Supervised Machine Learning in Cold Metal Transfer (CMT)

S. Arungalai Vendan; Rajeev Kamal; Abhinav Karan; Liang Gao; Xiaodong Niu; Akhil Garg

doi:10.1007/978-981-13-9382-2_2

Supervised Machine Learning in Cold Metal Transfer (CMT)

S. Arungalai Vendan^*, Rajeev Kamal, Abhinav Karan, Liang Gao, Xiaodong Niu, Akhil Garg

^*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceeding › Chapter › peer-review

1 Citation (Scopus)

Abstract

The cold metal transfer process evolved from the MIG/MAG process to address the lacunas posed during the aluminium and other materials joining. CMT being controlled process permits the material transfer with the nominal flow of current thereby reducing power consumption and material wastage. Constant retraction of filler wire at very short intervals is one of the major sequential steps in CMT process.

Original language	English
Title of host publication	Engineering Applications of Computational Methods
Publisher	Springer
Pages	57-118
Number of pages	62
DOIs	https://doi.org/10.1007/978-981-13-9382-2_2
Publication status	Published - 2020
Externally published	Yes

Publication series

Name	Engineering Applications of Computational Methods
Volume	1
ISSN (Print)	2662-3366
ISSN (Electronic)	2662-3374

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10.1007/978-981-13-9382-2_2

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AU - Niu, Xiaodong

AU - Garg, Akhil

PY - 2020

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Supervised Machine Learning in Cold Metal Transfer (CMT)

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