Vehicle Component Dimensioning Using ML Damage Prediction
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Solution Overview
Problem
Current vehicle component design fails to accurately account for stresses and strains induced during operation, leading to suboptimal specifications in terms of size, shape, weight, and fatigue limit, as existing methods lack comprehensive data integration from various vehicle operations and environments.
Innovation Solution
A system that generates operation parameter histograms from collected data, uses machine learning to classify environments and vehicle use, and adjusts three-dimensional component schematics based on predictive damage models, incorporating vehicle dynamics and environmental data to optimize component design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional component design methods are used, then design process is simple, but component specifications are suboptimal in terms of size, shape, weight, and fatigue limit
Solution Approach 1:
The system performs preliminary data collection from actual vehicle operations and environmental conditions before component design begins. Operation parameters are gathered and analyzed in advance to create realistic test scenarios, allowing the component to be designed with accurate stress and strain requirements from the outset rather than using generic design assumptions.
Solution Approach 2:
The system implements continuous feedback loops where component performance data from actual vehicle operations is collected, analyzed, and used to refine subsequent component designs. Test results feed back into the design process, allowing iterative improvement of component specifications based on real-world performance data and identified stress patterns.
2Measurement precision
If comprehensive data integration from various vehicle operations and environments is implemented, then component design accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the complex data integration task into distinct modules: data collection from vehicle operations, environmental data acquisition, machine learning classification of environments and use conditions, and predictive damage modeling. Each module handles a specific aspect of the data flow, making the overall complex system manageable and maintainable while achieving high measurement precision.
Solution Approach 2:
The system introduces machine learning algorithms as intermediaries between raw operational data and component design parameters. The ML models classify environments and vehicle use patterns, transforming complex multi-source data into structured inputs for predictive damage models, thereby bridging the gap between comprehensive data collection and actionable design insights.
3Reliability
If machine learning classification and predictive damage models are used, then component design optimization is achieved, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary classification of operating conditions using machine learning models before running computationally intensive predictive damage simulations. By pre-categorizing environments and vehicle use patterns, the system reduces the complexity and time required for subsequent damage modeling, as simulations can focus on specific classified scenarios rather than analyzing all possible conditions from scratch.
Solution Approach 2:
The system applies predictive damage modeling selectively to the most critical classified scenarios rather than exhaustive analysis of all possible operating conditions. This partial action approach focuses computational resources on the most impactful design optimizations while accepting that not every edge case requires full simulation, thereby reducing overall processing time while maintaining component reliability.
Data Source
AI summary
A computer includes a processor and a memory storing instructions executable by the processor to generate an operation parameter histogram for each of a plurality of vehicle operation parameters, operate a test vehicle according to the operation parameter histograms, input each operation parameter histogram and a plurality of test vehicle environment data to a machine learning program, output from the machine learning program a classification for each operation parameter histogram from the machine learning program, input the classifications to a predictive damage model of a three-dimensional schematic of a vehicle component, and adjust one of a size or a shape of the three-dimensional schematic based the output of the predictive damage model. The operation parameter histograms are based on collected data about operation of a plurality of vehicles. Each operation parameter histogram includes an array of elements. Each element is a number of data points for one of the operation parameters that are within a specified range. Each classification represents a type of environment and a type of test vehicle use.