Artificial neural Network-Based approaches for Bi-directional modelling of robotic wire arc additive manufacturing

被引：3

作者：

Bose, Souvik ^{[1
]}

Biswas, Adrija ^{[1
]}

Tiwari, Yoshit ^{[2
]}

Mukherjee, Manidipto ^{[2
]}

Roy, Shibendu Shekhar ^{[1
]}

机构：

[1] Natl Inst Technol, Dept Mech Engn, Durgapur, WB, India

[2] CSIR Cent Mech Engn Res Inst, Durgapur, WB, India

来源：

MATERIALS TODAY-PROCEEDINGS | 2022年 / 62卷

关键词：

WAAM; Bead geometry; Neural network; Momentum; Grey wolf optimizer; Metaheuristic; BEAD GEOMETRY; OPTIMIZATION; PREDICTION;

D O I：

10.1016/j.matpr.2022.04.331

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Additive manufacturing (AM) process has seen its usage go astronomically up in the recent years. One of the oldest additive manufacturing processes namely, wire arc additive manufacturing (WAAM) has seen many recent developments due to the urge of attaining high efficiency in manufacturing field. Not only is it a very simple process among the other AM processes, but also it can be used for manufacturing very complex structures with a very low budget. The process of robotic WAAM demands precise modelling of the relationship between the input and response parameters so as to guarantee desired output of fine quality. To meet the demands of the process, hybridized artificial neural network (ANN) models were developed in this study and used for forward and backward mappings. Gradient descent with momentum and grey wolf optimization (GWO) are the algorithms that have been coupled with the neural networks in this paper. The novelty of this paper lies with the performance comparison in bi-directional mapping by hybridizing ANN models with the aforementioned algorithms in robotic WAAM domain. The gradient descent with momentum algorithm has shown better results than the metaheuristic algorithm in both the mappings while taking much lesser execution time. Copyright (C) 2022 Elsevier Ltd. All rights reserved.

引用

页码：6507 / 6513

页数：7