Discrete Model Reference Adaptive Control System for Automatic Profiling Machine by Peng Song, Guo-kai Xu, Xiu-chun Zhao

“…In this paper, the discrete model reference adaptive control system of automatic profiling machine is discussed. Firstly, the model of automatic profiling machine is presented according to the parameters of DC motor. …”
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Optimization of a Redundant Freedom Machining Toolpath for Scroll Profile Machining by Song Gao, Zifang Hu, Huicheng Zhou, Jiejun Xie, Chenglei Zhang, Xiaohan Zhang

“…This paper addresses issues related to unsmooth toolpaths of machine tool axes and high acceleration in the rotary axis during redundant degrees of freedom scroll profile machining. This paper proposes a toolpath optimization method for redundant axes, with optimization objectives focused on reducing the counts of directional changes in the linear axes and smoothing the trajectories of all axes. …”
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RESULTS OF EXPLOITATION OF CRYSTALLIZERS SLEEVES CONSTRUCTION «VM-SINUS» ON PROFILED MACHINES OF BILLETS CONTINUOUS CASTING OF RUP «BMZ» by A. V. Demin

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Machine Learning of Serum Cytokine and Chemokine Profiles Can Classify Inflammatory Bowel Disease Beyond Clinical Diagnosis by Jun Miyoshi, Satoshi Tamura, Noriaki Oguri, Daisuke Saito, Yuu Nishinarita, Haruka Wada, Nobuki Nemoto, Minoru Matsuura, Tadakazu Hisamatsu

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硬齿面齿轮滚挤技术及有限元分析 by 任志刚, 郑鹏

“…A ausform-finishing process,which integrates the technology of heat treatment,hardening of steel components and tooth profile machining into a manufacturing operation,is discussed,and the machining principle of Ausform-finishing is represented.Then the gear steel 20CrMo which is widely used in auto and precise machine industry is analyzed by ANSYS software.A better basis for the experimental investigation of this technology is provided.…”
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Analysis of the Error Sensitive Direction and Establishment of the Equation of Face Gear Tooth Surface with Error by Ming Xingzu, Li Mande, Wang Wei, Zhao Lei

“…Based on the theory of multi-body system and the principle of gear meshing,according to six-axis five linkage gear CNC machine structure and the actual conditions of grinding,the grinding tooth surface equation of face gear with error of six-axis five linkage CNC machine is established.Through the simulation,the influence of the grinding wheel spindle kinematic pair on different error sensitive direction is analyzed,the results show that the feed axis B5 relative to machine tool bed B1 moving has larger effect on the tooth profile machining of face gear.A prejudge theory for CNC grinding to improve the machining precision and modified tooth surface error is provided.…”
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基于APDL与高级语言的齿轮有限元建模方法 by 唐进元, 刘艳萍, 周长江, 吕文利

“…According to the method for plane curves synthesis, conjugate curves cut by the different parts of the hob are composed numerically, and the datum of complex tooth profile are calculated exactly.The datum are called by APDL in ANSYS, then FEM geometrical model of complex tooth profile machined by pre-grinding hob are established accurately.Combined by strong numeric analysis and treatment of MATLAB and the facility and openness of APDL, compositive programming with MATLAB and APDL is advanced based on full-text data files.…”
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中心蜗杆范成法数控加工廓面误差综合研究 by 胡自化, 陈生墨, 陈小告, 张华

“…In order to explore the influence law of original errors included machine tool kinematic errors,tool wear errors on the profile machining accuracy of sun-worm,based on the multi-body system error-modeling method and the conjugate meshing theory of spatial mechanism,the sunworm profile equation included these errors is deduced.The normal error between worm theoretical profile and error profile is calculated by using Newton iteration method and the result is analyzed to describe the effect of each error and comprehensive error on sun-worm profile accuracy.The main research conclusion can effectively disclosure and predict the law of influence that the various errors for sun-worm profile normal error.Consequently,a scientific proof is supplied to compensate machine geometric errors and improve the precision and quality of the sun-worm.…”
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MEA‐seqX: High‐Resolution Profiling of Large‐Scale Electrophysiological and Transcriptional Network Dynamics by Brett Addison Emery, Xin Hu, Diana Klütsch, Shahrukh Khanzada, Ludvig Larsson, Ionut Dumitru, Jonas Frisén, Joakim Lundeberg, Gerd Kempermann, Hayder Amin

“…This platform also identifies different cell types based on their distinct bimodal profiles. Machine‐learning algorithms accurately predict network‐wide electrophysiological activity features from spatial gene expression, demonstrating a previously inaccessible convergence across modalities, time, and scales.…”
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Breast cancer is detectable from peripheral blood using machine learning over T cell receptor repertoires by Miriam Zuckerbrot-Schuldenfrei, Ari Raphael, Alona Zilberberg, Sol Efroni

“…We collected blood samples from 98 women, including patients and healthy donors. Following TCR profiling, machine learning of these data was able to show an association between TCR profiles and breast cancer presence or absence with high accuracy (average AUC of 0.96). …”
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