Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Performance Evaluation of Machine Learning-based Algorithm and Taguchi
  Algorithm for the Determination of the Hardness Value of the Friction Stir
  Welded AA 6262 Joints at a Nugget Zone   Nowadays, industry 4.0 plays a tremendous role in the manufacturing
industries for increasing the amount of data and accuracy in modern
manufacturing systems. Thanks to artificial intelligence, particularly machine
learning, big data analytics have dramatically amended, and manufacturers
easily exploit organized and unorganized data. This study utilized hybrid
optimization algorithms to find friction stir welding and optimal hardness
value at the nugget zone. A similar AA 6262 material was used and welded in a
butt joint configuration. Tool rotational speed (RPM), tool traverse speed
(mm/min), and the plane depth (mm) are used as controllable parameters and
optimized using Taguchi L9, Random Forest, and XG Boost machine learning tools.
Analysis of variance was also conducted at a 95% confidence interval for
identifying the significant parameters. The result indicated that the
coefficient of determination from Taguchi L9 orthogonal array is 0.91 obtained
while Random Forest and XG Boost algorithm imparted 0.62 and 0.65,
respectively.

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The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
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The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
19
The categories of this paper are: eess
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
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The categories of this paper are: hep-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-fin