Long-short-term memory (LSTM)-based modeling of the stiffness of 3D-printed PLA parts

IF 2.7 4区材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY

Materials Letters Pub Date : 2024-10-30 DOI:10.1016/j.matlet.2024.137636

Mohammad Hossein Nikzad , Mohammad Heidari-Rarani , Reza Rasti

引用次数: 0

Abstract

This study applied a computationally efficient Taguchi-based long-short-term memory (LSTM) algorithm to predict the elastic modulus of 3D-printed polylactic acid (PLA) specimens. 128 data points were collected from the literature, and 80% were allocated for training and the rest for the validation of the LSTM algorithm. The results suggested that the LSTM algorithm, configured with 25 units in the first memory cell, 100 units in the second memory cell, the “selu” activation function in the first memory cell, the “elu” activation function in the second memory cell, the RMSprop optimizer, and a learning rate of 0.01, was precisely able to predict the elastic modulus of 3D-printed PLA parts.

Abstract Image

查看原文本刊更多论文

基于长短期记忆（LSTM）的 3D 打印聚乳酸部件刚度建模

本研究采用了一种计算效率较高的基于田口长短期记忆（LSTM）的算法来预测三维打印聚乳酸（PLA）试样的弹性模量。研究从文献中收集了 128 个数据点，其中 80% 用于训练，其余用于验证 LSTM 算法。结果表明，在第一存储单元中配置 25 个单元、在第二存储单元中配置 100 个单元、在第一存储单元中配置 "selu "激活函数、在第二存储单元中配置 "elu "激活函数、RMSprop 优化器和 0.01 学习率的 LSTM 算法能够精确预测 3D 打印聚乳酸部件的弹性模量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Materials Letters 工程技术-材料科学：综合

CiteScore

5.60

自引率

3.30%

发文量

1948

审稿时长

50 days

期刊介绍： Materials Letters has an open access mirror journal Materials Letters: X, sharing the same aims and scope, editorial team, submission system and rigorous peer review. Materials Letters is dedicated to publishing novel, cutting edge reports of broad interest to the materials community. The journal provides a forum for materials scientists and engineers, physicists, and chemists to rapidly communicate on the most important topics in the field of materials. Contributions include, but are not limited to, a variety of topics such as: • Materials - Metals and alloys, amorphous solids, ceramics, composites, polymers, semiconductors • Applications - Structural, opto-electronic, magnetic, medical, MEMS, sensors, smart • Characterization - Analytical, microscopy, scanning probes, nanoscopic, optical, electrical, magnetic, acoustic, spectroscopic, diffraction • Novel Materials - Micro and nanostructures (nanowires, nanotubes, nanoparticles), nanocomposites, thin films, superlattices, quantum dots. • Processing - Crystal growth, thin film processing, sol-gel processing, mechanical processing, assembly, nanocrystalline processing. • Properties - Mechanical, magnetic, optical, electrical, ferroelectric, thermal, interfacial, transport, thermodynamic • Synthesis - Quenching, solid state, solidification, solution synthesis, vapor deposition, high pressure, explosive