Analysis and modelling of dissimilar materials welding based on K-nearest neighbour predictor

被引：56
|
作者：
Sathish, T. [1 ]
Rangarajan, S. [2 ]
Muthuram, A. [2 ]
Kumar, R. Praveen [2 ]
机构：
[1] SMR East Coast Coll Engn & Technol, Thanjavur, Tamil Nadu, India
[2] St Peters Inst Higher Educ & Res, Chennai, Tamil Nadu, India
来源：
MATERIALS TODAY-PROCEEDINGS | 2020年 / 21卷
关键词：
Dissimilar materials welding; Weld seam geometry prediction; K-nearest neighbour; Taguchi experiment; Machine learning; NEURAL-NETWORK; PARAMETER OPTIMIZATION;
D O I：
10.1016/j.matpr.2019.05.371
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
In dissimilar materials the welded joints are apply to the Automotive and ship industries. The welded joints quality is better than the essential of suitable weld seam geometry. No previous technique produce the accurate prediction geometry of laser weld seam. The initial data generation Taguchi experiments are conducted on laser welding of two different materials. One is low carbon steel (Q235) and other is stainless steel (SUS301L-HT). The K-Nearest Neighbour predictor is modelled for the laser welding process. This method is providing the following function such as better prediction, accuracy and reduced computation time. (C) 2019 Elsevier Ltd. All rights reserved.
引用
收藏
页码：108 / 112
页数：5
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