Abstract
To ensure the stability of product quality and production continuity, quality control is drawing increasing attention from the process industry. However, current methods cannot meet requirements with regard to time series data, high coupling parameters, delayed data acquisition, and ambiguous operation control. A digital twin–driven (DTD) method is proposed for real-time monitoring, evaluation, and optimization of process parameters that are strongly related to product quality. Based on a process simulation model, production status information and quality related data are obtained. Combined with an improved genetic algorithm (GA), a time sequential prediction model of bidirectional gated recurrent unit (bi-GRU) with attention mechanism (AM) is built to flexibly allocate parameter weights, accurately predict product quality, timely evaluate technical process, and rapidly generate optimized control plans. A typical case study and relevant field tests from the process industry are presented to prove the effectiveness of the method. Results indicate that the proposed method clearly outperforms its competitors.
Highlights
Process industries are those that add value to raw materials by mixing, separating, heating, molding or chemical reactions
This paper proposes a digital twin-driven method for online quality control, aiming to control technical process in real time, reduce human intervention, improve product quality and ensure its stability
To further validate the effect of parameter optimization algorithm, the digital twin-driven (DTD) method for online quality control is compared with current control method, which depends on rough assessment and adjustment of key process parameter
Summary
Version of Record: A version of this preprint was published at The International Journal of Advanced Manufacturing Technology on January 5th, 2022.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: The International Journal of Advanced Manufacturing Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.