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

页码：108 / 112

页数：5

共 50 条

[1] A fast differential evolution algorithm using k-Nearest Neighbour predictor
Liu, Yang
Sun, Fan
[J]. EXPERT SYSTEMS WITH APPLICATIONS, 2011, 38 (04) : 4254 - 4258
[2] An empirical analysis of the probabilistic K-nearest neighbour classifier
Manocha, S.
Girolami, M. A.
[J]. PATTERN RECOGNITION LETTERS, 2007, 28 (13) : 1818 - 1824
[3] Balanced k-nearest neighbour imputation
Hasler, Caren
Tille, Yves
[J]. STATISTICS, 2016, 50 (06) : 1310 - 1331
[4] k-Nearest Neighbour Classifiers - A Tutorial
Cunningham, Padraig
Delany, Sarah Jane
[J]. ACM COMPUTING SURVEYS, 2021, 54 (06)
[5] Prediction of MUET Results Based on K-Nearest Neighbour Algorithm
Sabri, Norlina Mohd
Hamrizan, Siti Fatimah Azzahra
[J]. Annals of Emerging Technologies in Computing, 2023, 7 (05) : 50 - 59
[6] Rock image classification based on k-nearest neighbour voting
Lepisto, L.
Kunttu, I.
Visa, A.
[J]. IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING, 2006, 153 (04): : 475 - 482
[7] A DEPTH-BASED MODIFICATION OF THE K-NEAREST NEIGHBOUR METHOD
Vencalek, Ondrej
Hlubinka, Daniel
[J]. KYBERNETIKA, 2021, 57 (01) : 15 - 37
[8] A binary neural k-nearest neighbour technique
Victoria J. Hodge
Jim Austin
[J]. Knowledge and Information Systems, 2005, 8 : 276 - 291
[9] A stacking weighted k-Nearest neighbour with thresholding
Rastin, Niloofar
Taheri, Mohammad
Jahromi, Mansoor Zolghadri
[J]. INFORMATION SCIENCES, 2021, 571 : 605 - 622
[10] Exact bagging with k-nearest neighbour classifiers
Caprile, B
Merler, S
Furlanello, C
Jurman, G
[J]. MULTIPLE CLASSIFIER SYSTEMS, PROCEEDINGS, 2004, 3077 : 72 - 81

← 1 2 3 4 5 →