Prediction of Comprehensive Thermal Error of a Preloaded Ball Screw on a Gantry Milling Machine

2017 ◽  
Vol 140 (2) ◽  
Author(s):  
Kuo Liu ◽  
Haibo Liu ◽  
Te Li ◽  
Yongqing Wang ◽  
Mingjia Sun ◽  
...  
Keyword(s):  
Thermal Field ◽  
Thermal Characteristics ◽  
Thermal Error ◽  
Least Square Method ◽  
High Accuracy ◽  
Least Square ◽  
Ball Screw ◽  
Milling Machine ◽  
Thermal Fields ◽  
Proposed Model

The conception of the comprehensive thermal error of servo axes is given. Thermal characteristics of a preloaded ball screw on a gantry milling machine is investigated, and the error and temperature data are obtained. The comprehensive thermal error is divided into two parts: thermal expansion error ((TEE) in the stroke range) and thermal drift error ((TDE) of origin). The thermal mechanism and thermal error variation of preloaded ball screw are expounded. Based on the generation, conduction, and convection theory of heat, the thermal field models of screw caused by friction of screw-nut pairs and bearing blocks are derived. The prediction for TEE is presented based on thermal fields of multiheat sources. Besides, the factors influencing TDE are analyzed, and the model of TDE is established based on the least square method. The predicted thermal field of the screw is analyzed. The simulation and experimental results indicate that high accuracy stability can be obtained using the proposed model. Moreover, high accuracy stability can still be achieved even if the moving state of servo axis changes randomly, the screw is preloaded, and the thermal deformation process is complex. Strong robustness of the model is verified.

Thermal Error Robust Modeling for High-Speed Motorized Spindle

2012 ◽  
Vol 466-467 ◽  
pp. 961-965 ◽  
Author(s):  
Chun Li Lei ◽  
Zhi Yuan Rui ◽  
Jun Liu ◽  
Li Na Ren
Keyword(s):  
High Speed ◽  
Thermal Error ◽  
Least Square Method ◽  
Information Criterion ◽  
Least Square ◽  
Proposed Model ◽  
Nc Machine ◽  
Input Variables ◽  
Thermal Error Model ◽  
Multivariate Autoregressive

To improve the manufacturing accuracy of NC machine tool, the thermal error model based on multivariate autoregressive method for a motorized high speed spindle is developed. The proposed model takes into account influences of the previous temperature rise and thermal deformation (input variables) on the thermal error (output variables). The linear trends of observed series are eliminated by the first difference. The order of multivariate autoregressive (MVAR) model is selected by using Akaike information criterion. The coefficients of the MVAR model are determined by the least square method. The established MVAR model is then used to forecast the thermal error and the experimental results have shown the validity and robustness of this model.

The Circle-End-Face Centering Device Based on Image Processing

2014 ◽  
Vol 926-930 ◽  
pp. 3447-3450
Author(s):  
Yong Bo Li ◽  
Yun Zhao Xu ◽  
Jia He
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
High Stability ◽  
Least Square Method ◽  
High Accuracy ◽  
Concentric Circle ◽  
Least Square ◽  
Programmable Logic ◽  
Edge Extraction ◽  
Stability And Accuracy

The circle-end-face centering device which is in the base of image processing accomplishes the center of the cylinders end face by digital image processing. Through edge extraction and doing the matching in the calculation of the least square method, the coordinate of the central of the circle can be calculated. At last, the drill will be moved to the central of the circle and do the punching by the actuator. This equipment is in the high accuracy and speed compared with the traditional way and the concentric circle laser of marking with the center. Meanwhile, the actuator consists of the programmable logic controller, touch panel and servo drivers which is in the high stability and accuracy. Certainly it is easy to be controlled by the method.

