近日，美国普渡大学的Xianfan Xu及其研究团队取得一项新进展。他们通过基于高斯过程主动机器学习的贝叶斯优化，提高投影多光子3D打印的几何精度。相关研究成果已于2025年1月20日在国际知名学术期刊《光：科学与应用》上发表。

多光子聚合是一种成熟且仍在积极发展的增材制造技术，适用于微/纳米尺度的3D打印。与所有增材制造技术一样，使用该方法进行3D打印时，确定实现结构尺寸精度所需的工艺参数并不总是那么简单，可能需要耗时的实验。

本文提出一种基于主动机器学习的框架，用于确定最近开发的高速逐层连续投影3D打印工艺的最佳工艺参数。所提出的主动学习框架采用贝叶斯优化来指导最佳实验设计，以便自适应地收集最具信息量的数据，从而有效训练基于高斯过程回归的机器学习模型。然后，该模型作为制造过程的替代模型，用于预测实现目标几何形状（例如，每个打印层的二维几何形状）的最佳工艺参数。

本文选取了三种不同尺度下的代表性二维形状作为测试用例。在每个案例中，主动学习框架都提高了几何精度，仅在四次贝叶斯优化迭代中就将误差大幅降低到测量精度范围内，且总训练数据量仅数百个。这些案例研究表明，本文开发的主动学习框架可广泛应用于其他增材制造工艺，以显著提高精度，同时大幅减少优化所需的实验数据采集工作量。

附：英文原文

Title: Bayesian optimization with Gaussian-process-based active machine learning for improvement of geometric accuracy in projection multi-photon 3D printing

Author: Johnson, Jason E., Jamil, Ishat Raihan, Pan, Liang, Lin, Guang, Xu, Xianfan

Issue&Volume: 2025-01-20

Abstract: Multi-photon polymerization is a well-established, yet actively developing, additive manufacturing technique for 3D printing on the micro/nanoscale. Like all additive manufacturing techniques, determining the process parameters necessary to achieve dimensional accuracy for a structure 3D printed using this method is not always straightforward and can require time-consuming experimentation. In this work, an active machine learning based framework is presented for determining optimal process parameters for the recently developed, high-speed, layer-by-layer continuous projection 3D printing process. The proposed active learning framework uses Bayesian optimization to inform optimal experimentation in order to adaptively collect the most informative data for effective training of a Gaussian-process-regression-based machine learning model. This model then serves as a surrogate for the manufacturing process: predicting optimal process parameters for achieving a target geometry, e.g., the 2D geometry of each printed layer. Three representative 2D shapes at three different scales are used as test cases. In each case, the active learning framework improves the geometric accuracy, with drastic reductions of the errors to within the measurement accuracy in just four iterations of the Bayesian optimization using only a few hundred of total training data. The case studies indicate that the active learning framework developed in this work can be broadly applied to other additive manufacturing processes to increase accuracy with significantly reduced experimental data collection effort for optimization.

DOI: 10.1038/s41377-024-01707-8

Source: https://www.nature.com/articles/s41377-024-01707-8