Adaptive Test Asset Assignment Using Scenario Behavior Models
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Solution Overview
Problem
Modern electronics manufacturing and assembly systems face challenges in dynamically assigning tests to test assets due to the need for more efficient and accurate methods for predicting system behavior and key performance indicators (KPIs), leading to potential delays and cost overruns.
Innovation Solution
A method and system for adaptively assigning scenario-based tests to test assets by analyzing component behaviors and scenario characteristics, using AI/ML to predict behavior outcomes and select appropriate test asset types, such as Model-in-the-Loop, Software-in-the-Loop, and Hardware-in-the-Loop, to validate KPIs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional static methods are used for assigning tests to test assets, then implementation is simple, but adaptability to different test scenarios and system behaviors is poor
Solution Approach 1:
The patent implements dynamic test asset type assignment by using behavior models to predict SUT component behaviors under different test scenarios. The system dynamically selects appropriate behavior models based on scenario characteristics and uses AI/ML to adaptively determine the most suitable test asset type, transforming the static assignment process into a dynamic, scenario-responsive system.
Solution Approach 2:
The system changes the parameter of test asset type selection from fixed to variable by introducing behavior model predictions as decision parameters. The assignment is based on predicted behavior outcomes derived from scenario characteristics and component behavior models, allowing the system to adapt parameters (test asset type) based on changing test conditions.
2Measurement precision
If more comprehensive test scenarios are implemented, then validation accuracy improves, but testing time and costs increase
Solution Approach 1:
The system performs preliminary action by using behavior models to predict SUT component behaviors before actual testing. The AI/ML-based prediction mechanism pre-assesses which test asset types are most appropriate for given scenarios, allowing the system to prepare and select optimal testing configurations in advance, thereby reducing actual testing time while maintaining validation accuracy.
Solution Approach 2:
The patent substitutes mechanical trial-and-error testing with AI/ML-based predictive modeling. Instead of physically testing multiple asset types to determine suitability, the system uses virtual behavior models and machine learning algorithms to predict outcomes, replacing time-consuming physical experimentation with computational prediction.
3Measurement precision
If multiple behavior models are executed for each component behavior, then prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The system segments the computational task by dividing it into distinct modules: scenario characteristic extraction, behavior model selection, prediction execution, and result aggregation. Each component behavior has its own dedicated behavior models, and the system processes them independently before combining results, making the complex computational task more manageable and organized.
Solution Approach 2:
The behavior models serve multiple functions: they predict component behaviors under different scenarios, evaluate test asset type suitability, and provide inputs for AI/ML-based decision making. This multi-functionality reduces the need for separate specialized systems, managing computational complexity through versatile model design.
Data Source
AI summary
Techniques for assigning scenario-based tests to test assets are described. In an example, a scenario-based test operable to test a key performance indicator (KPI) of a System Under Test (SUT), a component behavior exhibited by a first component of the SUT, and a scenario characteristic are received. Based on the component behavior and the scenario characteristic, a first plurality of behavior models associated with the component behavior are identified. Based on the scenario characteristic a characteristic value is extracted from the scenario-based test. Each behavior model of the first plurality of behavior models is executed using the characteristic value to generate a first plurality of predicted behavior outcomes. Based on the first plurality of predicted behavior outcomes, a first test asset type from a plurality of test asset types is selected and the scenario-based test is transmitted to a test asset of the first test asset type.