Composite Ply Stack-Up Modeling with Convolution Smoothing
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Solution Overview
Problem
The existing methods for modeling composite aircraft parts fail to accurately represent the smoothed resin ramps between plies, leading to inefficiencies in material property determination, fabrication, and weight reduction, particularly in complex structures like fuselages with tens of thousands of edges.
Innovation Solution
A convolution process is applied to a discrete representation of the ply stack-up to approximate smoothed ramps between different plies, using a B-spline kernel to model the resin flow and integrate it with the surface model, ensuring accurate representation of the cured composite part geometry.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional discrete representation methods are used to model ply stack-up geometry, then the model creation process is simple, but the accuracy of resin ramp representation deteriorates
Solution Approach 1:
A convolution kernel acts as an intermediary between the discrete ply stack-up representation and the continuous resin ramp geometry. The kernel smooths the discrete edges to generate realistic resin ramp transitions, mediating between the simplified input model and the accurate physical geometry representation.
Solution Approach 2:
The modeling approach transforms the geometric parameters of the ply stack-up by applying convolution operations. This changes the sharp discrete edges into smooth continuous transitions, accurately representing the resin flow and ramp formation during composite curing while maintaining computational efficiency.
2Manufacturing precision
If sharp edges are modeled in ply stack-up, then the pre-cure geometry is accurately represented, but the cured geometry with resin ramps becomes inaccurate
Solution Approach 1:
The convolution smoothing operation is performed in advance on the discrete ply representation to predict the final cured geometry with resin ramps. This preliminary action incorporates resin flow behavior into the model before manufacturing, capturing the transformation from pre-cure sharp edges to cured smooth transitions.
Solution Approach 2:
The convolution process creates a smoothed copy of the discrete ply stack-up geometry that represents the cured state. This copied model includes the resin ramp information that would otherwise be lost, providing an accurate representation of the final part geometry without requiring complex fluid dynamics simulations.
3Measurement precision
If complex resin flow simulations are performed, then the cured geometry accuracy improves, but the computational time and resources increase significantly
Solution Approach 1:
Instead of using computationally expensive and complex resin flow simulation models, the invention employs a simple convolution kernel operation that is computationally inexpensive. This disposable approximation method provides sufficient accuracy for manufacturing applications without the time and resource burden of detailed fluid dynamics simulations.
Solution Approach 2:
The complex mechanical/resin flow simulation system is replaced with a mathematical convolution operation. This substitution maintains the essential physics of resin flow and ramp formation while dramatically reducing computational complexity, making the modeling process efficient and practical for industrial applications.
Data Source
AI summary
A composite part including a ply stack-up and resin is modeled. The modeling includes performing a convolution on a representation of the stack-up to approximate smoothed ramps between different plies of the stack-up.