Contextual Hybrid Digital Twin for Real-Time Process Gap Learning
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Solution Overview
Problem
Conventional manufacturing systems face challenges in real-time data processing and creating accurate models due to variations in production systems, making it difficult to monitor and optimize processes effectively, especially with factors like machine state, operator training, and environmental conditions affecting the accuracy of generic models.
Innovation Solution
A processor-implemented method and system that utilize a contextual hybrid digital twin, combining physics-based and machine learning models to learn behavioral patterns, minimize gaps between simulated and real-time data, and create a robust digital twin for real-time monitoring and control of manufacturing processes using soft sensing devices and performance analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional generic models are used for manufacturing process monitoring, then model development is simpler, but accuracy deteriorates due to variations in machine state, operator training, and environmental factors
Solution Approach 1:
The patent implements dynamic model adaptation by continuously updating the digital twin model with real-time data from the manufacturing process. The system transitions from static generic models to dynamic models that adapt to changing conditions including machine state variations, operator training levels, and environmental factors. This is achieved through continuous data collection and model retraining mechanisms that keep the model accurate without requiring complete model redesign.
Solution Approach 2:
The system changes model parameters dynamically based on observed process conditions. Instead of using fixed parameters in generic models, the patent adjusts model parameters in real-time based on actual manufacturing data, sensor readings, and process variations. This allows the model to maintain high accuracy across different operating conditions without increasing structural complexity.
2Manufacturing precision
If real-time data processing is implemented for process optimization, then manufacturing quality improves, but data processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and filtering data in real-time as it is collected from sensors and manufacturing equipment. The system performs initial data cleaning, normalization, and feature extraction immediately upon data generation, preparing it for subsequent analysis. This preliminary processing reduces the computational burden on later stages and enables faster overall processing while maintaining optimization accuracy.
Solution Approach 2:
The data processing system is segmented into multiple independent modules that process different aspects of manufacturing data in parallel. The patent divides the processing pipeline into data collection, preprocessing, model inference, and control action stages, each handled by specialized components. This segmentation allows concurrent processing of multiple data streams and reduces bottlenecks, decreasing total processing time while maintaining comprehensive process optimization.
3Reliability
If digital twin models are created for each specific production line, then model accuracy improves, but system complexity and development effort increase
Solution Approach 1:
The patent implements a hierarchical digital twin architecture where a generic base model provides universal functionality across all production lines, and specific customization is achieved through parameter adjustment rather than complete model recreation. The system allows a single digital twin framework to serve multiple production lines by adapting to different machine states, operators, and environmental conditions through data-driven parameter tuning, reducing development effort while maintaining reliability.
Solution Approach 2:
The system employs a nested digital twin structure where specific production line models are nested within a broader generic model framework. The patent creates a hierarchy where general manufacturing processes are modeled at the parent level, and specific line variations are modeled as child instances that inherit from and specialize the parent model. This nesting reduces complexity by avoiding complete separate model development for each line while maintaining the reliability needed for specific applications.
Data Source
AI summary
This disclosure relates generally to systems and methods for monitoring and controlling a manufacturing process using contextual hybrid digital twin. Data pertaining to the manufacturing process is obtained from a plurality of data generation sources which is further inputted to one or more physics based models and train machine learning models such that simulated data and real time tata is obtained. Further, a gap between the simulated data and the real time data is determined and learnt. The learnt gap is further minimized and an augmented set of models are obtained. The augmented set of models along with a set of soft-sensing data is used to create the contextual hybrid digital twin for the manufacturing process. The performance of the manufacturing process is monitored and controlled using a performance analytics and decision making enablers of the contextual hybrid digital twin respectively in real time.