scholarly journals Research on the application of multiplication dimension reduction method in global sensitivity analysis of CNC machine tools

AIP Advances ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 015029 ◽  
Author(s):  
Huachao Guo ◽  
Xiaojun Yang ◽  
Shuliang Wang ◽  
Zhongmou Dai
Keyword(s):  
Sensitivity Analysis ◽  
Dimension Reduction ◽  
Machine Tools ◽  
Reduction Method ◽  
Global Sensitivity Analysis ◽  
Cnc Machine Tools ◽  
Dimension Reduction Method ◽  
Cnc Machine ◽  
Global Sensitivity

Dimensionality Reduction of High-Fidelity Machine Tool Models by Using Global Sensitivity Analysis

2021 ◽  
pp. 1-13
Author(s):  
Johannes Ellinger ◽  
Thomas Semm ◽  
Michael F. Zäh
Keyword(s):  
Sensitivity Analysis ◽  
Machine Tool ◽  
Parameter Identification ◽  
Machine Tools ◽  
Global Sensitivity Analysis ◽  
Reference Model ◽  
Evaluation Criteria ◽  
Unknown Parameters ◽  
Global Sensitivity ◽  
Process Simulations

Abstract Models that are able to accurately predict the dynamic behavior of machine tools are crucial for a variety of applications ranging from machine tool design to process simulations. However, with increasing accuracy, the models tend to become increasingly complex, which can cause problems identifying the unknown parameters which the models are based on. In this paper, a method is presented that shows how parameter identification can be eased by systematically reducing the dimensionality of a given dynamic machine tool model. The approach presented is based on ranking the model's input parameters by means of a global sensitivity analysis. It is shown that the number of parameters, which need to be identified, can be drastically reduced with only limited impact on the model's fidelity. This is validated by means of model evaluation criteria and frequency response functions which show a mean conformity of 98.9 % with the full-scale reference model. The paper is concluded by a short demonstration on how to use the results from the global sensitivity analysis for parameter identification.

scholarly journals Global Quantitative Sensitivity Analysis and Compensation of Geometric Errors of CNC Machine Tool

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Shijie Guo ◽  
Dongsheng Zhang ◽  
Yang Xi
Keyword(s):  
Sensitivity Analysis ◽  
Machine Tool ◽  
Machine Tools ◽  
Geometric Error ◽  
Test Method ◽  
Cnc Machine Tools ◽  
Geometric Errors ◽  
Cnc Machine ◽  
Open Cnc ◽  
Open Cnc System

A quantitative analysis to identify the key geometric error elements and their coupling is the prerequisite and foundation for improving the precision of machine tools. The purpose of this paper is to identify key geometric error elements and compensate for geometric errors accordingly. The geometric error model of three-axis machine tool is built on the basis of multibody system theory; and the quantitative global sensitivity analysis (GSA) model of geometric error elements is constructed by using extended Fourier amplitude sensitivity test method. The crucial geometric errors are identified; and stochastic characteristics of geometric errors are taken into consideration in the formulation of building up the compensation strategy. The validity of geometric error compensation based on sensitivity analysis is verified on a high-precision three-axis machine tool with open CNC system. The experimental results show that the average compensation rates along theX,Y, andZdirections are 59.8%, 65.5%, and 73.5%, respectively. The methods of sensitivity analysis and geometric errors compensation presented in this paper are suitable for identifying the key geometric errors and improving the precision of CNC machine tools effectively.

scholarly journals Improving multi-objective reservoir operation optimization with sensitivity-informed dimension reduction

2015 ◽  
Vol 19 (8) ◽  
pp. 3557-3570 ◽  
Author(s):  
J. Chu ◽  
C. Zhang ◽  
G. Fu ◽  
Y. Li ◽  
H. Zhou
Keyword(s):  
Sensitivity Analysis ◽  
Dimension Reduction ◽  
Reservoir Operation ◽  
Global Sensitivity Analysis ◽  
Optimization Problems ◽  
Liaoning Province ◽  
Global Sensitivity ◽  
Multi Objective ◽  
Decision Variables ◽  
Efficiency And Effectiveness

Abstract. This study investigates the effectiveness of a sensitivity-informed method for multi-objective operation of reservoir systems, which uses global sensitivity analysis as a screening tool to reduce computational demands. Sobol's method is used to screen insensitive decision variables and guide the formulation of the optimization problems with a significantly reduced number of decision variables. This sensitivity-informed method dramatically reduces the computational demands required for attaining high-quality approximations of optimal trade-off relationships between conflicting design objectives. The search results obtained from the reduced complexity multi-objective reservoir operation problems are then used to pre-condition the full search of the original optimization problem. In two case studies, the Dahuofang reservoir and the inter-basin multi-reservoir system in Liaoning province, China, sensitivity analysis results show that reservoir performance is strongly controlled by a small proportion of decision variables. Sensitivity-informed dimension reduction and pre-conditioning are evaluated in their ability to improve the efficiency and effectiveness of multi-objective evolutionary optimization. Overall, this study illustrates the efficiency and effectiveness of the sensitivity-informed method and the use of global sensitivity analysis to inform dimension reduction of optimization problems when solving complex multi-objective reservoir operation problems.

Reliability and Sensitivity Analysis of Mechanical Structures Based on Dimension Reduction Method

2014 ◽  
Vol 635-637 ◽  
pp. 411-416
Author(s):  
Shu Xin Zhang ◽  
Kai Chao Yu
Keyword(s):  
Sensitivity Analysis ◽  
Dimension Reduction ◽  
Reduction Method ◽  
Random Variables ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Dimension Reduction Method ◽  
Distribution Parameters ◽  
Analytic Derivation ◽  
Random Responses

This paper presents a method for probabilistic sensitivity analysis of mechanical components or structural systems subject to random uncertainties in loads, material properties and geometry. The bi-variate dimension reduction method is applied to compute the response moments and their sensitivities with respect to the distribution parameters of basic random variables. Saddlepoint approximations with truncated cumulant generating functions are employed to estimate the probability density functions and cumulative distribution functions of the random responses. The rigorous analytic derivation of the sensitivities of the probability of failure of the systems under consideration with respect to the distribution parameters of basic random variables is derived. Finally, the practicality and efficiency of the proposed method are demonstrated by an application example.

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