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A Dynamic System to Predict an Assembly Line Worker's Comfortable Work-Duration Time by Using the Machine Learning Technique
Kulkarni M.S.,
Published in Elsevier B.V.
Volume: 106
Pages: 270 - 275
Worker' work-rotation between the workstations of an assembly line is a common task to an assembly line manager, to manage the workload between the assembly line workers. Usually, worker work rotation makes an interruption to a continuous production system. It is a desire of an assembly line manager to avoid the worker's work-rotation, at least between the work breaks. This work aims to protect the manager's interest by predicting the comfortable work-duration time of an assembly line worker for a given work, based on the working condition and the instantaneous physical & mental status of the worker. The comfortable work-duration time can be used during the worker's work assignment to reduce interruptions. Factors, which influence the longevity of workers' comfortable work duration time are identified. IIoT based sensors are proposed here to monitor the relevant factors for diagnostics. Machine Learning techniques are used as a part of prognostics to predict the comfortable work-duration time of a worker, based on his/her physical & mental status. Implementation methodology is explained along with a simulated experiment. © 2022 Elsevier B.V.. All rights reserved.
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Published in Elsevier B.V.
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