Digital twin for adaptive process control and optimization with machine learning
A cutting-edge digital twin solution that leverages adaptive machine learning for process control and optimization, enhancing manufacturing efficiency and product quality. It integrates real-time feedback adjustment and performance monitoring to ensure robust process management.
Overview
The Digital Twin for Adaptive Process Control and Optimization harnesses the power of machine learning to enhance manufacturing processes. By combining an exponentially weighted moving average (EWMA) feedback adjustment scheme with adaptive machine learning, this solution continuously refines control parameters based on process performance. This approach ensures effective control and sustained model reliability under varying conditions, resulting in superior product quality and enhanced operational efficiency.
Technical specifications
Key features:
- Adaptive tuning: Machine learning algorithms continuously adjust control parameters for optimal process performance.
- Performance monitoring: Real-time tracking of prediction errors and control stability metrics allows for immediate updates and adjustments.
- Digital twin technology: Provides an accurate simulation of manufacturing processes, enabling what-if scenarios and model validation using both historical and real-time data.
- Multi-step optimization: Algorithms applicable across multiple manufacturing steps, such as mixing and heating, optimize multiple process outputs including energy efficiency metrics.
- Principal Component Analysis (PCA): Enhances optimization by leveraging data from numerous inputs and outputs.
Technology readiness level
This innovative technology is currently at TRL 4, having been validated in a laboratory setting. Further development and integration into manufacturing environments are planned to progress towards full deployment.
About San Jose State University
San José State University is a comprehensive public R2 research university serving a large, diverse community in the heart of Silicon Valley. Industry engagement is built in: the Office of Innovation & Corporate Partnerships is a single front door for companies, and the SJSU Research Foundation supports contracting and compliance for sponsored projects. The College of Engineering’s co‑op program and industry advisory connections link corporate R&D to workforce‑ready talent. New facilities, including the Interdisciplinary Science Building, provide modern labs and collaboration spaces downtown. Research is supported by competitive federal funding from agencies such as NSF, NIH, and NASA.
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