tuning analysis codes and performing time-dependent quality assurance on the reference sample,analysts are provided a species-blind sample suitable for calculating efficiencies and corrections for individual≈30-min data-taking runs.For this sample,species-specific information is disguised,but individual output files contain data from a single isobar species.Only run-by-run corrections and code alteration subsequent to these corrections are allowed at this stage.Following these modifications,the'frozen'code is passed over the fully un-blind data,completing the blind analysis.As a check of the feasibility of the blind analysis procedure,analysts completed a'mock data challenge,'analyzing data from Au+Au collisions at√^(S)NN=27 Ge V,collected in 2018.The Au+Au data were prepared in the same manner intended for the isobar blind data.The details of the blind analysis procedure and results from the mock data challenge are presented. | J.Adam L.Adamczyk J.R.Adams J.K.Adkins G.Agakishiev M.M.Aggarwal Z.Ahammed I.Alekseev D.M.Anderson A.Aparin E.C.Aschenauer M.U.Ashraf F.G.Atetalla A.Attri G.S.Averichev V.Bairathi K.Barish A.Behera R.Bellwied A.Bhasin J.Bielcik J.Bielcikova L.C.Bland I.G.Bordyuzhin J.D.Brandenburg A.V.Brandin J.Butterworth H.Caines M.Calderon de la Barca Sanchez D.Cebra I.Chakaberia P.Chaloupka B.K.Chan F-H.Chang Z.Chang N.Chankova-Bunzarova A.Chatterjee D.Chen J.Chen J.H.Chen X.Chen Z.Chen J.Cheng M.Cherney M.Chevalier S.Choudhury W.Christie X.Chu H.J.Crawford M.Csanad M.Daugherity T.G.Dedovich I.M.Deppner A.A.Derevschikov L.Didenko X.Dong J.L.Drachenberg J.C.Dunlop T.Edmonds N.Elsey J.Engelage G.Eppley S.Esumi O.Evdokimov A.Ewigleben O.Eyser R.Fatemi S.Fazio P.Federic J.Fedorisin C.J.Feng Y.Feng P.Filip E.Finch Y.Fisyak A.Francisco L.Fulek C.A.Gagliardi T.Galatyuk F.Geurts A.Gibson K.Gopal X.Gou D.Grosnick W.Guryn A.I.Hamad A.Hamed S.Harabasz J.W.Harris S.He W.He X.H.He Y.He S.Heppelmann S.Heppelmann N.Herrmann E.Hoffman L.Holub Y.Hong S.Horvat Y.Hu H.Z.Huang S.L.Huang T.Huang X.Huang T.J.Humanic P.Huo G.Igo D.Isenhower W.W.Jacobs C.Jena A.Jentsch Y.Ji J.Jia K.Jiang S.Jowzaee X.Ju E.G.Judd S.Kabana M.L.Kabir S.Kagamaster D.Kalinkin K.Kang D.Kapukchyan K.Kauder H.W.Ke D.Keane A.Kechechyan M.Kelsey Y.V.Khyzhniak D.P.Kikoła C.Kim B.Kimelman D.Kincses T.A.Kinghorn I.Kisel A.Kiselev M.Kocan L.Kochenda L.K.Kosarzewski L.Kramarik P.Kravtsov K.Krueger N.Kulathunga Mudiyanselage L.Kumar S.Kumar R.Kunnawalkam Elayavalli J.H.Kwasizur R.Lacey S.Lan J.M.Landgraf J.Lauret A.Lebedev R.Lednicky J.H.Lee Y.H.Leung C.Li C.Li W.Li W.Li X.Li Y.Li Y.Liang R.Licenik T.Lin Y.Lin M.A.Lisa F.Liu H.Liu P.Liu