Hitachi, Ltd.

Yi-Chu Chang

Heming Chen

Wei Yuan

LiLi Zheng

In example implementations described herein, there are systems and methods enabling manufacturers to become more efficient by providing a digital tool to track, visualize, quantify, and notify production bottlenecks for quick actions. Some implementations include an apparatus including a processor configured to analyze time-stamp data collected associated with a plurality of elements of an industrial process. The processor may be configured to generate, based on the analyzed time-stamp data, a color coded visualization of the industrial process, where generating the color coded visualization includes associating each of the plurality of elements of the industrial process with a corresponding color indicating a state of an element in the plurality of elements of the industrial process. The processor may further be configured to present, via a display, the generated color coded visualization of the industrial process.

Production line analyzer and visualizer for line performance improvement

Example implementations described herein involve systems and methods for operation of a robot configured to work on a first process and a second process, which can involve receiving sensor data indicative of a status of one or more of the first process and the second process; for the status indicative of the first process waiting on the robot, controlling the robot to work on the first process; and for the status indicative of the first process not waiting on the robot, controlling the robot to conduct one or more of work on the second process or return to standby.

Autonomous task management industrial robot

Example implementations described herein are directed to a simulation environment for a real world system involving one or more robots and one or more sensors. Scenarios are loaded into a simulation environment having one or more virtual robots corresponding to the one or more robots, and one or more virtual sensors corresponding to the one or more virtual system to train a control strategy model from reinforcement learning, which is subsequently deployed to the real world environment. In cases of failure of the real world environment, the failures are provided to the simulation environment to generate an updated control strategy model for the real world environment.

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Farmington Hills, MI, United States of America

Yi-Chu Chang