Research of Quantitative Analyzer for Fluorescence Immune Chromatography Strips

2013 ◽  
Vol 373-375 ◽  
pp. 1000-1006
Author(s):  
Xiao Ting Li ◽  
Kai Jiang ◽  
Ping Wang
Keyword(s):  
Least Square Method ◽  
High Accuracy ◽  
Least Square ◽  
Good Prospect ◽  
Fluorescence Immunoassay ◽  
Response Characteristics ◽  
Photoelectric Detection ◽  
Detection Technology ◽  
Relative Standard ◽  
Low Pass

Fluorescence immunoassay technology is widely recognized as the most sensitive detection technology, now widely used in clinical testing. The research of quantitative analyzer for fluorescence immune chromatography strips is of great significance. In this thesis, a quantitative analyzer for fluorescence immune chromatography strips has realized, based on photoelectric detection principle and immunochromatographic. The system uses LPC1788 and MSP430 as processors. Using some algorithms such as low-pass filtering, least square method and area method to analyze the detected waveform and eliminate noise with high accuracy. In our experiment, the minimum detection concentration is at several mIU/ml levels, the relative standard deviation is less than 2% and it has good linear response characteristics in a concentration range from 0.5125 to 952mIU/ml while the correlation coefficient R2 is 0.981. The instrument has a good prospect.

Methods of Feature Weighting Calculation and Case Retrieval in CBR Case Base

2014 ◽  
Vol 490-491 ◽  
pp. 1391-1398
Author(s):  
Xiao Ping Li
Keyword(s):  
Least Squares Method ◽  
Least Square Method ◽  
High Accuracy ◽  
Typical Case ◽  
Feature Weighting ◽  
Least Square ◽  
Case Retrieval ◽  
Use Of Data ◽  
On Line ◽  
Simulation Results

This article proposes the necessity and feasibility of the use of Data Mining and Knowledge Discovery in CBR reasoning. This paper focuses on the method of empowering feature items based on least squares method parameter identification, and achieve the method of Similarity case retrieval on this basis, the object is the typical case database of railway rescue. The simulation results show that: the least square method can effectively make estimation and identification of the feature parameters, and can continuously correct on-line. High accuracy and fast convergence characteristics of the assigned parameters show that the algorithm has a certain application value.

500kV Substation Service Power System Modeling Research Based on Least Square Method with Parameter Optimization by SPO

2013 ◽  
Vol 860-863 ◽  
pp. 2534-2539
Author(s):  
Chang Ming Chen ◽  
Jiang Zeng ◽  
Xiang Hua Zhang
Keyword(s):  
Power System ◽  
Energy Saving ◽  
Parameter Optimization ◽  
Emission Reduction ◽  
System Modeling ◽  
Power Grid ◽  
Least Square Method ◽  
High Accuracy ◽  
Least Square ◽  
Grid Based

EHV substation service power is an important load in the power grid which is meaningful to be examined for the energy saving and emission reduction of the power grid. Based on the analysis of EHV substation service power and the improvement of the least square method, this paper get a new set of optimization which is is highly general and can reach high accuracy.

Thermal Error Compensation and Modeling for CNC Boring and Milling Center

2014 ◽  
Vol 945-949 ◽  
pp. 1665-1668
Author(s):  
Jun Sun ◽  
Yan Ping Xu ◽  
Xing Liu
Keyword(s):  
Error Compensation ◽  
Structural Characteristics ◽  
Thermal Error ◽  
Least Square Method ◽  
Measured Data ◽  
Least Square ◽  
Model Parameters ◽  
Heat Sources ◽  
Thermal Error Model ◽  
Thermal Error Compensation

The thermal error compensation and modeling is an effective way to improve the machining tool precision. This article took the CNC boring and milling center for example. Firstly, this article made analysis and research on the heat sources by analyzing the structural characteristics of CNC boring and milling center, and then on the basis of the previously measured data of selected critical temperature points, established the thermal error model using a method of multiple linear regression based on least square method. The model parameters were solved by MATLAB software. Finally, thermal error compensation model was tested.

scholarly journals Forming a Hierarchical Choquet Integral with a GA-Based Heuristic Least Square Method

Mathematics ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 1155
Author(s):  
Chen ◽  
Huang
Keyword(s):  
Choquet Integral ◽  
Least Square Method ◽  
Least Square ◽  
Integral Model ◽  
Fuzzy Measures ◽  
Proposed Model ◽  
Choquet Integrals ◽  
The Mean ◽  
Input Variables ◽  
Least Mean Squares Algorithm

