Hitachi And Daicel Develop Image Analysis System To Detect Signs Of Facilities Failures And Deviations In Front-Line Worker Activities
14 July 2016 | Automation, Electronics, Popular
Hitachi’s project research with Daicel Corporation, has resulted in final development of an image analysis system that supports quality improvements and increased productivity by detecting signs of operational failures in production line facilities and deviations in worker activities on the front lines of manufacturing.
For 16 months starting in February 2015, Hitachi and Daicel have been conducting joint verification tests aimed at the practical application of the image analysis system at Daicel’s Harima Plant (Tatsuno City, Hyogo Prefecture), which manufactures core components for airbags.
Furthermore, by shifting the role of on-site management supervisors from a focus on “after the fact” measures to the monitoring of trends and preventative measures using obtained image data, they will tie these activities into the prevention of failures before they occur.
In recent years, mega-recalls in various industries have brought about a renewed awareness of the importance of accumulating and managing manufacturing performance data to identify the causes of product defects, and to implement countermeasures.
In the future, starting at the Harima Plant, Hitachi and Daicel plan to install this image analysis system at six overseas plants, and aim to construct a globally integrated management system by aggregating and analyzing information via cloud service. Hitachi will make this image analysis system available to the manufacturing industry worldwide as generalized digital solutions by applying ideas and technologies of the IoT platform ‘Lumada’.
In the advanced manufacturing workplaces of the future, it will be necessary to gather a wide range of work related performance data, including manufacturing performance and inspection data and the results of visual checks by workers, achieve new traceability by establishing mutual links among these different forms of product performance data. All of this will be achieved by introducing new manufacturing execution systems that incorporate IoT technologies.