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

作　　者：Jason E.Johnson Ishat Raihan Jamil Liang Pan Guang Lin Xianfan Xu 

机构地区：[1]School of Mechanical Engineering,Purdue University,585 Purdue Mall,West Lafayette,IN47907,USA [2]Birck Nanotechnology Center,Purdue University,1205 Mitch Daniels Blvd,WestLafayette,IN47907,USA [3]Department of Mathematics,Purdue University,Mathematical Sciences Bldg,150 N University St,West Lafayette,IN47907,USA

出　　处：《Light(Science & Applications)》2025年第2期509-520,共12页光(科学与应用)(英文版)

基　　金：supported by the National Science Foundation(NSF)through grant numbers CMMI-2135585 and CMMI-2229143;J.EJ.acknowledges the National Science Foundation for support under the Graduate Research Fellowship Program(GRFP)under grant number DGE-1842166.

摘　　要：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 effortforoptimization.

关 键 词：determining optimal process parameters process parameters manufacturing technique dimensional accuracy active machine learning structure d printed Bayesian optimization additive manufacturing 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]