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Voxel Printing Anatomy: Design and Fabrication of Realistic, Presurgical Planning Models through Bitmap Printing

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A Data-Driven Approach to Complex Voxel Predictions in Grayscale Digital Light Processing Additive Manufacturing

Jason P Killgore¹, Thomas J Kolibaba¹, Benjamin W Caplins¹

¹Applied Chemicals and Materials Division, National Institue of Standards and Technology, Boulder, CO, 80305, USA.

Small (Weinheim an Der Bergstrasse, Germany)

|July 6, 2023

Summary

This summary is machine-generated.

Machine learning models accurately predict 3D printing geometry in digital light processing (DLP) additive manufacturing. This data-driven approach enhances precision by correcting photomasks for improved voxel geometry control.

Keywords:

3D printing digital light processing machine learning neural networks pix2pix stereolithography vat photopolymerization

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Area of Science:

Materials Science
Computer Science

Background:

Digital Light Processing (DLP) is a key additive manufacturing technique.
Predicting and controlling voxel geometry is crucial for precision in DLP.

Purpose of the Study:

To develop and validate machine learning models for predicting 3D printed voxel geometry in DLP.
To explore the application of U-net and pix2pix conditional generative adversarial networks (cGANs) for enhanced precision.

Main Methods:

Utilized a confocal microscopy workflow for high-throughput data acquisition of voxel interactions.
Trained pix2pix cGAN models on data from randomly gray-scaled digital photomasks.
Validated model predictions against actual 3D prints, assessing sub-pixel resolution accuracy.

Main Results:

Machine learning models demonstrated accurate predictions of 3D printed voxel geometry with sub-pixel resolution.
The trained cGAN successfully performed virtual DLP experiments, including cure depth and anti-aliasing.
The pix2pix model showed applicability to larger masks than those used in training and could inform print failure analysis.

Conclusions:

Data-driven machine learning, particularly U-nets and cGANs, shows significant promise for predicting and correcting photomasks in DLP additive manufacturing.
This methodology can lead to increased precision and improved quality control in 3D printing processes.