Near-perfect replication on amorphous alloys through active force modulation based on machine learning/neural network parameter prediction  

作　　者：Senkuan Meng Zheng Wang Ruisong Zhu Ruijie Liu Jiang Ma Lina Hu Weihua Wang 

机构地区：[1]Key Laboratory for Liquid-Solid Structural Evolution and Processing of Materials(Ministry of Education),School of Materials Science and Engineering,Shandong University,Jinan 250061,China [2]College of Mechatronics and Control Engineering,Shenzhen University,Shenzhen 518060,China [3]Songshan Lake Materials Laboratory,Dongguan 523808,China [4]Institute of Physics,Chinese Academy of Sciences,Beijing 100190,China

出　　处：《Science China(Physics,Mechanics & Astronomy)》2025年第1期177-187,共11页中国科学:物理学、力学、天文学(英文版)

基　　金：supported by the Key Basic and Applied Research Program of Guangdong Province(Grant No.2019B030302010);the Taishan Scholars Program of Shandong Province(Grant No.tsqn201909010);the National Natural Science Foundation of China(Grant Nos.51901139,51971120 and U1902221);the Key R&D Program of Shandong Province(Grant No.2022CXGC020308)。

摘　　要：As a microforming technique,micro/nano-structural replication possesses advantages of high precision and efficiency.With the remarkable superplasticity in the supercooled liquid region,amorphous alloys or metallic glasses(MGs)are regarded as ideal materials for miniature fabrication.However,due to the intrinsic metastable nature of supercooled liquids,the design of imprinting processes for MGs poses a challenge.In the past,process parameters have largely relied on trial-and-error strategies.In this work,a low-frequency active force modulation system is employed to apply a stable,precise,and real-time feedback stress field for imprinting of MG samples.Low-frequency vibrations can facilitate the filling of microstructures on the template surface by reducing the effective viscosity of the supercooled liquid.With the dataset composed of orthogonal experiments,a machine learning strategy based on back-propagation(BP)neural networks was utilized to construct a 3D parameter space for temperature,stress,and time,and to predict the corresponding filling ratio.Furthermore,the optimal combination of imprinting process parameters was identified,and its filling ratio was experimentally validated to reach as high as 0.94.The near-perfect replication of microstructures confirms the superiority of the active force modulation system and the data-driven strategy of machine learning-assisted parameter design.At the same time,this one-step microforming process provides a new approach to addressing the accuracy-cost trade-off dilemma in precision manufacturing.

关 键 词：metallic glass amorphous alloy MICROFORMING IMPRINTING machine learning 

分 类 号：TG139.8[一般工业技术—材料科学与工程] TP18[金属学及工艺—合金]