Tool Health Classification in Metallic Milling Process Using Acoustic Emission and Long Short-Term Memory Networks: A Deep Learning Approach-Reference-Cited by-同舟云学术

Tool Health Classification in Metallic Milling Process Using Acoustic Emission and Long Short-Term Memory Networks: A Deep Learning Approach

Author:

Khan Fawad¹^ORCID,Kamal Khurram¹,Ratlamwala Tahir Abdul Hussain¹^ORCID,Alkahtani Mohammed²^ORCID,Almatani Mohammed²^ORCID,Mathavan Senthan³^ORCID

Affiliation:

1. Department of Engineering Sciences, National University of Sciences and Technology, Islamabad, Pakistan

2. Department of Industrial Engineering, College of Engineering, King Saud University, Riyadh, Saudi Arabia

3. Department of Civil and Structural Engineering, Nottingham Trent University, Nottingham, U.K

Funder

King Saud University, Riyadh, Saudi Arabia, through the Researchers Supporting Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

Reference32 articles.

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