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<p>The utilization of 3D printing technology has undergone a significant upsurge in the production of components and devices across various scientific, engineering, and technological spheres. Among the array of additive manufacturing (AM) techniques, VAT photopolymerization emerges as a promising avenue for fabricating millimeter-scale objects with resolutions on the order of tens of micrometers. Within VAT photopolymerization, digital light processing (DLP) stands out as a technique where layer-by-layer construction is pivotal. In this context, both the thickness of each layer and the angle at which consecutive layers are deposited become critical factors in shaping the ultimate mechanical strength of the object. This publication introduces an integrated <i>experimental framework</i>, a reproducible methodology that combines structural design, fixture adaptation, and mechanical analysis, to facilitate the tensile testing of millimeter scale DLP printed structures using conventional equipment. Rather than a formal standard, the framework provides a generalized, reusable workflow for correlating printing parameters (tilt angle, layer thickness) with mechanical response. Additionally, there has been a notable rise in the integration of machine learning techniques in digital AM, where simulation data are employed to train the machine learning models. In this manuscript, we advocate for the incorporation of systematically obtained experimental data, as such performed in this manuscript, into the training of machine learning models, aiming to incorporate discrepancies in experimental setups or environments.</p>

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New framework for design and mechanical testing of 3D structures printed via digital light processing (DLP)

  • 期刊论文

  • 发表日期：

    2026年1月7日

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  • Saurabh Awasthi,
  • Arist Balaj,
  • Zhoubin Ni,
  • SeungYeon Kang

摘要

The utilization of 3D printing technology has undergone a significant upsurge in the production of components and devices across various scientific, engineering, and technological spheres. Among the array of additive manufacturing (AM) techniques, VAT photopolymerization emerges as a promising avenue for fabricating millimeter-scale objects with resolutions on the order of tens of micrometers. Within VAT photopolymerization, digital light processing (DLP) stands out as a technique where layer-by-layer construction is pivotal. In this context, both the thickness of each layer and the angle at which consecutive layers are deposited become critical factors in shaping the ultimate mechanical strength of the object. This publication introduces an integrated experimental framework, a reproducible methodology that combines structural design, fixture adaptation, and mechanical analysis, to facilitate the tensile testing of millimeter scale DLP printed structures using conventional equipment. Rather than a formal standard, the framework provides a generalized, reusable workflow for correlating printing parameters (tilt angle, layer thickness) with mechanical response. Additionally, there has been a notable rise in the integration of machine learning techniques in digital AM, where simulation data are employed to train the machine learning models. In this manuscript, we advocate for the incorporation of systematically obtained experimental data, as such performed in this manuscript, into the training of machine learning models, aiming to incorporate discrepancies in experimental setups or environments.