Digital Twin and IIoT in Optimizing Manufacturing Process and Quality Management
many challenges contributing to the weak process control , such as the difficulty in maintaining stability of process control parameters ( e . g ., continuous casting speed and temperature of molten steel ), in detecting interference of uncontrollable factors ( e . g ., the floating of inclusions in molten steel ), and that in practice , due to the lack of means , there are many influential process parameters are not only uncontrolled but often not yet visible ( e . g ., in the rolling process , the temperature of the entire process in between the start and the final rolling point ).
Additionally , many product quality issues are related to equipment status and operation methods . Therefore , in order to obtain quality products , standards need to be formulated and quantified for ingredients , processes , equipment , and operation methods , and equally crucial , there need to be real-time measurement and monitoring the actual process to ensure these standards are met , and any exceptions are detected promptly so they can be corrected in time .
Furthermore , the quality of steel product is affected by many coupling factors . Constrained by practical conditions , it is impossible to control every factor that may affect quality . To improve product quality , the key is then to identify the key factors and place strong emphasis on controlling them . However , the cause-and-effect relationship of various factors are often complex and coupled with each other , thus brings great challenge in identifying what the key factors are without in-depth analytics . On the other hand , the analytics for this and other similar purposes depends on large amount of quality data that contain the correlations between what seem to be randomly occurring defects to process control specifications and actual process data , equipment status and operational methods that may cause the defects .
These obviously cannot be accomplished with the conventional approach of relying heavily on spotted manual inspection and recording , and by counting on the independent and isolated automation systems and application systems that cannot share and align data . A totally new approach supported by a new set of technologies is required to address these challenges to achieve total quality control in the production environment . At this time , the IIoT and digital twin technologies are the right choice for addressing these challenges , as demonstrated by our practice .
3 . THE SOLUTION
This article reports a successful case , using an industrial Internet platform embedded with a digital twin framework to break through the barriers at different architecture levels and among different domain application systems as described above . The final solution system
• carries out collection of process control and quality data , enables near real-time data analytic , establishes a new data- and analytics-driven production process and product quality management application .
• performs online dynamic comparative analysis on actual process data in reference to process design specification , carries out dynamic process quality monitoring and quality
IIC Journal of Innovation - 65 -