A real-time iterative machine learning approach for temperature profile prediction in additive manufacturing processes

by   Arindam Paul, et al.

Additive Manufacturing (AM) is a manufacturing paradigm that builds three-dimensional objects from a computer-aided design model by successively adding material layer by layer. AM has become very popular in the past decade due to its utility for fast prototyping such as 3D printing as well as manufacturing functional parts with complex geometries using processes such as laser metal deposition that would be difficult to create using traditional machining. As the process for creating an intricate part for an expensive metal such as Titanium is prohibitive with respect to cost, computational models are used to simulate the behavior of AM processes before the experimental run. However, as the simulations are computationally costly and time-consuming for predicting multiscale multi-physics phenomena in AM, physics-informed data-driven machine-learning systems for predicting the behavior of AM processes are immensely beneficial. Such models accelerate not only multiscale simulation tools but also empower real-time control systems using in-situ data. In this paper, we design and develop essential components of a scientific framework for developing a data-driven model-based real-time control system. Finite element methods are employed for solving time-dependent heat equations and developing the database. The proposed framework uses extremely randomized trees - an ensemble of bagged decision trees as the regression algorithm iteratively using temperatures of prior voxels and laser information as inputs to predict temperatures of subsequent voxels. The models achieve mean absolute percentage errors below 1 processes. The code is made available for the research community at https://anonymous.4open.science/r/112b41b9-05cb-478c-8a07-9366770ee504.


page 1

page 2

page 3

page 5

page 7

page 9


Hybrid full-field thermal characterization of additive manufacturing processes using physics-informed neural networks with data

Understanding the thermal behavior of additive manufacturing (AM) proces...

Additive manufacturing process design with differentiable simulations

We present a novel computational paradigm for process design in manufact...

A Physics-Informed Machine Learning Model for Porosity Analysis in Laser Powder Bed Fusion Additive Manufacturing

To control part quality, it is critical to analyze pore generation mecha...

An Evolutional Algorithm for Automatic 2D Layer Segmentation in Laser-aided Additive Manufacturing

Toolpath planning is an important task in laser aided additive manufactu...

Fast and Accurate Reduced-Order Modeling of a MOOSE-based Additive Manufacturing Model with Operator Learning

One predominant challenge in additive manufacturing (AM) is to achieve s...

Geometric Modeling and Physics Simulation Framework for Building a Digital Twin of Extrusion-based Additive Manufacturing

Accurate simulation of the printing process is essential for improving p...

Please sign up or login with your details

Forgot password? Click here to reset