P.Liu T.Liu X.Liu Y.Liu Z.Liu T.Ljubicic W.J.Llope R.S.Longacre N.S.Lukow S.Luo X.Luo G.L.Ma L.Ma R.Ma Y.G.Ma N.Magdy R.Majka D.Mallick S.Margetis C.Markert H.S.Matis J.A.Mazer N.G.Minaev S.Mioduszewski B.Mohanty I.Mooney Z.Moravcova D.A.Morozov M.Nagy J.D.Nam Md.Nasim K.Nayak D.Neff J.M.Nelson D.B.Nemes M.Nie G.Nigmatkulov T.Niida L.V.Nogach T.Nonaka A.S.Nunes G.Odyniec A.Ogawa S.Oh V.A.Okorokov B.S.Page R.Pak A.Pandav Y.Panebratsev B.Pawlik D.Pawlowska H.Pei C.Perkins L.Pinsky R.L.Pinter J.Pluta J.Porter M.Posik N.K.Pruthi M.Przybycien J.Putschke H.Qiu A.Quintero S.K.Radhakrishnan S.Ramachandran R.L.Ray R.Reed H.G.Ritter O.V.Rogachevskiy J.L.Romero L.Ruan J.Rusnak N.R.Sahoo H.Sako S.Salur J.Sandweiss S.Sato W.B.Schmidke N.Schmitz B.R.Schweid F.Seck J.Seger M.Sergeeva R.Seto P.Seyboth N.Shah E.Shahaliev P.V.Shanmuganathan M.Shao A.I.Sheikh W.Q.Shen S.S.Shi Y.Shi Q.Y.Shou E.P.Sichtermann R.Sikora M.Simko J.Singh S.Singha N.Smirnov W.Solyst P.Sorensen H.M.Spinka B.Srivastava T.D.S.Stanislaus M.Stefaniak D.J.Stewart M.Strikhanov B.Stringfellow A.A.P.Suaide M.Sumbera B.Summa X.M.Sun X.Sun Y.Sun Y.Sun B.Surrow D.N.Svirida P.Szymanski A.H.Tang Z.Tang A.Taranenko T.Tarnowsky J.H.Thomas A.R.Timmins D.Tlusty M.Tokarev C.A.Tomkiel S.Trentalange R.E.Tribble P.Tribedy S.K.Tripathy O.D.Tsai Z.Tu T.Ullrich D.G.Underwood I.Upsal G.Van Buren J.Vanek A.N.Vasiliev I.Vassiliev F.Videbæk S.Vokal S.A.Voloshin F.Wang G.Wang J.S.Wang P.Wang Y.Wang Y.Wang Z.Wang J.C.Webb P.C.Weidenkaff L.Wen G.D.Westfall H.Wieman S.W.Wissink R.Witt Y.Wu Z.G.Xiao G.Xie W.Xie H.Xu N.Xu Q.H.Xu Y.F.Xu Y.Xu Z.Xu Z.Xu C.Yang Q.Yang S.Yang Y.Yang Z.Yang Z.Ye Z.Ye L.Yi K.Yip Y.Yu H.Zbroszczyk W.Zha C.Zhang D.Zhang S.Zhang S.Zhang X.P.Zhang Y.Zhang Y.Zhang Z.J.Zhang Z.Zhang Z.Zhang J.Zhao C.Zhong C.Zhou X.Zhu Z.Zhu M.Zurek M.Zyzak STAR Collaboration Abilene | 2021 | Nuclear Science and Techniques2021，32，5： | 3 |
| 7 | 深度学习与多信号融合在铣刀磨损状态识别中的研究显示文摘为精确地识别刀具磨损状态,提出了一种深度学习与多信号融合相结合的识别方法。以自编码网络为基础,构建了堆叠稀疏自编码网络。采集铣刀不同磨损状态下的力信号、振动信号及声发射信号,并对上述信号进行小波包分解以便获取能够表征铣刀磨损的时频域特征。利用无监督学习和有监督学习对堆叠稀疏自编码网络进行训练,建立了深度学习的铣刀磨损状态识别模型。研究结果表明,多信号融合的深度学习模型对铣刀磨损状态识别准确率达到94.44%。 | 穆殿方 刘献礼 岳彩旭 Steven Y.LIANG 陈志涛 李恒帅 徐梦迪 | 2021 | 机械科学与技术2021，40，10： | 3 |
| 8 | 刀具磨损自动识别及检测系统显示文摘对加工状态下刀具磨损值及时有效的检测,可以在保证加工精度的前提下提高加工效率,同时为刀具剩余寿命预测提供有力的数据支撑。针对刀具磨损值测量过程中人工参与的不足、易受主观因素影响、检测精度低等问题,建立了刀具磨损图像自动检测系统,基于最大类间方差及遗传算法迭代寻找最佳阈值,进行磨损区域分割;利用磨损区域呈现“线状”的特点,进行磨损区域特征识别及滤波;基于多级Hough变换以及磨损区域二次识别的方法,将刀具磨损区域在图像中清晰地提取出来。在此基础上,基于Canny算子边缘检测方法建立刀具磨损曲线,计算出磨损区域的磨损值。在刀具磨损检测系统计算的磨损量与超景深显微镜的人工测量结果进行比对,经实验表明,该磨损检测系统检测误差在±6%以内,并且提高了刀具磨损值的检测效率。 | 李恒帅 刘献礼 岳彩旭 李晓晨 Steven Y.Liang Lihui Wang | 2021 | 计算机应用2021，41，S01： | 3 |
