Machine-Learning Cushioning Data Acquisition for Shock Response Design
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Solution Overview
Problem
Existing technologies face challenges in designing diverse cushioning materials due to the difficulty in correlating shock response data with stress-strain curves, making it hard to optimize cushioning materials for various protection targets and usage scenarios.
Innovation Solution
A data acquisition method and device that utilize machine learning models to associate shock response spectra with acceleration waveforms and stress-strain curves, enabling the generation of objective data for designing cushioning materials that effectively protect objects from shock.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to correlate shock response data with stress-strain curves, then the design process becomes complex and time-consuming, but the patent enables simple and reliable design through machine learning association
Solution Approach 1:
The patent introduces machine learning models as intermediary components that automatically correlate shock response spectra with acceleration waveforms and stress-strain curves. These ML models serve as mediators between different data types, eliminating the need for complex manual correlation processes and enabling automated cushioning material design.
Solution Approach 2:
The patent replaces traditional mechanical and manual methods of data correlation with machine learning-based computational approaches. Instead of using conventional experimental methods and manual analysis to correlate shock response data with stress-strain curves, the system uses trained ML models to perform these correlations automatically, significantly improving design efficiency.
2Adaptability or versatility
If diverse cushioning materials are designed for various protection targets, then the adaptability increases, but the difficulty of correlating shock response data with stress-strain curves increases
Solution Approach 1:
The patent creates universal machine learning models that can handle multiple types of cushioning materials and protection targets through a single unified approach. The ML models are trained to generalize across different material types, geometries, and shock scenarios, enabling diverse cushioning material design without requiring separate correlation methods for each case.
Solution Approach 2:
The patent utilizes machine learning models that can adapt to different parameters and conditions by changing their input data characteristics. The ML models process various types of shock response data and stress-strain curve data with different parameters, automatically adjusting to handle diverse cushioning material designs through parameter-based generalization rather than requiring separate correlation procedures.
Data Source
AI summary
The data acquisition method includes a first step for acquiring input data including at least one of shock response data that relates to a shock response spectrum and stress-strain data that relates to a stress-strain curve, and a second step for executing at least one of (i) a first acquisition step for acquiring, using first association data in which an acceleration waveform representing shock acceleration of an object that is protected by a cushioning material and the shock response spectrum of the object are associated, first objective data representing the acceleration waveform corresponding to the acquired shock response data and (ii) a second acquisition step for acquiring, using second association data in which shape data of the cushioning material and the stress-strain curve of the cushioning material are associated, second objective data representing the shape data corresponding to the input stress-strain data.