Control Loop Template Prediction for Automated Engineering Design
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Solution Overview
Problem
Current engineering design processes for industrial control and design applications are time-consuming and resource-intensive, relying heavily on human skill and lacking scalability, as they require manual effort to understand design inputs and reuse existing artifacts, and conventional methods are limited by a rules-driven approach that fails to efficiently address new project requirements.
Innovation Solution
A knowledge-driven artificial intelligence engine that uses machine learning to automate engineering design by training a knowledge base with pairings of control loop data and templates, allowing for the prediction and instantiation of new control loops, and incorporating feedback for continuous improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a manually driven design process is used, then engineering skill and expertise can be applied to ensure design quality, but the process consumes significant time and resources and lacks scalability
Solution Approach 1:
The patent replaces the manual mechanical design process with an automated AI-based system. The machine learning model learns from historical design data and automatically generates design configurations, substituting human engineers' manual work with an automated computational system that maintains design quality while dramatically increasing productivity
Solution Approach 2:
The system performs preliminary learning from historical design data before actual design tasks. The machine learning model is trained on past designs, lessons learned, and best practices, enabling it to automatically apply this pre-acquired knowledge to new design problems, thereby maintaining quality without manual intervention
2Extent of automation
If a rules driven approach is applied, then some automation can be achieved, but constraints limit use and scalability, and significant time resources are still required to design rules for every new project
Solution Approach 1:
The patent transitions from static rules to a dynamic machine learning model. The system adapts its behavior based on learned patterns from historical data, allowing it to handle diverse project requirements flexibly. The model can be continuously retrained with new data, making the automation both adaptable and versatile across different project types
Solution Approach 2:
The system changes from fixed rule parameters to learned parameters from training data. The machine learning model identifies optimal design parameters and configurations based on patterns in historical data, allowing automatic adaptation to different project constraints and requirements without manual rule redesign
3Reliability
If engineers manually review and determine design solutions, then design quality and expertise application are maintained, but the process requires significant time and cannot efficiently address new project requirements
Solution Approach 1:
The patent replaces manual engineering review processes with automated machine learning-based design generation. The system learns from historical designs and automatically produces design configurations, eliminating the time-consuming manual review process while maintaining design accuracy through the model's learned patterns and expertise
Data Source
AI summary
A method of automating engineering design is provided. The method includes receiving a training set including pairings of control loop data for respective control loops identified in digitized design data and templates that were instantiated using the control loop data of the respective control loops and training, using machine learning, a knowledge base, based on the training set. The knowledge base, once trained, is configured to be queried with digitized new control loop data, predict a template to pair with the digitized new control loop data, and the predicted template, and the predicted template is configured to be instantiated with the new control loop data for implementation of a control loop in an engineering system.