Drop Test Response Surface for Worst-Case Orientation Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for predicting the worst-case scenario in drop tests for objects are time-consuming and require multiple user personas with specialized knowledge, lacking an automated and efficient way to identify the most critical orientation and damage for drop simulations.
Innovation Solution
A method that pre-configures the setup for drop test simulations, integrating with CAD tools, using a drop test solver and response surface optimization to automatically determine the worst-case orientation and minimize safety factors, reducing the need for specialized knowledge and streamlining the design process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional drop test analysis methods are used with multiple personas and sequential processes, then expertise in physics-based simulation and CAD design is utilized, but the design cycle becomes time-consuming and complex
Solution Approach 1:
The patent merges the roles of designer and analyst into a single integrated system. The designer directly inputs CAD geometry and material properties, and the system automatically performs FEA modeling, drop test simulation, and result interpretation without requiring separate analyst intervention. This consolidation eliminates the sequential handoff process and reduces design cycle time while maintaining analysis accuracy.
Solution Approach 2:
The system performs preliminary configuration of the FEA model and drop test setup automatically based on the CAD geometry. Material properties, mesh generation, boundary conditions, and test parameters are pre-configured by the system before the designer runs the simulation. This preliminary automation eliminates the need for manual setup by experts and accelerates the analysis process.
2Measurement precision
If comprehensive drop test configurations are evaluated to predict worst-case scenarios, then accuracy of failure prediction is improved, but the complexity of setup and interpretation increases
Solution Approach 1:
The system performs self-service by automatically evaluating multiple drop test configurations and orientations without requiring expert intervention. The designer simply provides the CAD model, and the system autonomously generates FEA models, runs simulations for various orientations, identifies the worst-case scenario, and presents results. This automation reduces setup complexity while maintaining comprehensive evaluation accuracy.
Solution Approach 2:
The system automatically varies key parameters such as drop orientation, impact location, and velocity based on the CAD geometry. It systematically changes these parameters to evaluate different drop scenarios and identify the worst-case configuration. This automated parameter exploration enables comprehensive accuracy without increasing user-facing complexity.
3Reliability
If multiple personas with specialized expertise are involved in the design and analysis process, then the quality of design validation is improved, but the process becomes difficult to operate for non-experts
Solution Approach 1:
The system acts as an intermediary that translates between CAD geometry and FEA simulation requirements. The designer works with familiar CAD tools and terminology, while the system automatically handles the translation to FEA models, simulation setup, and result interpretation. This intermediary layer shields the designer from complex physics-based simulation details while maintaining validation quality.
Solution Approach 2:
The system replaces the mechanical process of manual FEA modeling and simulation setup with an automated computational process. Instead of requiring designers to manually create FEA models, define material properties, and configure drop tests, the system performs these tasks automatically through algorithms that process the CAD geometry and generate simulation inputs. This substitution maintains expert-level validation quality while dramatically improving ease of operation.
Data Source
AI summary
A method, apparatus, and system provide the ability to perform a drop test using a response surface. Inputs including a target safety factor (T-SF), a drop height, and a computer model, are acquired. An initial template is evaluated by computing a safety factor for a set of orientations, for each model point MP in the computer model across a set of times (t). A minimum safety factor is determined and a response surface for the model is generated. An actual safety factor SFm is generated by conducting a drop test simulation of the model based on a point having the minimum safety factor and a corresponding orientation. Consistency/validity of the actual safety factor is compared to the target safety factor and the model either passes or additional points may be added to the set of points and the process repeats based on an updated response surface.