scholarly journals Form Error Estimation Using Spatial Statistics

1999 ◽  
Vol 122 (1) ◽  
pp. 262-272 ◽  
Author(s):  
Tai-Hung Yang ◽  
John Jackman
Keyword(s):  
Error Estimates ◽  
Statistical Methods ◽  
Spatial Statistics ◽  
Error Estimation ◽  
Manufacturing Processes ◽  
Form Error ◽  
Essential Step ◽  
Large Sets ◽  
Uniform Sample ◽  
Estimate Form

Form error estimation is an essential step in the assessment of product geometry created through one or more manufacturing processes. We present a new method using spatial statistics to estimate form error. Using large sets of uniform sample points measured from five common machined surfaces, we compare the form error estimates using individual points and fitted surfaces obtained through spatial statistical methods. The results show that spatial statistics can provide more accurate estimates of form error under certain conditions. [S1087-1357(00)01701-9]

Co-Kriging Method for Form Error Estimation Incorporating Condition Variable Measurements

2015 ◽  
Vol 138 (4) ◽  
Author(s):  
Shichang Du ◽  
Lan Fei
Keyword(s):  
Spatial Correlation ◽  
Spatial Statistics ◽  
Error Estimation ◽  
Surface Form ◽  
Form Error ◽  
Machining Processes ◽  
Machining Conditions ◽  
Machining Errors ◽  
Part Surface ◽  
Cross Variogram

The form error estimation under various machining conditions is an essential step in the assessment of product surface quality generated in machining processes. Coordinate measuring machines (CMMs) are widely used to measure complicated surface form error. However, considering measurement cost, only a few measurement points are collected offline by a CMM for a part surface. Therefore, spatial statistics is adopted to interpolate more points for more accurate form error estimation. It is of great significance to decrease the deviation between the interpolated height value and the real one. Compared to univariate spatial statistics, only concerning spatial correlation of height value, this paper presents a method based on multivariate spatial statistics, co-Kriging (CK), to estimate surface form error not only concerning spatial correlation but also concerning the influence of machining conditions. This method can reconstruct a more accurate part surface and make the estimation deviation smaller. It characterizes the spatial correlation of machining errors by variogram and cross-variogram, and it is implemented on one of the common features: flatness error. Simulated datasets as well as actual CMM data are applied to demonstrate the improvement achieved by the proposed multivariate spatial statistics method over the univariate method and other interpolation methods.

Error estimation and bias correction in phase-improvement calculations

1999 ◽  
Vol 55 (9) ◽  
pp. 1555-1567 ◽  
Author(s):  
Kevin Cowtan
Keyword(s):  
Error Estimates ◽  
Error Estimation ◽  
Bayesian Methods ◽  
Bias Correction ◽  
Phase Error ◽  
Acta Cryst

With the rise of Bayesian methods in crystallography, the error estimates attached to estimated phases are becoming as important as the phase estimates themselves. Phase improvement by density modification can cause problems in this environment because the quality of the resulting phases is usually overestimated. This problem is addressed by an extension of the γ correction [Abrahams (1997). Acta Cryst. D53, 371–376] to arbitrary density-modification techniques. The degree to which the improved phases are biased by the features of the initial map is investigated in order to determine the limits of the resulting procedure and the quality of the phase-error estimates.

Statistical Methods in the Automotive Industry

2015 ◽  
Vol 791 ◽  
pp. 56-62
Author(s):  
Ewa Golińska ◽  
Marcin Zemczak
Keyword(s):  
Decision Making ◽  
Quality Management ◽  
Statistical Process Control ◽  
Statistical Methods ◽  
Automotive Industry ◽  
Manufacturing Processes ◽  
Key Factors ◽  
High Quality ◽  
Statistical Process ◽  
Decision Making Processes

Controlling of the manufacturing processes is one of the key factors that allow companies to produce goods of high quality. In the article one of methods of the quality management - statistical process control (SPC) has been discussed. On the example of the car headrests manufacturing enterprise benefits from applying this method have been presented. Authors pointed out that the use of statistical methods in decision-making processes must not be difficult and time-consuming, if is being supported by a computer with using inexpensive tools.

scholarly journals Election Result Prediction using Spatial Statistical Method

2019 ◽  
Vol 8 (2S6) ◽  
pp. 838-843
Keyword(s):  
Data Analysis ◽  
Statistical Method ◽  
Statistical Methods ◽  
Spatial Statistics ◽  
Exploratory Data Analysis ◽  
17Th Century ◽  
Election Result ◽  
Exploratory Data ◽  
Modern Representative ◽  
Democratic Country

Since 17th Century, Modern representative democracy in all forms is being made possible by having a formal way of collecting people's overall opinion on who is to be chosen as their representative. Election is a most important event in any democratic country. Confronting on predicting the winning person/party in the election is the biggest challenge here. In this paper, a study of spatial statistics method to predict the election result using the exploratory data analysis is carried out by applying spatial statistical methods. The paper presents, prediction of the election result with respect to the income level of the citizen and also a comparison of results obtained from the different statistical methods

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