Digital Twin and IIoT in Optimizing Manufacturing Process and Quality Management
Fig . 3-7 : Digital twin class ( equipment type ) design GUI .
• Data analytic models are at the heart of this solution . There are various analytic models tracking the process parameters to ensure they each falls within the standard range on the basis of each individual PiPs . Furthermore , because of the complexity of the production processes that involve many uncontrollable process parameters , e . g ., most evidently , the quality and characteristics of raw material , controlled process parameters e . g ., inlet gas flowrate to a heating furnace , and target process parameters , e . g ., the heating and cooling speeds and steady temperatures of a furnace . It is not always possible to rely on single parameter range checking to determine if the process is running optimally or if certain combinations of otherwise normal ranged parameters would be conducive to product defects . The relationships among these parameters are often nonlinear , strongly coupled and some with direct or indirect feedbacks among themselves and making them difficult to assess the cause-effect for product defect or other considerations . Therefore , it is increasingly more common to combine first-principled engineering know-how and data science to create hybrid analytic models not only to detect defects and also to detect process conditions that likely to lead to the occurrence of defects . It is crucial to gather as much as possible data from the production environment for modeling , including leaving sufficient data for model validation .
• The applications , built on the digital twin API , implemented as micro-services in the Application Framework layer , support the following business functions that map to the requirements described above : o Quality and process real-time monitoring and alerting o Quality anomaly and process exception handling o Quality performance Lean management o Quality and process optimization analysis o Product quality traceability system
IIC Journal of Innovation - 75 -