International Conference on Gears 2019最新文献

英文中文

Japanese standardization for gear steel qualification via evaluation of HV-scattering 日本齿轮钢高压散射评定合格标准

International Conference on Gears 2019

Pub Date : 1900-01-01 DOI: 10.51202/9783181023556-893

A. Kubo, M. Nagae, K. Kawasaki

引用次数: 0

Improvement of the noise and vibration behavior of an electromechanical brake booster – An integral approach 机电制动助力器噪声和振动性能的改进——一种集成方法

International Conference on Gears 2019

Pub Date : 1900-01-01 DOI: 10.51202/9783181023556-1431

J. Fechler, S. Oberle, M. Knopfle, V. Merz

引用次数: 0

Analysis on the efficiency of HypoGear hypgear的效率分析

International Conference on Gears 2019

Pub Date : 1900-01-01 DOI: 10.51202/9783181023556-127

F. Eigner, R. Michel, M. Berger

引用次数: 0

Investigation of the potential of using surrogate models in the gear design process 在齿轮设计过程中使用替代模型的可能性的调查

International Conference on Gears 2019

Pub Date : 1900-01-01 DOI: 10.51202/9783181023556-727

J. Brimmers, M. Willecke, C. Lopenhaus, C. Brecher

State of the Art Surrogate models, also known as response surface models or metamodels, are approximation models, which are based on mathematical functions (Ref.1). In engineering, surrogate models are used to correlate the input and output variables of experiments and simulations (Refs. 2–10). This is especially true for very time-consuming, costly or high number of experiments/ simulations. In this case, the surrogate model can be evaluated much faster in comparison to the experiment or complex simulation. This is most important for design space exploration or optimization where a high number of experiments of simulations is necessary (Ref. 5). In order to reduce the time effort, the extensive simulation is only performed for a reduced number of parameter sets. These initial parameter sets are defined by means of methods of design of experiment (DOE), e.g., fullfactorial sampling or latin hypercube sampling (Ref. 11). For computational problems a latin hypercube sampling or the Monte-Carlo approach (random sampling) is often used to identify the initial parameter sets. Once the initial parameter sets are identified, the simulation is performed at these given points. The results of the simulation are used to fit a surrogate model to the given input variables in order to approximate the system behavior of the engineering system. Possible approximation types for surrogate models are shown in Figure 1. The most common modeling types are models based on radial basis functions (RBF), kriging models, also known as Gaussian process models, and models based on multivariate adaptive regression splines (MARS). RBFs are functions whose value only depends on the Euclidian distance from the origin (Ref. 12). An approximation model consists of a number of different radial basis functions, which are weighted accordingly. The weights of each basis functions are tuned in order to improve the quality of the approximation for the given number of data points. In the example in Figure 1 the function f(x) = 1 + sin(x2) was evaluated at six test data points and approximated by the usage of an RBF surrogate model consisting of Gaussian basis functions, as a type of RBF. The approximation follows the trend of the sine function but is not able to predict any of the test data points in high accordance. Kriging or Gaussian process models originate from geosciences and are usually used to predict the location of certain commodities like oil or gold for which only a finite number of boreholes exists (Ref. 13). The Gaussian process consists of two parts, one global and one local part. The global part can be

代理模型，也称为响应面模型或元模型，是基于数学函数的近似模型(参考文献1)。在工程中，替代模型用于关联实验和模拟的输入和输出变量(参考文献2-10)。这对于非常耗时、昂贵或大量的实验/模拟来说尤其如此。在这种情况下，与实验或复杂的模拟相比，可以更快地评估代理模型。这对于设计空间探索或优化是最重要的，因为大量的模拟实验是必要的(参考文献5)。为了减少时间，广泛的模拟只针对减少的参数集进行。这些初始参数集是通过实验设计(DOE)的方法定义的，例如，全因子抽样或拉丁超立方抽样(参考文献11)。对于计算问题，通常使用拉丁超立方抽样或蒙特卡罗方法(随机抽样)来确定初始参数集。一旦确定了初始参数集，就在这些给定的点上执行模拟。仿真结果被用来拟合一个替代模型来拟合给定的输入变量，以近似工程系统的系统行为。代理模型可能的近似类型如图1所示。最常见的建模类型是基于径向基函数(RBF)的模型、克里格模型(也称为高斯过程模型)和基于多变量自适应回归样条(MARS)的模型。rbf是函数，其值仅取决于到原点的欧几里得距离(参考文献12)。一个近似模型由许多不同的径向基函数组成，并对它们进行相应的加权。对每个基函数的权重进行调整，以提高给定数据点数量的近似质量。在图1的示例中，函数f(x) = 1 + sin(x2)在六个测试数据点上进行评估，并使用由高斯基函数组成的RBF代理模型作为RBF的一种类型进行近似。近似遵循正弦函数的趋势，但不能高度一致地预测任何测试数据点。克里金或高斯过程模型起源于地球科学，通常用于预测某些商品的位置，如石油或黄金，这些商品只有有限数量的钻孔存在(参考文献13)。高斯过程由两个部分组成，一个是全局部分，一个是局部部分。全局部分可以是

