Model Control Scheme Switching for Industrial Parameter Optimization
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Solution Overview
Problem
Industrial automation environments face challenges in leveraging operational data for real-time insights due to the vast amount of data generated and the complexity of manually editing control programs to optimize industrial processes.
Innovation Solution
Integration of machine learning models within industrial control code to optimize parameters such as performance, yield, and energy conservation, allowing for autonomous adjustments and improvements in industrial processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are integrated into industrial control code to optimize parameters, then productivity and manufacturing precision are improved, but device complexity increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components between operational data and control decisions. These models process complex data patterns and translate them into actionable control parameters, enabling optimization without directly complicating the core control system architecture.
Solution Approach 2:
The control system is segmented into modular components: data collection modules, machine learning model modules, and execution modules. Each model control scheme is an independent, interchangeable unit that can be selected and deployed without redesigning the entire control system, managing complexity through modularity.
2Adaptability or versatility
If multiple model control schemes are maintained for different parameters, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system employs a universal model management architecture that can handle multiple different model control schemes through a common interface and selection mechanism. This universal framework allows the system to adapt to different optimization parameters (performance, yield, energy conservation) without requiring separate management systems for each.
Solution Approach 2:
The system dynamically selects and switches between different model control schemes based on current operational conditions and optimization goals. The model management component can change which model is active in real-time, providing adaptability while keeping the actual number of simultaneously active models limited.
3Manufacturing precision
If manual editing of control programs is performed to optimize processes, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
Machine learning models are pre-trained on historical operational data to learn optimal control strategies for various scenarios. This preliminary training phase captures expert knowledge and optimization patterns, so that during runtime, the system can directly apply these pre-learned optimizations without requiring manual analysis and editing of control programs.
Solution Approach 2:
The system uses machine learning models to automatically generate and adjust control parameters based on operational data, eliminating the need for manual intervention. The models self-service the optimization function by continuously learning from data and autonomously adjusting control strategies to maintain manufacturing precision.
Data Source
AI summary
Various embodiments of the present technology generally relate to solutions for integrating machine learning models into industrial automation environments. More specifically, embodiments of the present technology include systems and methods for implementing machine learning models within industrial control code to improve performance, increase productivity, and add capability to existing control programs. In an embodiment, a system comprises: a storage component configured to maintain a set of model control schemes for controlling an industrial process, a control component configured to control the industrial process with a control program running a model control scheme, wherein the model control scheme is configured to optimize a first parameter of the industrial process, and a model management component configured to change the model control scheme to optimize a second parameter of the industrial process that is distinct from the first parameter.