CFRTP Induction Welding Surrogate Models for Faster Process Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The thermoplastic composite induction welding process for carbon fiber reinforced thermoplastics (CFRTP) is computationally expensive and resource-intensive due to the need for high-fidelity simulations and extensive experiments, making process optimization challenging and inefficient.
Innovation Solution
A multi-source machine learning modeling framework is employed to optimize process parameters by selecting a suitable surrogate model, incorporating uncertainty quantification, and fusing heterogeneous data sources, including multi-physics models and experimental data, to reduce data requirements and enhance process efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-fidelity simulations and extensive experiments are used for CFRTP induction welding process optimization, then manufacturing precision and reliability are improved, but computational cost and resource consumption increase significantly
Solution Approach 1:
The patent creates surrogate models that copy the behavior of complex multi-physics simulations, allowing rapid prediction of welding outcomes without running computationally expensive high-fidelity simulations. These surrogate models are trained on limited high-fidelity data and then used for rapid process optimization, reducing computational cost while maintaining prediction accuracy.
Solution Approach 2:
The patent introduces machine learning surrogate models as intermediaries between high-fidelity simulations and process optimization. These surrogate models act as mediators that translate complex simulation data into rapid predictions, enabling efficient optimization without directly running expensive simulations for each optimization iteration.
2Measurement precision
If high-fidelity simulations and extensive experiments are conducted for CFRTP induction welding, then process parameter accuracy is improved, but time consumption and resource requirements increase
Solution Approach 1:
The patent performs preliminary high-fidelity simulations and experiments to train surrogate models, then uses these trained models for rapid process optimization. This preliminary action captures the essential physics and material behavior, allowing subsequent optimization to proceed quickly without repeated high-fidelity simulations.
Solution Approach 2:
The surrogate models create simplified copies of the complex physical processes, enabling rapid prediction of welding outcomes without time-consuming high-fidelity simulations. These copies maintain accuracy for optimization purposes while reducing computational time significantly.
3Reliability
If multiple data sources and machine learning frameworks are integrated, then model reliability and uncertainty quantification are improved, but system complexity increases
Solution Approach 1:
The patent merges multiple data sources (simulations, experiments, literature) and multiple machine learning frameworks into a unified multi-source learning system. This integration allows the system to leverage diverse data and algorithms while providing comprehensive uncertainty quantification, improving prediction reliability despite the inherent complexity.
Solution Approach 2:
The patent develops a universal multi-source machine learning framework that can handle multiple data types, multiple physics models, and multiple optimization objectives simultaneously. This multi-functional system provides both accurate predictions and uncertainty quantification through a single integrated platform.
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 accelerates and enhances the CFRTP welding process by providing fast and accurate surrogate models for process-property mapping, achieving up to 2x faster process design optimization and 25% energy savings while ensuring reliable temperature control and recrystallization in welded parts.
Implementation Method 1
CFRTP composite induction welding
Implementation Method 2
induction heating processes for CFRTP
Implementation Method 3
thermal models
Implementation Method 4
minimum time above a melting temperature
Implementation Method 5
cooling rate after welding to allow for recrystallization
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A system having a set of instructions executable by the system for multi-source machine learning modeling framework for process property mapping of thermoplastic composite manufacturing, the set of instructions comprising: an instruction to select a surrogate machine learning model from a suite of machine learning networks; an instruction to involve uncertainty quantification associated with predictions which provide a quantified estimate of how much the machine learning model can be trusted; an instruction to provide multi-physics process model output to the machine learning model; an instruction to provide heterogeneous data sources for use by the machine learning model; an instruction to determine estimates of optimal process parameters employing budget-constrained multi-fidelity process optimization; an instruction for deployment the multi-source machine learning model in the implementation of carbon fiber reinforced thermoplastic polymer induction welding; and an instruction to perform induction welding with an optimized recipe.