A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Estimating printed circuit board assembly times using neural networks
Tekijät: Vainio F, Maier M, Knuutila T, Alhoniemi E, Johnsson M, Nevalainen OS
Kustantaja: TAYLOR & FRANCIS LTD
Julkaisuvuosi: 2010
Journal: International Journal of Production Research
Tietokannassa oleva lehden nimi: INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
Lehden akronyymi: INT J PROD RES
Numero sarjassa: 8
Vuosikerta: 48
Numero: 8
Aloitussivu: 2201
Lopetussivu: 2218
Sivujen määrä: 18
ISSN: 0020-7543
DOI: https://doi.org/10.1080/00207540802572574
Tiivistelmä
Several production planning tasks in the printed circuit board (PCB) assembly industry involve the estimation of the component placement times for different PCB types and placement machines. This kind of task may be, for example, the scheduling of jobs or line balancing for single or multiple jobs. The simplest approach to time estimation is to let the production time be a linear function of the number of components to be placed. To achieve more accurate results, the model should include more parameters (e.g. the number of different component types, the number of different component shapes, the dimensions of the PCBs, etc.). In this study we train multilayer neural networks to approximate the assembly times of two different types of assembly machines based on several parameter combinations. It turns out that conventional learning methods are prone to overfitting when the number of hidden units of the network is large in relation to the number of training cases. To avoid this and complicated training and testing, we use Bayesian regularisation to achieve efficient learning and good accuracy automatically.
Several production planning tasks in the printed circuit board (PCB) assembly industry involve the estimation of the component placement times for different PCB types and placement machines. This kind of task may be, for example, the scheduling of jobs or line balancing for single or multiple jobs. The simplest approach to time estimation is to let the production time be a linear function of the number of components to be placed. To achieve more accurate results, the model should include more parameters (e.g. the number of different component types, the number of different component shapes, the dimensions of the PCBs, etc.). In this study we train multilayer neural networks to approximate the assembly times of two different types of assembly machines based on several parameter combinations. It turns out that conventional learning methods are prone to overfitting when the number of hidden units of the network is large in relation to the number of training cases. To avoid this and complicated training and testing, we use Bayesian regularisation to achieve efficient learning and good accuracy automatically.
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