# Data-driven non-intrusive reduced order modelling of selective laser melting additive manufacturing process using proper orthogonal decomposition and convolutional autoencoder

## TL;DR

This paper introduces two machine learning models to speed up simulations of 3D printing processes while maintaining accuracy.

## Contribution

The novel contribution is the development and comparison of two non-intrusive reduced-order models (POD-ANN and CAE-MLP) for additive manufacturing simulations.

## Key findings

- The CAE-MLP model outperforms the POD-ANN model in prediction accuracy and performance.
- Both models show strong correlation with high-fidelity simulations and experimental results.
- The integration of machine learning with reduced-order modeling improves analysis efficiency for additive manufacturing.

## Abstract

This study proposes and compares two data-driven, non-intrusive reduced-order models (ROMs) for additive manufacturing (AM) processes: a combined proper orthogonal decomposition-artificial neural network (POD-ANN) and a convolutional autoencoder-multilayer perceptron (CAE-MLP). The POD-ANN model utilizes proper orthogonal decomposition to create a reduced-order model, which is then combined with an artificial neural network to establish a surrogate model linking the snapshot matrix to the input parameters. This approach effectively reduces the dimensionality of the high-fidelity snapshot matrix and constructs a regression framework for accurate predictions. Conversely, the CAE-MLP model employs a 1D convolutional autoencoder to reduce the spatial dimension of a high-fidelity snapshot matrix derived from numerical simulations. The compressed latent space is then projected onto the input variables using a multilayer perceptron (MLP) regression model. This method leverages deep learning techniques to handle the complexity of the data and improve prediction accuracy. The accuracy and efficiency of both models are evaluated through thermo-mechanical analysis of an AM-built part. The comparison of statistical moments from high-fidelity simulation results with ROM predictions reveals a strong correlation. Furthermore, the predictions are validated against experimental results at various locations. While both models demonstrate good agreement with experimental data, the CAE-MLP model outperforms the POD-ANN model in terms of prediction accuracy and performance. The findings highlight the potential of integrating reduced-order modeling techniques with machine learning algorithms to enhance the analysis of complex AM processes. The proposed models offer a robust framework for future research and applications in the field of additive manufacturing, providing high precision and efficiency.