Log-linear learning model for predicting a steady-state manual assembly time
Articles
Vytautas Kleiza
Kaunas University of Technology, Lithuania
Justinas Tilindis
Kaunas University of Technology, Lithuania
Published 2014-07-07
https://doi.org/10.15388/NA.2014.4.5
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Keywords

learning curve
data fitting
parameter estimating
mathematical modeling
manual assembly process

How to Cite

Kleiza V. and Tilindis J. (2014) “Log-linear learning model for predicting a steady-state manual assembly time”, Nonlinear Analysis: Modelling and Control, 19(4), pp. 592-601. doi: 10.15388/NA.2014.4.5.

Abstract

This paper presents the method for estimating the parameters of a two parameter learning curve (LC). Different values of parameters and different sample sizes are used for this estimation. Based on the experimental data an adequate mathematically grounded LC model is proposed for a manual assembly process of automotive wiring harness. The model enables us to determine the LC parameters αε (slope coefficient) and the learning rate stabilization point xc, i.e. to completely restore LC and predict the production process. The propositions that ground the model application correctness are proved. The model adequacy is estimated, based on concrete production process monitoring data. The criterion that determines production process without stabilized learning rate is proposed.

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