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Marling, H. (2014) *Statistical Methods for Estimation of Paint Thickness and its Variance*. Göteborg : Chalmers University of Technology

** BibTeX **

@misc{

Marling2014,

author={Marling, Hannes},

title={Statistical Methods for Estimation of Paint Thickness and its Variance},

abstract={IPS Virtual Paint at Fraunhofer Chalmers Centre (FCC) is a software for simulating
electrostatic spray painting of objects. Simulating the painting process is extremely
computationally demanding and hence very time consuming. It is therefore desirable to
be able to obtain results based on as few number of simulations as possible. This, and
the random behavior of the simulations, give rise to randomness in the estimated paint
thickness that is not easily quantified by means of ordinary methods. The purpose of
this thesis is to further develop methods for estimating the resulting paint thickness and
its variance.
Different varieties of models based on nonparametric kernel density estimation for
estimating of paint thickness were evaluated. This was done using synthetic data, along
with data generated via IPS Virtual Paint. Variations of anisotropic kernel estimations
for reducing bias in the estimates were also investigated. Also, methods for enhancing
existing methods when performing paint thickness estimations along the edges of an object
were developed. Both the anisotropic methods together with the edge compensation
algorithms showed to improve the quality of the estimates.
Previous work at FCC have used regression models for estimating the variance of the
estimated paint thickness. This method is however not applicable for the entire object.
In this report an alternative method, based on bootstrap, for estimating the variance
is analyzed. The results show that bootstrap performs well for the different scenarios
investigated, with results consistent with regression models and more exact estimates
produced through numerous painting simulations.},

publisher={Institutionen för matematiska vetenskaper, matematisk statistik, Chalmers tekniska högskola,},

place={Göteborg},

year={2014},

note={66},

}

** RefWorks **

RT Generic

SR Electronic

ID 199241

A1 Marling, Hannes

T1 Statistical Methods for Estimation of Paint Thickness and its Variance

YR 2014

AB IPS Virtual Paint at Fraunhofer Chalmers Centre (FCC) is a software for simulating
electrostatic spray painting of objects. Simulating the painting process is extremely
computationally demanding and hence very time consuming. It is therefore desirable to
be able to obtain results based on as few number of simulations as possible. This, and
the random behavior of the simulations, give rise to randomness in the estimated paint
thickness that is not easily quantified by means of ordinary methods. The purpose of
this thesis is to further develop methods for estimating the resulting paint thickness and
its variance.
Different varieties of models based on nonparametric kernel density estimation for
estimating of paint thickness were evaluated. This was done using synthetic data, along
with data generated via IPS Virtual Paint. Variations of anisotropic kernel estimations
for reducing bias in the estimates were also investigated. Also, methods for enhancing
existing methods when performing paint thickness estimations along the edges of an object
were developed. Both the anisotropic methods together with the edge compensation
algorithms showed to improve the quality of the estimates.
Previous work at FCC have used regression models for estimating the variance of the
estimated paint thickness. This method is however not applicable for the entire object.
In this report an alternative method, based on bootstrap, for estimating the variance
is analyzed. The results show that bootstrap performs well for the different scenarios
investigated, with results consistent with regression models and more exact estimates
produced through numerous painting simulations.

PB Institutionen för matematiska vetenskaper, matematisk statistik, Chalmers tekniska högskola,

LA eng

LK http://publications.lib.chalmers.se/records/fulltext/199241/199241.pdf

OL 30