Modelling and optimisation of creep feed deep surface grinding using FEM-based NNGA

被引：0

作者：

Narayan, Audhesh ^{[1
]}

Yadava, Vinod ^{[1
]}

机构：

[1] Motilal Nehru Natl Inst Technol, Allahabad 211004, Uttar Pradesh, India

来源：

INTERNATIONAL JOURNAL OF ENGINEERING SYSTEMS MODELLING AND SIMULATION | 2016年 / 8卷 / 01期

关键词：

creep feed deep surface grinding; CFDSG; thermal stress; finite element method; FEM; neural network; genetic algorithm;

D O I：

10.1504/IJESMS.2016.073320

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This paper presents the application of a hybrid approach comprising of neural network and genetic algorithm for modelling and optimisation of creep feed deep surface grinding process. Finite element method has been used to generate dataset for neural network model. Subsequently, NN model has been coupled with genetic algorithm to find optimum input parameters of creep feed deep surface grinding. The proposed hybrid approach is well capable to predict thermal stresses in the workpiece quickly and also minimise it with reasonable accuracy during creep feed deep surface grinding process.

引用

页码：65 / 74

页数：10

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