2014
DOI: 10.1007/s12206-013-1135-2
|View full text |Cite
|
Sign up to set email alerts
|

Delamination analysis of the helical milling of carbon fiber-reinforced plastics by using the artificial neural network model

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
14
0

Year Published

2015
2015
2021
2021

Publication Types

Select...
8
2

Relationship

0
10

Authors

Journals

citations
Cited by 43 publications
(20 citation statements)
references
References 14 publications
0
14
0
Order By: Relevance
“…Recent research [26] shows that helical milling of CFRP results in much lower delamination than that in other processing methods including high-speed drilling. This is because helical milling reduces the thrust force, and more space is available to expel the chips.…”
Section: Delamination In Millingmentioning
confidence: 99%
“…Recent research [26] shows that helical milling of CFRP results in much lower delamination than that in other processing methods including high-speed drilling. This is because helical milling reduces the thrust force, and more space is available to expel the chips.…”
Section: Delamination In Millingmentioning
confidence: 99%
“…To correctly identify texture images,machine learning is considered to be a prominent techique. It is used for classification and regression and is used by many authors in the variety of applications such as EEG signal classification [14], Fault diagnosis [15,16], Manufacturing [17,18] etc. Support Vector Machine (SVM) [19], Artificial Neural Network (ANN) [20], Naive Bayes (NB) are widely used machine learning algorithms and is used by many authors for classification purpose.…”
Section: Introductionmentioning
confidence: 99%
“…Artificial neural network model was developed to fuse thrust force, cutting speed, spindle speed and feed parameters to predict the drilled hole number. Qin et al [11] explored the better hole quality with one-time machining by helical milling in a carbon fiber-reinforced plastic. A neural network model developed based on the correlation between the delamination and the process parameters.…”
Section: Introductionmentioning
confidence: 99%