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Assembly Machines

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Olli Nevalainen – One of the best experts on this subject based on the ideXlab platform.

  • Estimating PCB Assembly Times Using Neural Networks
    International Journal of Production Research, 2010
    Co-Authors: Frans Vainio, Michael Maier, Timo Knuutila, Esa Alhoniemi, Mika Johnsson, Olli Nevalainen

    Abstract:

    Several production planning tasks in 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 is, for example, scheduling of jobs or line balancing for single or multiple jobs. The simplest approach to time estimation is to let the production time to be a linear function of the number of the 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 (MLPs) to approximate 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 he 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.

  • Estimating printed circuit board Assembly times using neural networks
    International Journal of Production Research, 2009
    Co-Authors: Frans Vainio, Michael Maier, Timo Knuutila, Esa Alhoniemi, Mika Johnsson, Olli Nevalainen

    Abstract:

    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 t…

Frans Vainio – One of the best experts on this subject based on the ideXlab platform.

  • Estimating PCB Assembly Times Using Neural Networks
    International Journal of Production Research, 2010
    Co-Authors: Frans Vainio, Michael Maier, Timo Knuutila, Esa Alhoniemi, Mika Johnsson, Olli Nevalainen

    Abstract:

    Several production planning tasks in 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 is, for example, scheduling of jobs or line balancing for single or multiple jobs. The simplest approach to time estimation is to let the production time to be a linear function of the number of the 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 (MLPs) to approximate 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 he 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.

  • Estimating printed circuit board Assembly times using neural networks
    International Journal of Production Research, 2009
    Co-Authors: Frans Vainio, Michael Maier, Timo Knuutila, Esa Alhoniemi, Mika Johnsson, Olli Nevalainen

    Abstract:

    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 t…

Mika Johnsson – One of the best experts on this subject based on the ideXlab platform.

  • Estimating PCB Assembly Times Using Neural Networks
    International Journal of Production Research, 2010
    Co-Authors: Frans Vainio, Michael Maier, Timo Knuutila, Esa Alhoniemi, Mika Johnsson, Olli Nevalainen

    Abstract:

    Several production planning tasks in 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 is, for example, scheduling of jobs or line balancing for single or multiple jobs. The simplest approach to time estimation is to let the production time to be a linear function of the number of the 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 (MLPs) to approximate 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 he 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.

  • Estimating printed circuit board Assembly times using neural networks
    International Journal of Production Research, 2009
    Co-Authors: Frans Vainio, Michael Maier, Timo Knuutila, Esa Alhoniemi, Mika Johnsson, Olli Nevalainen

    Abstract:

    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 t…