Machine learning classification applied to the effect of AFSD process parameters on tensile properties

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

Materials Letters Pub Date : 2024-09-11 DOI:10.1016/j.matlet.2024.137356

引用次数: 0

Abstract

Additive Friction Stir Deposition (AFSD) is a key technology in additive manufacturing, where process parameters greatly impact deposition layer properties. Currently, there is no established method for systematically improving these properties through parameter investigation. This study addresses this by applying six machine learning (ML) classification algorithms to a dataset of 130 samples, classifying the ultimate tensile strength (UTS) based on feed rate, rotational speed, and downforce. The Support Vector Machine (SVM) algorithm achieved the highest accuracy at 95.3%. This research provides a precise method for classifying AFSD process parameters and demonstrates the potential of ML techniques for optimizing manufacturing processes, presenting a novel approach to enhancing AFSD deposition layer performance.

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将机器学习分类应用于 AFSD 工艺参数对拉伸性能的影响

快速摩擦搅拌沉积（AFSD）是快速成型制造中的一项关键技术，其工艺参数对沉积层性能有很大影响。目前，还没有一种成熟的方法可以通过参数调查来系统地改善这些特性。为了解决这个问题，本研究将六种机器学习 (ML) 分类算法应用于 130 个样本的数据集，根据进给率、转速和下压力对极限拉伸强度 (UTS) 进行分类。支持向量机 (SVM) 算法的准确率最高，达到 95.3%。这项研究提供了一种对 AFSD 过程参数进行分类的精确方法，展示了 ML 技术在优化制造过程方面的潜力，为提高 AFSD 沉积层性能提供了一种新方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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