Neural Network-based Model for Supporting the Expert Driven Project Estimation Process in Mold Manufacturing

FLORJANIČ, Blaž ;GOVEKAR, Edvard ;KUZMAN, Karl .
Neural Network-based Model for Supporting the Expert Driven Project Estimation Process in Mold Manufacturing. 
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 59, n.1, p. 3-13, june 2018. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/sl/article/neural-network-based-model-for-supporting-the-expert-driven-project-estimation-process-in-mold-manufacturing/>. Date accessed: 20 dec. 2024. 
doi:http://dx.doi.org/10.5545/sv-jme.2012.747.

Florjanič, B., Govekar, E., & Kuzman, K.
(2013).
Neural Network-based Model for Supporting the Expert Driven Project Estimation Process in Mold Manufacturing.
Strojniški vestnik - Journal of Mechanical Engineering, 59(1), 3-13.
doi:http://dx.doi.org/10.5545/sv-jme.2012.747

@article{sv-jmesv-jme.2012.747,
	author = {Blaž  Florjanič and Edvard  Govekar and Karl  Kuzman},
	title = {Neural Network-based Model for Supporting the Expert Driven Project Estimation Process in Mold Manufacturing},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {59},
	number = {1},
	year = {2013},
	keywords = {mold making; manufacturing; artificial neural networks; estimation process},
	abstract = {One of the crucial activities for running a successful mold manufacturing business is project estimation. The estimation process is an early project activity which is usually handled by highly skilled, in-house experts. One of the most important parameters affecting the estimation process is the volume of manufacturing hours (VMH) to produce the mold. This article suggests how to address the problem of estimating the volume of manufacturing hours by using the support of an artificial neural network (ANN) model, and its inclusion into the expert driven project estimation process. Based on the histogram of ANN estimations the percentage of unwanted underestimations of the VMH can be estimated as well and decreased by an introduced safety factor. The developed model-based estimation enables an expert to improve project estimation by using easily obtainable input data.},
	issn = {0039-2480},	pages = {3-13},	doi = {10.5545/sv-jme.2012.747},
	url = {https://www.sv-jme.eu/sl/article/neural-network-based-model-for-supporting-the-expert-driven-project-estimation-process-in-mold-manufacturing/}
}

Florjanič, B.,Govekar, E.,Kuzman, K.
2013 June 59. Neural Network-based Model for Supporting the Expert Driven Project Estimation Process in Mold Manufacturing. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 59:1

%A Florjanič, Blaž 
%A Govekar, Edvard 
%A Kuzman, Karl 
%D 2013
%T Neural Network-based Model for Supporting the Expert Driven Project Estimation Process in Mold Manufacturing
%B 2013
%9 mold making; manufacturing; artificial neural networks; estimation process
%! Neural Network-based Model for Supporting the Expert Driven Project Estimation Process in Mold Manufacturing
%K mold making; manufacturing; artificial neural networks; estimation process
%X One of the crucial activities for running a successful mold manufacturing business is project estimation. The estimation process is an early project activity which is usually handled by highly skilled, in-house experts. One of the most important parameters affecting the estimation process is the volume of manufacturing hours (VMH) to produce the mold. This article suggests how to address the problem of estimating the volume of manufacturing hours by using the support of an artificial neural network (ANN) model, and its inclusion into the expert driven project estimation process. Based on the histogram of ANN estimations the percentage of unwanted underestimations of the VMH can be estimated as well and decreased by an introduced safety factor. The developed model-based estimation enables an expert to improve project estimation by using easily obtainable input data.
%U https://www.sv-jme.eu/sl/article/neural-network-based-model-for-supporting-the-expert-driven-project-estimation-process-in-mold-manufacturing/
%0 Journal Article
%R 10.5545/sv-jme.2012.747
%& 3
%P 11
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 59
%N 1
%@ 0039-2480
%8 2018-06-28
%7 2018-06-28

Florjanič, Blaž, Edvard  Govekar, & Karl  Kuzman.
"Neural Network-based Model for Supporting the Expert Driven Project Estimation Process in Mold Manufacturing." Strojniški vestnik - Journal of Mechanical Engineering [Online], 59.1 (2013): 3-13. Web.  20 Dec. 2024

TY  - JOUR
AU  - Florjanič, Blaž 
AU  - Govekar, Edvard 
AU  - Kuzman, Karl 
PY  - 2013
TI  - Neural Network-based Model for Supporting the Expert Driven Project Estimation Process in Mold Manufacturing
JF  - Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2012.747
KW  - mold making; manufacturing; artificial neural networks; estimation process
N2  - One of the crucial activities for running a successful mold manufacturing business is project estimation. The estimation process is an early project activity which is usually handled by highly skilled, in-house experts. One of the most important parameters affecting the estimation process is the volume of manufacturing hours (VMH) to produce the mold. This article suggests how to address the problem of estimating the volume of manufacturing hours by using the support of an artificial neural network (ANN) model, and its inclusion into the expert driven project estimation process. Based on the histogram of ANN estimations the percentage of unwanted underestimations of the VMH can be estimated as well and decreased by an introduced safety factor. The developed model-based estimation enables an expert to improve project estimation by using easily obtainable input data.
UR  - https://www.sv-jme.eu/sl/article/neural-network-based-model-for-supporting-the-expert-driven-project-estimation-process-in-mold-manufacturing/

@article{{sv-jme}{sv-jme.2012.747},
	author = {Florjanič, B., Govekar, E., Kuzman, K.},
	title = {Neural Network-based Model for Supporting the Expert Driven Project Estimation Process in Mold Manufacturing},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {59},
	number = {1},
	year = {2013},
	doi = {10.5545/sv-jme.2012.747},
	url = {https://www.sv-jme.eu/sl/article/neural-network-based-model-for-supporting-the-expert-driven-project-estimation-process-in-mold-manufacturing/}
}

TY  - JOUR
AU  - Florjanič, Blaž 
AU  - Govekar, Edvard 
AU  - Kuzman, Karl 
PY  - 2018/06/28
TI  - Neural Network-based Model for Supporting the Expert Driven Project Estimation Process in Mold Manufacturing
JF  - Strojniški vestnik - Journal of Mechanical Engineering; Vol 59, No 1 (2013): Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2012.747
KW  - mold making, manufacturing, artificial neural networks, estimation process
N2  - One of the crucial activities for running a successful mold manufacturing business is project estimation. The estimation process is an early project activity which is usually handled by highly skilled, in-house experts. One of the most important parameters affecting the estimation process is the volume of manufacturing hours (VMH) to produce the mold. This article suggests how to address the problem of estimating the volume of manufacturing hours by using the support of an artificial neural network (ANN) model, and its inclusion into the expert driven project estimation process. Based on the histogram of ANN estimations the percentage of unwanted underestimations of the VMH can be estimated as well and decreased by an introduced safety factor. The developed model-based estimation enables an expert to improve project estimation by using easily obtainable input data.
UR  - https://www.sv-jme.eu/sl/article/neural-network-based-model-for-supporting-the-expert-driven-project-estimation-process-in-mold-manufacturing/

Florjanič, Blaž, Govekar, Edvard, AND Kuzman, Karl.
"Neural Network-based Model for Supporting the Expert Driven Project Estimation Process in Mold Manufacturing" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 59 Number 1 (28 June 2018)