Chip Analysis for Tool Wear Monitoring in Machining: A Deep Learning Approach-Reference-Cited by-同舟云学术

Chip Analysis for Tool Wear Monitoring in Machining: A Deep Learning Approach

Published:2024 Issue: Volume:12 Page:112672-112689
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Ur Rehman Atiq¹^ORCID,Salwa Rabbi Nishat Tahira¹,Uddin Ahmed Mobyen¹^ORCID,Begum Shahina¹,Ranjan Abhishek²^ORCID

Affiliation:

1. Artificial Intelligence and Intelligent Systems Research Group, School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden

2. Research and Development Team, SECO Tools AB, Västmanland, Sweden

Funder

DIGital Twins for Industrial COGnitive Systems (DIGICOGS) through the Industry 4.0 and Artificial Intelligence

Vinnova Diarienr and the Innovation Program Process Industrial IT and Automation

Cognitive Predictive Maintenance and Quality Assurance using Explainable Ai and Machine Learning

Sweden’s Innovation Agency (VINNOVA) and Mälardalen University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10636165.pdf?arnumber=10636165

Reference44 articles.

1. Performance analysis of coated carbide tool in turning of Nimonic 80A superalloy under different cutting environments

2. Evaluation of turning with different cooling-lubricating techniques in terms of surface integrity and tribologic properties

3. Intelligent tool wear monitoring and multi-step prediction based on deep learning model

4. Investigation of microstructure, mechanical and machinability properties of Mo-added steel produced by powder metallurgy method

5. Finite Element Modelling of Cutting Forces and Power Consumption in Turning of AISI 420 Martensitic Stainless Steel