Multiscale Modeling for Composite Residual Stress Prediction
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Solution Overview
Problem
Current industrial design processes for composite materials, particularly in aerospace and automotive industries, fail to explicitly account for the dependency of material properties on curing processes, leading to inefficiencies in product development due to experimental optimization of manufacturing parameters and high costs associated with frequent prototype modifications.
Innovation Solution
A processor-implemented method and system using multiscale modeling techniques for determining deformation profiles and residual stresses in molded composite parts, which involves creating a geometry model, performing thermo-chemical and thermo-mechanical analysis to optimize manufacturing process parameters and material properties, thereby reducing the need for extensive experimental testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If experimental optimization of manufacturing parameters is used, then material properties can be improved, but product development time and costs increase
Solution Approach 1:
The patent creates a virtual copy of the manufacturing process through computational modeling. The multiscale model replicates the curing process, thermal gradients, and material behavior in silico, allowing prediction of material properties without physical experimentation. This virtual copying enables optimization of manufacturing parameters while avoiding repeated prototyping and testing cycles.
Solution Approach 2:
The patent performs preliminary computational analysis before actual manufacturing. By using the multiscale model to predict curing profiles, thermal stresses, and final material properties in advance, the system allows optimization of manufacturing parameters during the design phase, preventing the need for time-consuming experimental iterations later.
2Reliability
If experimental optimization of manufacturing parameters is used, then material properties can be improved, but costs increase
Solution Approach 1:
The computational model serves as a virtual substitute for expensive physical experiments. By simulating the curing process and predicting material properties through multiscale modeling, the system eliminates the need for multiple costly prototypes and material tests, significantly reducing development costs while maintaining accuracy in material property prediction.
Solution Approach 2:
The patent replaces the physical experimental system with a computational simulation system. Instead of conducting actual manufacturing experiments that consume materials and money, the system uses numerical methods and multiscale modeling to predict outcomes, substituting computational resources for physical resources and reducing overall costs.
3Device complexity
If curing process dependency is not included in design process, then design process remains simple, but manufacturing precision deteriorates
Solution Approach 1:
The patent incorporates curing process parameters (temperature, time, pressure) as explicit variables in the design model. The multiscale framework tracks how these parameters evolve during curing and their impact on material properties, transforming the design process from a geometric exercise to a physics-based prediction tool that accounts for manufacturing realities.
Solution Approach 2:
The system performs preliminary analysis of curing process effects during the design phase. By predicting thermal gradients, degree of cure distribution, and resulting material properties before manufacturing, the system allows designers to optimize both geometry and processing parameters together, improving manufacturing precision without significantly increasing overall process complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables the optimization of manufacturing process parameters and material properties, reducing product development time and costs by incorporating computational methods to analyze and improve the quality of composite structures, thereby capturing the full potential of composite materials more effectively.
Implementation Method 1
performing, via a multiscale modeling technique executed by the one or more hardware processors, thermo chemical analysis on one or more portions of the at least one molded part to obtain one or more curing profiles at the one or more portions by using the one or more constituent properties, wherein the one or more curing profiles are obtained by incrementally applying one or more temperature profiles
Implementation Method 2
performing, via the multiscale modeling technique executed by the one or more hardware processors, thermo mechanical analysis on the one or more portions of the at least one molded part to determine residual stresses and one or more deformation profiles pertaining to the one or more portions of the at least one molded part based on the one or more curing profiles
Data Source
AI summary
Conventional approaches of physical experiments for the effects of cure kinetics in composites materials may lack in capturing lower length scale effects at bulk level. The computational state of the art approaches has not focused on the issue of scale bridging between multiple length scales for manufacturing effects in composites. This limits its usability for specific materials or situations. Embodiments of the present disclosure provide systems and methods that implement a multiscale analysis for determining residual stress and deformation profiles in molded parts comprising composite material. More specifically, present disclosure implements the multiscale analysis wherein a thermal chemical analysis and thermal mechanical analysis are linked to achieve two-way coupling for curing effects at each node/point of molded parts having composite material to provide flexibility and versatility in terms of exploring multiple material combinations without major modification in the approach.