Digital Twin and IIoT in Optimizing Manufacturing Process and Quality Management
the same domain but in different upstream and downstream processes , have not achieved the level of information integration and exchange that are needed ( e . g ., product quality management requires information about equipment operation status , and quality management systems in adjacent upstream and downstream are required to exchange information ), so it is very difficult to support cross-process and cross-domain holistic and dynamic data- and analytics-driven operation management .
2.3 Specific Challenges in Steelmaking Process Control and Quality Management
The challenges and potential opportunity for improvements in the smaller scope of the two closely linked application domains : production process and product quality management , are similar .
• It is a common practice that the existing process management applications are mainly responsible for information management of product specification , production process and quality design . Too often these systems are not connected to the production management systems , and therefore the transmission of detailed process control specification for specific products still relies on conventional electronic documents exchange to reach the on-site production management systems and thus requires manual configuration of the processes .
• Additionally , most of the on-site operation process data and quality data cannot be automatically collected but rather to rely on manual reports to track , trace , and archive . As result , it is difficult to discover quality issues soon enough so that they can be addressed in time for corrective actions to advert or minimize the impact . Therefore , problem root cause analysis and solving if any are often after the fact .
• Quality analysis is still a manual exercise on static historical data using conventional SPClike toolsets that are not connected to the production environment .
• Production process control specification and on-site operation data and quality outcome are not easily correlated to provide guidance on product and process control design .
• Product full lifecycle quality traceability is hard to achieve .
• Moreover , the process management put too much manual operational and recording workload on workers that are not efficient and prone to error .
The main quality attributes of steel products include structure performance determined by micro-structure of the material , surface quality and geometric dimensions , which mainly depend on the composition of the material and the processing technologies involving chemical reactions , thermal dynamic and mechanical processing during the production processes . Therefore , it is necessary to obtain data and information related to the three main categories of quality attributes and process control design with respect to the customer ’ s requirements .
The quality pass rate of steel products , especially high-end special steel products , is often low . The main cause for this is the relatively weakness in process control that leads to large fluctuations in the processes that inadvertently affect the quality of the final products . There are
- 64 - March 2021