: Identifying the fuzzy measures of the Choquet integral model is an important component in resolving complicated multi-criteria decision-making (MCDM) problems. Previous papers solved the above problem by using various mathematical programming models and regression-based methods. However, when considering complicated MCDM problems (e.g., 10 criteria), the presence of too many parameters might result in unavailable or inconsistent solutions. While k-additive or p-symmetric measures are provided to reduce the number of fuzzy measures, they cannot prevent the problem of identifying the fuzzy measures in a high-dimension situation. Therefore, Sugeno and his colleagues proposed a hierarchical Choquet integral model to overcome the problem, but it required the partition information of the criteria, which usually cannot be obtained in practice. In this paper, we proposed a GA-based heuristic least mean-squares algorithm (HLMS) to construct the hierarchical Choquet integral and overcame the above problems. The genetic algorithm (GA) was used here to determine the input variables of the sub-Choquet integrals automatically, according to the objective of the mean square error (MSE), and calculated the fuzzy measures with the HLMS. Then, we summed these sub-Choquet integrals into the final Choquet integral for the purpose of regression or classification. In addition, we tested our method with four datasets and compared these results with the conventional Choquet integral, logit model, and neural network. On the basis of the results, the proposed model was competitive with respect to other models.

Research on Algorithm for Non-Cooperative Target Inertial Parameters Identification

2014 ◽  
Vol 543-547 ◽  
pp. 1550-1554
Author(s):  
Zhi Gang Chen ◽  
Ju Shu ◽  
Su Xia Zhu
Keyword(s):  
Least Square Method ◽  
High Accuracy ◽  
Least Square ◽  
New Method ◽  
Recursive Method ◽  
Recursive Least Square ◽  
Inertial Parameters ◽  
Conservation Of Momentum ◽  
Simulation Results ◽  
Recursive Least Square Method

It is of great importance to accomplish identifying the non-cooperative targets inertial parameters for capturing and controlling it. This paper adopts an identification method utilizing the tactile and vision to establish the identification equations based on the principle of conservation of momentum. In order to solve the equations precisely and rapidly, two of the most widely used methods: the least square method and the recursive least-square method (RLS) are employed for simulation and calculation utilizing ADAMS and MATLAB, and the limitation of each method is pointed out. Then the paper proposes a new method named the recursive method based on the least square solution. This method provides the identification equation a new solving thought and realizes fast identification of the targets inertial parameters at high accuracy. The simulation results verify the validity and feasibility of the proposed approach.

Complementary Use of Partial Least-Squares and Artificial Neural Networks for the Annual Electricity Consumption Forecast

2012 ◽  
Vol 512-515 ◽  
pp. 1113-1116 ◽  
Author(s):  
Hui Feng Jiang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Electricity Consumption ◽  
Least Square Method ◽  
Partial Least Square ◽  
Least Square ◽  
Factors Affecting ◽  
The Neural Network ◽  
Proposed Model ◽  
Partial Least Square Method

A model for predicting annual electricity consumption based on the combination of neural network and partial least square method was proposed. The factors affecting the annual electricity consumption are analyzed by means of partial least square method to extract the most important components so that not only the problem of multi-correlation among variables can be solves but also the amount of input dimensions of the neural network can be reduced. Besides, the application of neural network helps to solve the problem of non-linearity of the model. The application example shows that the proposed model has high precision.

Application and Non-Homogeneous Index Grey Model NIGM(1,1,k)

2010 ◽  
Vol 34-35 ◽  
pp. 148-152
Author(s):  
Zhe Ming He ◽  
You Xin Luo ◽  
Bin Zeng
Keyword(s):  
Deductive Reasoning ◽  
Least Square Method ◽  
Least Square ◽  
Grey Model ◽  
Dispersion Index ◽  
Index Function ◽  
Proposed Model ◽  
Modeling Accuracy ◽  
Application Fields ◽  
Formula Computing

To improve the modeling accuracy of grey model and broaden its application fields, a non-homogeneous index grey model (termed NIGM(1,1,k)) was built, which is based on the non-homogeneous dispersion index function and the formula computing the parameters of grey model NIGM(1,1,k) was proposed through the least square method. The function of the time response sequence of the proposed grey model was solved by taking differential equations as a deductive reasoning tool. The proposed grey NIGM(1,1,k) model has the characteristic of high precision as well as high adaptability. Examples validate the practicability and reliability of the proposed model.

Sign in / Sign up

Export Citation Format

Share Document