ML Surrogate Model for Binder Jet Distortion Prediction
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Solution Overview
Problem
Current methods for quantifying distortion of binder jet parts during sintering are computationally costly and inefficient, as they require extensive simulations and retraining for each geometry, limiting their predictive power and scalability.
Innovation Solution
The development of machine-learning surrogate models, specifically deep neural networks, that can predict distortion and variability of complex geometries by training on simple geometries, using image-based representations and convolutional neural networks (CNNs), enabling rapid prediction and analysis of distortion outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If calibrated computer simulation code is used to quantify distortion variability, then measurement precision is improved, but computational cost increases significantly
Solution Approach 1:
The system performs preliminary actions by training the machine learning model on a dataset of simulated green body parts with known distortion characteristics before actual production. This pre-training phase captures the relationship between manufacturing parameters and distortion outcomes, enabling rapid prediction without requiring costly real-time simulations for each new design.
Solution Approach 2:
The system creates a digital copy or surrogate model of the complex sintering process through machine learning. Instead of running computationally expensive physics-based simulations for each prediction, the system uses the trained ML model that replicates distortion behavior, providing accurate predictions at a fraction of the computational cost.
2Manufacturing precision
If extensive simulations are performed for each geometry, then manufacturing precision is improved, but productivity decreases
Solution Approach 1:
The system performs the computationally intensive work of learning distortion patterns in advance through training on diverse green body part geometries and material properties. Once trained, the model provides rapid predictions for new designs without requiring extensive simulations, thus maintaining precision while dramatically improving productivity.
Solution Approach 2:
The system changes the computational parameters from running full physics-based simulations to using a pre-trained machine learning model. This parameter change transitions the system from high-computation/low-speed mode to low-computation/high-speed mode while preserving prediction accuracy through the model's learned relationships.
3Adaptability or versatility
If retraining is performed for each geometry, then adaptability is improved, but loss of time increases
Solution Approach 1:
The machine learning model is designed with universality to handle multiple geometry types, material properties, and manufacturing parameters within a single model. The model learns generalizable patterns from diverse training data that apply across different green body part configurations, eliminating the need for geometry-specific retraining while maintaining adaptability.
Solution Approach 2:
The system performs comprehensive training in advance on a wide variety of geometries and conditions to build a robust, generalizable model. This preliminary action ensures the model can adapt to new geometries without requiring retraining, as it has already learned the underlying physical relationships that govern distortion across different configurations.
Data Source
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AI summary
A method for predicting distortion of a part during sintering in an additive process includes receiving a computerized representation of a complex geometric part, discretizing the computerized representation of the complex geometric part into a plurality of elements, processing the plurality of elements of the computerized representation of the complex geometric part with a machine-learning model configured to predict a distorted geometry of the complex geometric part in response to a sintering process, wherein the machine-learning model is trained to predict distortion of a set of primitive geometric coupons represented by image data fed into the machine-learning model during training, the set of primitive geometric coupons having fewer geometries than the complex geometric part, the complex geometric part comprises a plurality of geometries corresponding to geometries associated with the set of primitive geometric coupons, and generating a computerized representation of the predicted distorted geometry of the complex geometric part.