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Deep learning models for optically characterizing 3D printers.

Danwu Chen, Philipp Urban

|March 17, 2021

Summary

This summary is machine-generated.

Two new deep learning models accurately predict 3D printer optical properties using fewer training samples. These models, Pure Deep Learning (PDL) and Deep-Learning-Linearized Cellular Neugebauer (DLLCN), enable precise material characterization for advanced applications.

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Area of Science:

* Additive Manufacturing
* Optical Engineering
* Computational Materials Science

Background:

* Multi-material 3D printing enables complex material arrangements with diverse optical characteristics.
* Accurate optical printer models are essential for predicting and reproducing these properties from printer control values (tonals).

Purpose of the Study:

* To develop and evaluate deep learning-based models for accurate optical characterization of 3D printers.
* To reduce the number of training samples required for high-accuracy optical property prediction.

Main Methods:

* Development of a Pure Deep Learning (PDL) model, a black-box approach.
* Implementation of a Deep-Learning-Linearized Cellular Neugebauer (DLLCN) model, leveraging deep learning to linearize the tonal-value-space of a cellular Neugebauer model.
* Testing on two six-material polyjetting 3D printers to predict reflectance and translucency.

Main Results:

* Both PDL and DLLCN models achieved high accuracy in predicting optical properties (reflectance and translucency).
* The models required significantly fewer training prints compared to a standard cellular Neugebauer model.
* Accuracies achieved were sufficient for most practical applications.

Conclusions:

* Deep learning offers an effective strategy for optically characterizing multi-material 3D printers.
* PDL and DLLCN models provide accurate predictions with reduced data requirements, advancing material design in 3D printing.
* These models enhance the ability to reproduce desired optical properties in 3D printed objects.