CFRTP Induction Welding Optimization With Multi-Source ML Surrogates
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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 experimental data, making process optimization challenging and inefficient.
Innovation Solution
A multi-source machine learning modeling framework is employed to optimize process parameters by fusing heterogeneous data sources, including multi-physics models and experimental data, to provide accurate and efficient surrogate models for process-property mapping and budget-constrained optimization, leveraging uncertainty quantification and suitable machine learning frameworks like Gaussian processes and deep neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-fidelity simulations and extensive experiments are used for CFRTP induction welding, 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. These surrogate models are trained on a subset of high-fidelity simulation data and experimental results, then used to predict welding outcomes without running expensive full simulations. This copying approach maintains manufacturing precision while dramatically reducing computational cost and resource consumption.
Solution Approach 2:
The patent introduces machine learning surrogate models as intermediaries between the complex multi-physics simulations/experiments and the welding process optimization. These surrogate models act as mediators that capture the essential relationships from high-fidelity data and provide fast predictions for optimization, eliminating the need to repeatedly run expensive simulations during the optimization process.
2Measurement precision
If multi-physics models and extensive experiments are conducted over wide range of process parameters, then model accuracy is improved, but time consumption and resource requirements increase
Solution Approach 1:
The patent performs preliminary high-fidelity simulations and experiments to generate training data for surrogate models. This preliminary action captures the essential process-property relationships in advance, allowing fast predictions during actual welding optimization without repeatedly running expensive simulations. The surrogate models are pre-trained on comprehensive data covering wide ranges of process parameters.
Solution Approach 2:
The patent creates surrogate models that copy the complex process-property relationships learned from extensive experiments and simulations. Once trained, these surrogate models provide fast predictions that maintain high accuracy while eliminating the time-consuming nature of running full multi-physics models repeatedly during optimization iterations.
3Ease of manufacture
If empirical methods are used for thermoplastic composite welding process development, then ease of implementation is improved, but reliability and consistency of material properties decrease
Solution Approach 1:
The patent implements feedback loops where surrogate model predictions are continuously validated against experimental results and simulation data. The model uncertainty quantification provides feedback on prediction reliability, allowing the system to identify when additional experiments or simulations are needed. This feedback mechanism maintains reliability and consistency of material properties while keeping the process development systematic and manageable.
Solution Approach 2:
The patent replaces empirical trial-and-error methods with machine learning-based surrogate models that provide systematic and consistent predictions. The surrogate models substitute the empirical approach with a data-driven system that maintains reliability through training on comprehensive datasets and uncertainty quantification, while improving ease of manufacture through automated predictions.
4Device complexity
If machine learning models are trained solely on simulation or experimental data, then model development simplicity is improved, but computational expense and data requirements increase
Solution Approach 1:
The patent merges multiple data sources including simulation data, experimental data, and literature data into a unified training dataset for the surrogate models. This combination allows the model to learn from diverse sources, reducing the need for extensive single-source data generation while maintaining accuracy. The merged approach balances computational expense by leveraging available data from multiple sources rather than generating all training data through expensive simulations or experiments.
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 reducing computational costs, achieving faster and more efficient process optimization, with up to 25% energy savings and 2× faster process design optimization, 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
minimum time above a melting temperature
Implementation Method 4
cooling rate after welding to allow for recrystallization
Data Source
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.