A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Real-Time Early Warning System for Sustainable and Intelligent Plastic Film Manufacturing
In this study, real-time preventive measures were formulated for a crusher process that is impossible to automate, due to the impossibility of installing sensors during the production of plastic films, and a real-time early warning system for semi-automated processes subsequently developed. First, the flow of a typical film process was ascertained. Second, a sustainable plan for real-time forecasting in a process that cannot be automated was developed using the semi-automation method flexible structure production control (FSPC). Third, statistical early selection of the process variables that are most probably responsible for failure was performed during data preprocessing. Then, a new, unified dataset was created using the link reordering method to transform the time sequence of the continuous process into one time zone. Fourth, a sustainable prediction algorithm was developed using the association rule method along with traditional statistical techniques, and verified using actual data. Finally, the overall developed logic was applied to new production process data to verify its prediction accuracy. The developed real-time early warning system for semi-automated processes contributes significantly to the smart manufacturing process both theoretically and practically.
Real-Time Early Warning System for Sustainable and Intelligent Plastic Film Manufacturing
In this study, real-time preventive measures were formulated for a crusher process that is impossible to automate, due to the impossibility of installing sensors during the production of plastic films, and a real-time early warning system for semi-automated processes subsequently developed. First, the flow of a typical film process was ascertained. Second, a sustainable plan for real-time forecasting in a process that cannot be automated was developed using the semi-automation method flexible structure production control (FSPC). Third, statistical early selection of the process variables that are most probably responsible for failure was performed during data preprocessing. Then, a new, unified dataset was created using the link reordering method to transform the time sequence of the continuous process into one time zone. Fourth, a sustainable prediction algorithm was developed using the association rule method along with traditional statistical techniques, and verified using actual data. Finally, the overall developed logic was applied to new production process data to verify its prediction accuracy. The developed real-time early warning system for semi-automated processes contributes significantly to the smart manufacturing process both theoretically and practically.
Real-Time Early Warning System for Sustainable and Intelligent Plastic Film Manufacturing
Jonghyuk Kim (author) / Hyunwoo Hwangbo (author)
2019
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Intelligent variable-resistance real-time monitoring and early warning anchor rod
| European Patent Office | 2023
| European Patent Office | 2020
REAL TIME ANALYSIS FOR EARLY WARNING SYSTEMS
| Springer Verlag | 2006