A Probabilistic View of Problems in Form Error Estimation

Yang, Tai-Hung; Jackman, John

doi:10.1115/1.2831116

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…First, sort the data in decreasing order, then calculate shrinkage and beam offset based on the maximum dimension and other dimensions. The idea of this method is to use maximum differences between dimensions to avoid the calculation and measure errors (Yang and Jackman, 1997). This method is simple and accurate.…”

Section: Methods 1 ± Maximum Basementioning

confidence: 99%

Calibration of shrinkage and beam offset in SLS process

Wang

1999

Rapid Prototyping Journal

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Selective laser sintering (SLS) is one of the most successful rapid tooling processes. Shrinkage and beam offset are the two most important control parameters in this process. First, the SLS process and procedure of calibration are described. Second, based on the property of shrinkage and beam offset, a basic formula of shrinkage and beam offset is derived. Finally, the procedure of applying shrinkage (scaling factor) and beam offset is described.

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Section: Methods 1 ± Maximum Basementioning

confidence: 99%

Calibration of shrinkage and beam offset in SLS process

Wang

1999

Rapid Prototyping Journal

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“…Comparison analysis of form error estimation with other methods. For the curved surface, the form error estimation is using the MinMax method introduced by Yang and Jackman [57]. It is calculated by the sum of the maximum distance between the upper or lower measured points and the fitting curved surface (see Fig.…”

Section: 222mentioning

confidence: 99%

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

Lan

2015

Journal of Manufacturing Science and Engineering

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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.

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“…Estimates of the¯atness errors using all 26 896 points for each surface were obtained using the minmax method described in reference [14]. These estimates are referred to as the 'true'¯atness error for each surface given the high density of sample points.…”

Section: Estimation Of 'True'¯atnessmentioning

confidence: 99%

“…A large number of points from the interpolated surface are sampled and the minmax method used to estimate the minimum zone mean pro®le [14]. Figure 7 shows an example of a reconstructed surface by a Shannon sampling series using 100 sample points on a bored surface.…”

Section: Shannon Samplingmentioning

confidence: 99%

A Shannon sampling approach to form error estimation

Yang

Jackman

2002

Proceedings of the Institution of Mechanical Engineers, Part B:

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Form error estimation is a critical step in verifying that a product satis®es design speci®cations. It is demonstrated how Shannon sampling can be used to reconstruct surface pro®les if they are treated as band-limited signals. To demonstrate its use in form estimation, a Shannon sampling series is applied to¯atness error estimation for uniform sample points measured from ®ve common machined surfaces. The results indicate that Shannon sampling provides superior performance over using points directly for estimation.

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A Probabilistic View of Problems in Form Error Estimation

Cited by 8 publications

References 14 publications

Calibration of shrinkage and beam offset in SLS process

Calibration of shrinkage and beam offset in SLS process

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

A Shannon sampling approach to form error estimation

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