Following the success of IMAGINE, the consortium aims to extend the integrated decisions from the real-time configuration to the predictive strategy. For intelligent manufacturing processes and systems, dynamic dispatching/scheduling and process/equipment control should be managed in a prognostic way. Production plans are dynamically optimized and used as the foundation for the predictive regulations of the process and equipment. As the production and engineering data are accumulated continuously, the predictive control and planning strategies will be empowered by the deep learning techniques that make sense of big data for accurate and precise previsions. Furthermore, the prognostic decisions will be interpreted with reasonable logic and traced back to the controllable factors for not only comprehending the analytic causality but also enhancing the control and planning dynamics. The methods developed in this project will be validated through the cooperation with local partners.
Monsieur Claude YUGMA (Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes)
The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.
LIMOS Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes
National Taiwan University / Graduade Institute of Industrial Engineering
Help of the ANR 213,996 euros
Beginning and duration of the scientific project: - 36 Months