The Authors propose a new analytical model able to evaluate human task duration of repetitive manual task. The model relates tasks duration with number of repetitions in case of both “learning” and “tiredness” phenomena occur. In order to have a good model parameters estimation with a reduced amount of field data, an Artificial Neural Network (ANN) is adopted. The model also leads to the Worker Skill Factor (WSF) definition, a new parameter able to couple task time features and human attitudes in a given work environment. The model framework reveals suitable for explaining actual working situations characterized by differently skilled workers engaged in performing manual tasks. The overall model has been applied to a case study concerning an assembly line of high pressure diesel injection systems for the automotive industry. Data collected from a set of work stations of the assembly line are adopted to train a three layers standard back propagation ANN. The trained ANN is used to predict model shape parameters of other assembly stations. Results obtained by the model fit well experimental data and reveal the model capability in capturing actual human performance of workers carrying out repetitive manual tasks.

Learning and “Tiredness” Phenomena in Manual Operation Performed in Lean Automated Manufacturing Systems: a Reference Model

DIGIESI, Salvatore;MOSSA, Giorgio;MUMMOLO, Giovanni
2004-01-01

Abstract

The Authors propose a new analytical model able to evaluate human task duration of repetitive manual task. The model relates tasks duration with number of repetitions in case of both “learning” and “tiredness” phenomena occur. In order to have a good model parameters estimation with a reduced amount of field data, an Artificial Neural Network (ANN) is adopted. The model also leads to the Worker Skill Factor (WSF) definition, a new parameter able to couple task time features and human attitudes in a given work environment. The model framework reveals suitable for explaining actual working situations characterized by differently skilled workers engaged in performing manual tasks. The overall model has been applied to a case study concerning an assembly line of high pressure diesel injection systems for the automotive industry. Data collected from a set of work stations of the assembly line are adopted to train a three layers standard back propagation ANN. The trained ANN is used to predict model shape parameters of other assembly stations. Results obtained by the model fit well experimental data and reveal the model capability in capturing actual human performance of workers carrying out repetitive manual tasks.
International IMS (Intelligent Manufacturing Systems) Forum 2004
88-901168-9-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/17046
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