Digital Twin and IIoT in Optimizing Manufacturing Process and Quality Management
Therefore , in order ensure the product quality in every processing phase or step , it is important to track the product in production ( PiP ) in association with the production equipment .
In this project , the tracking of PiP is done with product digital twin , as sketched in the top-middle part of Fig . 3-6 ( in red color ). From information in the work orders , the system automatically creates product digital twin for each PiP . The initial product digital twin model includes information such as its ID , product type , process specification , etc . As the PiP moves through different stages in its production process , a dynamic association is made with the equipment that is processing the PiP from which key attributes such as start and end processing time and other attributes , e . g ., the position of the PiP with a given piece of equipment , are recorded in the product digital twin . Any relevant actual process data from the equipment is also automatically entered into the product digital twin , and dynamically analyzed in reference to its corresponding process specification at that processing stage . Any deviation from the process specification by the actual process data can be identified , recorded and reported . Similarly , any records of quality measurement and inspection will also be entered into the product digital twin as well . When the processing of a PiP is completed , the data of the full life cycle of the product in the production process is also fully recorded and ready to be queried and analysis in the corresponding product digital twin model .
To consider the product digital twin in a different angle , products are what manufacturers made to create value for its enterprise . As the product in process moving through the production processes , it represents the value flows of the production . By tracking the product in process via product digital twin through the production processes , it in fact is tracking the value flows of the manufacturing enterprise . By further considering the raw material costs , energy cost , defect cost , the OEE of the equipment , labor cost , and the market or order prices of the final products , etc ., it is possible to compute and visualize in near real time the value that are being created throughout the production environment . This will help the manufacturer to better manage the production in identify and reduce or eliminate cost and maximize value creation by making informed decisions in what to make and how to make them .
4 . CONCLUSION AND LEARNINGS
By leveraging the built-in capability of Yo-i Thingswise iDOS platform , the complex project was completed from design to delivery and acceptance in about 3-4 months , less than half of the time it might need should the platform were not used . The production process and product quality management system were well received by the customer during the initial phase of trials and garnered positive praises from them . The same customer has extended this project into a different processing plant as of the writing of this article .
There are a few points of learning from this project :
- 78 - March 2021