| 9 | Numerical simulation of thermal field of FSW 2219 aluminum alloy thick plate显示文摘To investigate the influence of temperature field of friction stir welding(FSW)2219 aluminum alloy thick plate,and to achieve effective prediction of temperature field,the authors establish a three-dimensional numerical simulation model of FSW 18 mm thick 2219 aluminum alloy based on ABAQUS/CEL,considering the morphological characteristics of the tool pin.The simulations of plunging,dwelling,and welding stages are achieved.The distribution of temperature and temperature cycle curve of characteristic points in welding process are obtained.The validity of the simulation results is verified by experiments.The influence of the tool-rotational speed and welding speed on temperature field is explored.The work lays a foundation for the prediction and control of temperature field in FSW medium thickness 2219 aluminum alloy,and provides reference for selection of welding parameters to ensure high quality welding of fuel tank of heavy-lift rocket. | 卢晓红 孙旭东 孙世煊 钱俊瑜 Steven Y.Liang | 2021 | China Welding2021，30，4： | 3 |
| 10 | 考虑应变-温度耦合与高温动态结晶的钛合金本构模型研究显示文摘Ti-6Al-4V合金的高速切削加工是一个复杂的高温高应变率的热力耦合过程,为更加准确研究Ti-6Al-4V合金在高温高应变率下的真实应力-应变关系,构建了一种修正Johnson-Cook(J-C)本构模型。修正的J-C本构模型综合考虑了塑性阶段应变硬化率会随加载温度的升高而降低的现象,以及在达到高温动态结晶效应的临界温度时,Ti-6Al-4V合金的流动应力会急剧下降的现象。基于修正J-C本构模型的流动应力-应变预测结果与试验数据吻合程度良好,误差率在8%以内,准确的反映了Ti-6Al-4V合金在不同加载温度下的真实应力-应变关系。 | 张铭 刘献礼 岳彩旭 Steven Y.LIANG 李恒帅 刘智博 | 2021 | 机械科学与技术2021，40，11： | 2 |
| 11 | Measurement of away-side broadening with self-subtraction of flow in Au+Au collisions at √sNN=200 GeV显示文摘High transverse momentum(pT)particle production is suppressed owing to the parton(jet)energy loss in the hot dense medium created in relativistic heavy-ion collisions.Redistribution of energy at low-to-modest pT has been difficult to measure,owing to large anisotropic backgrounds.We report a data-driven method for background evaluation and subtraction,exploiting the away-side pseudorapidity gaps,to measure the jetlike correlation shape in Au+Au collisions at √sNN=200 GeV in the STAR experiment.The correlation shapes,for trigger particles pT>3GeV/c and various associated particle pT ranges within 0.5 | L.Adamczyk J.R.Adams J.K.Adkins G.Agakishiev M.M.Aggarwal Z.Ahammed I.Alekseev D.M.Anderson A.Aparin E.C.Aschenauer M.U.Ashraf F.G.Atetalla A.Attri G.S.Averichev V.Bairathi K.Barish A.Behera R.Bellwied A.Bhasin J.Bielcik J.Bielcikova L.C.Bland I.G.Bordyuzhin J.D.Brandenburg A.V.Brandin J.Butterworth H.Caines M.Calderón de la Barca Sánchez D.Cebra I.Chakaberia P.Chaloupka B.K.Chan F-H.Chang Z.Chang N.Chankova-Bunzarova A.Chatterjee