Vehicle Fixture Locator Layout for Cost-Quality Balance
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Solution Overview
Problem
The challenge lies in determining the optimal number and position of locators required to fix a part on a vehicle fixture, as more than three locators increase costs while fewer locators may compromise product quality.
Innovation Solution
A computer system utilizing a trained artificial neural network to determine the target number and distance of locators based on part parameters, optimizing for minimal cost and maximum quality by predicting part deviation and deflection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If more locators are used to fix the part on the fixture, then product quality and stability are improved, but fixture cost increases
Solution Approach 1:
The patent uses artificial neural networks to predict part deviation and deflection based on various parameters including number of locators, locator positions, part geometry, and material properties. By changing these parameters systematically and predicting their impact on quality metrics, the system identifies optimal configurations that achieve required quality with minimum locators, thus resolving the contradiction between quality and cost
Solution Approach 2:
The system performs preliminary prediction of part deviation and deflection using trained neural networks before actual manufacturing. This allows identification of optimal locator configurations in advance, avoiding unnecessary locators and reducing fixture cost while ensuring quality requirements are met through pre-validation of the configuration
2Stability of the object's composition
If more locators are placed closer together, then part stability is improved, but distance between locators decreases and complexity increases
Solution Approach 1:
The patent employs dynamic optimization where the neural network evaluates multiple locator configurations and their impact on part stability. The system adaptively determines optimal locator positions based on part geometry, material properties, and stability requirements, rather than using fixed rigid patterns. This dynamic approach achieves required stability with simpler, more spaced-out locator configurations
3Device complexity
If fewer locators are used to reduce cost, then fixture cost decreases, but product quality may be compromised
Solution Approach 1:
The system uses partial action by determining the minimum necessary number of locators required to achieve quality requirements. Through neural network prediction, it identifies the exact point where adding more locators provides diminishing returns, allowing use of fewer locators than traditional methods while maintaining quality. This avoids excessive action of using more locators than necessary
4Area of stationary object
If locators are placed closer together to improve coverage, then part coverage is improved, but distance between locators decreases and complexity increases
Solution Approach 1:
The patent applies local quality by determining optimal locator positions based on local part characteristics such as geometry, thickness, and material properties at different locations. The neural network evaluates which areas require closer locators and which can have more spaced-out locators, creating non-uniform distributions that achieve adequate coverage without unnecessary complexity in high-coverage areas
Data Source
AI summary
Fixing a part on a vehicle based on determining a number and a position of locators required to fix a part on a fixture of a vehicle is disclosed. In exemplary aspects, a trained artificial neural network is provided for determining a target number of locators and a target distance between adjacent locators placed on a part to fix the part on a fixture.