Industrial Installation Configuration Using Machine-Learned Component Models
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Solution Overview
Problem
Industrial installations face challenges in efficiently designing and configuring components for capturing, handling, and machining objects due to the complexity and variability of processes, which existing methods struggle to address effectively.
Innovation Solution
A method and system utilizing machine-learned component models to predict process success and determine configuration parameters for installation components based on object models, enabling quick, precise, and reliable design and configuration, with modular training and pre-training capabilities for improved feasibility and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional methods are used to design and configure installation components, then the design process is thorough and systematic, but the design time and complexity increase significantly
Solution Approach 1:
The system performs preliminary analysis and configuration determination using machine-learned models before actual installation or detailed design work begins. The predictive models evaluate process success and determine configuration parameters in advance, allowing designers to avoid time-consuming trial-and-error iterations and focus only on optimizing pre-identified promising configurations.
Solution Approach 2:
The patent replaces traditional mechanical design iteration processes with a computational system using machine-learned predictive models. Instead of physically prototyping and testing configurations, the system uses trained models to predict process success and determine optimal configuration parameters computationally, dramatically reducing design time while maintaining precision.
2Adaptability or versatility
If traditional configuration methods are used, then all possible parameters can be manually adjusted, but the process becomes complex and error-prone
Solution Approach 1:
The machine-learned models perform self-service by automatically determining optimal configuration parameters based on input data about the industrial installation and process requirements. The models independently evaluate multiple parameters and their interactions, selecting optimal values without requiring manual adjustment of each parameter, thereby reducing configuration complexity while maintaining flexibility.
Solution Approach 2:
The system uses parameter changes as input to the machine-learned models to predict process success and determine optimal configuration values. By transforming the configuration problem into a predictive modeling task where parameters are systematically varied and evaluated by trained models, the system maintains configuration flexibility while reducing the complexity of manual parameter adjustment.
3Productivity
If feasibility analysis is performed without predictive models, then all design options can be explored, but the analysis becomes time-consuming and resource-intensive
Solution Approach 1:
Feasibility analysis is performed preliminarily using machine-learned predictive models that have been trained on historical data and simulations. The models quickly evaluate whether proposed configurations are likely to succeed before detailed design and implementation proceed, enabling rapid filtering of infeasible options while maintaining reliable feasibility assessment through trained model predictions.
Solution Approach 2:
The patent replaces resource-intensive traditional feasibility analysis methods with computational predictive modeling. Instead of performing exhaustive simulations or physical tests to assess feasibility, the system uses trained machine-learned models to predict process success probabilities, dramatically improving design efficiency while maintaining reliable feasibility determination through model-based predictions.
Data Source
AI summary
A method for analyzing and/or configuring an industrial installation, which has at least one first installation component for capturing, handling and/or machining at least one first object. A process success of the first installation component is predicted and/or a value for a configuration parameter of the first installation component is determined on the basis of at least one first object model of the first object with the aid of at least one first machine-learned component model of the first installation component.