D.Chen J.H.Chen X.Chen Z.Chen J.Cheng M.Cherney M.Chevalier S.Choudhury W.Christie X.Chu H.J.Crawford M.Csanád M.Daugherity T.G.Dedovich I.M.Deppner A.A.Derevschikov L.Didenko X.Dong J.L.Drachenberg J.C.Dunlop T.Edmonds N.Elsey J.Engelage G.Eppley S.Esumi O.Evdokimov A.Ewigleben O.Eyser R.Fatemi S.Fazio P.Federic J.Fedorisin C.J.Feng Y.Feng P.Filip E.Finch Y.Fisyak A.Francisco L.Fulek C.A.Gagliardi T.Galatyuk F.Geurts A.Gibson K.Gopal D.Grosnick W.Guryn A.I.Hamad A.Hamed S.Harabasz J.W.Harris S.He W.He X.H.He S.Heppelmann S.Heppelmann N.Herrmann E.Hoffman L.Holub Y.Hong S.Horvat Y.Hu H.Z.Huang S.L.Huang T.Huang X.Huang T.J.Humanic P.Huo G.Igo D.Isenhower W.W.Jacobs C.Jena A.Jentsch Y.JI J.Jia K.Jiang S.Jowzaee X.Ju E.G.Judd S.Kabana M.L.Kabir S.Kagamaster D.Kalinkin K.Kang D.Kapukchyan K.Kauder H.W.Ke D.Keane A.Kechechyan M.Kelsey Y.V.Khyzhniak D.P.Kikoła C.Kim B.Kimelman D.Kincses T.A.Kinghorn I.Kisel A.Kiselev M.Kocan L.Kochenda L.K.Kosarzewski L.Kramarik P.Kravtsov K.Krueger N.Kulathunga Mudiyanselage L.Kumar S.Kumar R.Kunnawalkam Elayavalli J.H.Kwasizur R.Lacey S.Lan J.M.Landgraf J.Lauret A.Lebedev R.Lednicky J.H.Lee Y.H.Leung C.Li W.Li W.Li X.Li Y.Li Y.Liang R.Licenik T.Lin Y.Lin M.A.Lisa F.Liu H.Liu P.Liu P.Liu T.Liu X.Liu Y.Liu Z.Liu T.Ljubicic W.J.Llope R.S.Longacre N.S.Lukow S.Luo X.Luo G.L.Ma L.Ma R.Ma Y.G.Ma N.Magdy R.Majka D.Mallick S.Margetis C.Markert H.S.Matis J.A.Mazer N.G.Minaev S.Mioduszewski B.Mohanty I.Mooney Z.Moravcova D.A.Morozov M.Nagy J.D.Nam Nasim Md K.Nayak D.Neff J.M.Nelson D.B.Nemes M.Nie G.Nigmatkulov T.Niida L.V.Nogach T.Nonaka A.S.Nunes G.Odyniec A.Ogawa S.Oh V.A.Okorokov B.S.Page R.Pak A.Pandav Y.Panebratsev B.Pawlik D.Pawlowska H.Pei C.Perkins L.Pinsky R.L.Pintér J.Pluta J.Porter M.Posik N.K.Pruthi M.Przybycien J.Putschke H.Qiu A.Quintero S.K.Radhakrishnan S.Ramachandran R.L.Ray R.Reed H.G.Ritter O.V.Rogachevskiy J.L.Romero L.Ruan J.Rusnak N.R.Sahoo H.Sako S.Salur J.Sandweiss S.Sato W.B.Schmidke N.Schmitz B.R.Schweid F.Seck J.Seger M.Sergeeva R.Seto P.Seyboth N.Shah E.Shahaliev P.V.Shanmuganathan M.Shao A.I.Sheikh F.Shen W.Q.Shen S.S.Shi Q.Y.Shou E.P.Sichtermann R.Sikora M.Simko J.Singh S.Singha N.Smirnov W.Solyst P.Sorensen H.M.Spinka B.Srivastava T.D.S.Stanislaus M.Stefaniak D.J.Stewart M.Strikhanov B.Stringfellow A.A.P.Suaide M.Sumbera B.Summa X.M.Sun X.Sun Y.Sun Y.Sun B.Surrow D.N.Svirida P.Szymanski A.H.Tang Z.Tang A.Taranenko T.Tarnowsky J.H.Thomas A.R.Timmins D.Tlusty M.Tokarev C.A.Tomkiel S.Trentalange R.E.Tribble P.Tribedy S.K.Tripathy O.D.Tsai Z.Tu T.Ullrich D.G.Underwood I.Upsal G.Van Buren J.Vanek A.N.Vasiliev I.Vassiliev F.Videbæk S.Vokal