{"title":"Investigation of the potential of using surrogate models in the gear design process","authors":"J. Brimmers, M. Willecke, C. Lopenhaus, C. Brecher","doi":"10.51202/9783181023556-727","DOIUrl":"https://doi.org/10.51202/9783181023556-727","url":null,"abstract":"State of the Art Surrogate models, also known as response surface models or metamodels, are approximation models, which are based on mathematical functions (Ref.1). In engineering, surrogate models are used to correlate the input and output variables of experiments and simulations (Refs. 2–10). This is especially true for very time-consuming, costly or high number of experiments/ simulations. In this case, the surrogate model can be evaluated much faster in comparison to the experiment or complex simulation. This is most important for design space exploration or optimization where a high number of experiments of simulations is necessary (Ref. 5). In order to reduce the time effort, the extensive simulation is only performed for a reduced number of parameter sets. These initial parameter sets are defined by means of methods of design of experiment (DOE), e.g., fullfactorial sampling or latin hypercube sampling (Ref. 11). For computational problems a latin hypercube sampling or the Monte-Carlo approach (random sampling) is often used to identify the initial parameter sets. Once the initial parameter sets are identified, the simulation is performed at these given points. The results of the simulation are used to fit a surrogate model to the given input variables in order to approximate the system behavior of the engineering system. Possible approximation types for surrogate models are shown in Figure 1. The most common modeling types are models based on radial basis functions (RBF), kriging models, also known as Gaussian process models, and models based on multivariate adaptive regression splines (MARS). RBFs are functions whose value only depends on the Euclidian distance from the origin (Ref. 12). An approximation model consists of a number of different radial basis functions, which are weighted accordingly. The weights of each basis functions are tuned in order to improve the quality of the approximation for the given number of data points. In the example in Figure 1 the function f(x) = 1 + sin(x2) was evaluated at six test data points and approximated by the usage of an RBF surrogate model consisting of Gaussian basis functions, as a type of RBF. The approximation follows the trend of the sine function but is not able to predict any of the test data points in high accordance. Kriging or Gaussian process models originate from geosciences and are usually used to predict the location of certain commodities like oil or gold for which only a finite number of boreholes exists (Ref. 13). The Gaussian process consists of two parts, one global and one local part. The global part can be","PeriodicalId":260409,"journal":{"name":"International Conference on Gears 2019","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125302902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analysis of manufacturing variability on the transmission error of an internal gear pair using a multibody framework 多体框架内齿轮副传动误差的制造变异性分析

International Conference on Gears 2019

Pub Date : 1900-01-01 DOI: 10.51202/9783181023556-1099

C. Natali, A. Rezayat, D. Park, P. Jiránek, T. Tamarozzi

引用次数: 1

DIN Q6 meets DIN Q10 – Need for modern internal gear production DIN Q6满足DIN Q10 -现代化内齿轮生产的需要

International Conference on Gears 2019

Pub Date : 1900-01-01 DOI: 10.51202/9783181023556-415

T. Kleiber

引用次数: 1

Design and rating by means of loaded TCA of straight bevel differential gears 用负载TCA法设计直齿锥齿轮差动齿轮

International Conference on Gears 2019

Pub Date : 1900-01-01 DOI: 10.51202/9783181023556-493

J. Thomas, C. Gosselin, P. Eng.

引用次数: 0

Influence of tooth profile modification on planetary gear sets dynamic 齿形修形对行星齿轮组动力学的影响

International Conference on Gears 2019

Pub Date : 1900-01-01 DOI: 10.51202/9783181023556-777

D. Kalinin, J. Y. Temis

引用次数: 2

Transfer of the tooth flank stress into an analogy test concept to provoke the damage pattern tooth flank fracture (TFF) 将齿面应力转化为模拟试验概念，引发齿面断裂损伤模式

International Conference on Gears 2019

Pub Date : 1900-01-01 DOI: 10.51202/9783181023556-25

C. Brecher, C. Lopenhaus, F. Goergen

引用次数: 1

Investigation on dynamic behavior of differential gearing with herringbone teeth 人字齿差动齿轮传动动力学特性研究

International Conference on Gears 2019

Pub Date : 1900-01-01 DOI: 10.51202/9783181023556-1215

K. Liu, H. Liu, W. Shi

引用次数: 0

首页上一页

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

International Conference on Gears 2019

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