Efficient work measurement system of manufacturing cells using speech recognition and digital image processing technology

Work measurement methods previously proposed require considerable time and effort by time study analysts because they have to measure the required time through direct observations. In this study, however, we propose a method which efficiently measures the standard times without involving human analysts by using speech recognition and digital image processing techniques. First, we implement a prototype system which can acquire the status of manufacturing cells through a speech recognition system. Second, using image processing, we suggest a method which consists of two main steps: motion representation and cycle segmentation. In the motion representation step, we first detect the motion of any object distinct from its background by differencing two consecutive images separated by a constant time interval. The images thus obtained then pass through an edge detector filter. Finally, the mean values of coordinates of significant pixels of the edge image are obtained. Through these processes, the motions of the observed worker are represented by two time series of data of worker location in horizontal and vertical axes. In the cycle segmentation step, we extract the frames which have maximum or minimum coordinates in one cycle, store them in a stack, and calculate each cycle time using these frames. In this step we also consider methods for detecting work delays due to unexpected events such as an operator’s movement out of the work area, or interruptions. To conclude, the experimental results show that the proposed method is very cost-effective and useful for measuring time standards for various work environments.