S.A.Voloshin F.Wang G.Wang J.S.Wang P.Wang Y.Wang Y.Wang Z.Wang J.C.Webb P.C.Weidenkaff L.Wen G.D.Westfall H.Wieman S.W.Wissink R.Witt Y.Wu Z.G.Xiao G.Xie W.Xie H.Xu N.Xu Q.H.Xu Y.F.Xu Y.Xu Z.Xu Z.Xu C.Yang Q.Yang S.Yang Y.Yang Z.Yang Z.Ye Z.Ye L.Yi K.Yip H.Zbroszczyk W.Zha C.Zhang D.Zhang S.Zhang S.Zhang X.P.Zhang Y.Zhang Y.Zhang Z.J.Zhang Z.Zhang Z.Zhang J.Zhao C.Zhong C.Zhou X.Zhu Z.Zhu M.Zurek M.Zyzak | 2020 | Chinese Physics C2020，44，10： | 2 |
| 12 | 轴向超声振动辅助铣削力的建模与实验研究显示文摘轴向超声振动辅助铣削工艺是近年来铣削领域的最新进展之一,目前对于轴向超声振动铣削力建模的研究相对较少。针对这一问题,提出了一个考虑轴向振动的超声铣削力建模方法。根据空间中刀尖运动轨迹的坐标,建立了准确的瞬时切厚模型,得到不同切削角度下切屑形成力和摩擦力模型。将铣削力与未变形的切屑截面面积建立函数关系,考虑了瞬时未变形切厚模型以及瞬时切深模型,得到了轴向冲击力模型。结合切屑形成力模型、摩擦力模型和轴向冲击力模型得到轴向超声振动辅助铣削力模型。研究结果表明:预测的铣削力变化趋向与实验测得的铣削力变化趋向吻合,最大峰值力的误差范围在7.53%~17.35%之间。本文结果将为超声振动辅助铣削工艺的优化研究提供理论基础。 | 魏学涛 岳彩旭 刘献礼 严复钢 魏士亮 Steven Y.Liang | 2021 | 机械科学与技术2021，40，12： | 2 |
| 13 | Identification of proteins differentially expressed between capillary endothelial cells of hepatocellular carcinoma and normal liver in an orthotopic rat tumor model using 2‐D DIGE显示文摘 | JinghuiJia JingyuWang MingTeh WeiSun JianhuaZhang IreneKee Pierce K.‐H.Chow Rosa Cynthia M.‐Y.Liang Maxey C. M.Chung RuowenGe | 2010 | Proteomics2010，，2： | 1 |
| 14 | Facile Synthesis of Novel Ionic Liquids Containing Dithiocarbamate显示文摘 | D.Zhan J.Chen Y.Liang | | 0，，4： | 1 |
| 15 | Preparation of the Novel Nanocomposite Co(OH)2/ Ultra‐Stable Y?Zeolite and Its Application as a Supercapacitor with High Energy Density显示文摘 | L.Cao F.Xu Y.‐Y.Liang | 2004 | Adv. Mater2004，，20： | 1 |
| 16 | Optimal linear state estimator with multiple packet dropouts显示文摘 | Y.Liang T.W.Chen Q.Pan | | 0，，06： | 1 |
| 17 | Identification of proteins differentially expressed between capillary endothelial cells of hepatocellular carcinoma and normal liver in an orthotopic rat tumor model using 2‐D DIGE显示文摘 | JinghuiJia JingyuWang MingTeh WeiSun JianhuaZhang IreneKee Pierce K.‐H.Chow Rosa Cynthia M.‐Y.Liang Maxey C. M.Chung RuowenGe | 2010 | Proteomics2010，，2： | 1 |
| 18 | A model to evaluate acquisition price and quantity of used products for remanufacturing显示文摘 | Pokharel S Y.Liang | | 0，，01： | 1 |
| 19 | Two‐dimensional electrophoresis map of the human hepatocellular carcinoma cell line, HCC‐M, and identification of the separated proteins by mass spectrometry显示文摘 | Teck KeongSeow Shao‐EnOng Rosa C. M. Y.Liang Ee‐CheeRen LilyChan KeliOu Maxey C. M.Chung | 2000 | ELECTROPHORESIS2000，，9： | 1 |
| 20 | Joint beamforming and power allocation for multiple access channels in cognitive radio networks显示文摘 | L.Zhang Y.Liang Y.Xin | | 0，，： | 1 |
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