钛合金电火花线切割表面粗糙度的机器学习建模

IF 3.5 Q1 ENGINEERING, MULTIDISCIPLINARY
Vikas Sharma, J. P. Misra, S. Singhal
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引用次数: 0

摘要

目的对钛合金的电火花线切割加工进行了研究,研究了电火花线的加工参数,如火花接通时间、火花关断时间、电流和伺服电压。本研究的目的是使用机器学习方法对输入/可控变量的表面粗糙度进行建模。使用扫描电子显微镜(SEM)和XRD方法检查机械加工表面。设计/方法/方法采用全因子法将不同加工参数的实验设计为三级四因子。使用机器学习方法,即高斯过程回归(GPR)和支持向量机(SVM)方法,对获得的表面粗糙度进行建模。在相关系数和均方根误差的基础上,对训练和测试数据的这些方法进行了比较。使用扫描电子显微镜和XRD对加工表面进行检查,以获得表面质量,并检查工具材料向工件材料的迁移。FindingsMachine学习算法在预测线切割加工(WEDM)过程的质量响应方面具有良好的范围,由于很难通过实验找到每次,因此节省了时间和成本。研究发现,所提出的模型计算时间最短,提供了更好的解决方案,并避免了决策者的优先权重计算。原始性/值所提出的建模可以更好地预测电火花线切割过程中使用镀锌黄铜丝电极加工Ti6Al7Nb时产生的表面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Surface roughness modeling using machine learning approaches for wire electro-spark machining of titanium alloy
PurposeIn the present study, wire electro-spark machining of Titanium alloy is performed with the machining parameter such as spark-on time, spark-off time, current and servo voltage. The purpose of this study is to model surface roughness using machine learning approach for input/controllable variable. Machined surface examined using scanning electron microscope (SEM) and XRD methods.Design/methodology/approachFull factorial approach has been used to design the experiments with varying machining parameters into three-level four factors. Obtained surface roughness was modeled using machine learning methods namely Gaussian process regression (GPR) and support vector machine (SVM) methods. These methods were compared for both training and testing data with a coefficient of correlation and root mean square error basis. Machined surface examined using scanned electron microscopy and XRD for surface quality produced and check migration of tool material to workpiece material.FindingsMachine learning algorithms has excellent scope for prediction quality response for the wire electric discharge machining (WEDM) process, resulting in saving of time and cost as it is difficult to find each time experimentally. It has been found that the proposed model with minimum computational time, provides better solution and avoids priority weightage calculation by decision-makers.Originality/valueThe proposed modeling provides better predication about surface produced while machining of Ti6Al7Nb using zinc-coated brass wire electrode during WEDM operation.
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来源期刊
International Journal of Structural Integrity
International Journal of Structural Integrity ENGINEERING, MULTIDISCIPLINARY-
CiteScore
5.40
自引率
14.80%
发文量
42
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