Research on tool wear classification of milling 508III steel based on chip spectrum feature

Guan, Rui; Cheng, Yaonan; Zhou, Shilong; Gai, Xiaoyu; Lu, Mengda; Xue, Jing

doi:10.1007/s00170-024-13854-8

Int J Adv Manuf Technol

2024

DOI: 10.1007/s00170-024-13854-8

|View full text |Cite

Research on tool wear classification of milling 508III steel based on chip spectrum feature

Rui Guan,

Yaonan Cheng,

Shilong Zhou

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Experimental investigation of different machine learning approaches for tool wear classification based on vision system of milled surface

El-Taybany,

Elhendawy

2024

Int J Interact Des Manuf

View full text Add to dashboard Cite

Experimental investigation of different machine learning approaches for tool wear classification based on vision system of milled surface

El-Taybany,

Elhendawy

2024

Int J Interact Des Manuf

View full text Add to dashboard Cite

Analysis of Chip Morphology in Heavy Milling of 508III Steel Considering Different Tool Wear Conditions

Guan,

Cheng,

Xue

et al. 2024

Materials

View full text Add to dashboard Cite

During the process of chip formation, the chip is subjected to extrusion pressure, friction, heat, and a strong chemical reaction. The chip’s macro and micro morphology, to a certain extent, reflect the condition of the tool during the cutting procedure. Therefore, researching the macroscopic and microscopic morphology of the chip’s surface in response to different tool wear conditions is of great significance to reproducing the cutting condition and analyzing the tool wear mechanism. This paper focuses on the chips formed by milling the difficult-to-machine material 508III high-strength steel. Firstly, the 508III steel milling experiment is carried out at the actual machining site to collect chip data under different tool wear conditions. Next, the free surface morphology of chips and the bottom surface morphology of chips are analyzed. Further, the chip edges are investigated, and their causes are analyzed. Finally, heavy milling 508III steel chip curl morphology analysis is performed. The research results play important roles in revealing the mechanism of tool wear and the relationship between chip morphology and tool wear. This information can be used to provide theoretical and technical support for monitoring the tool wear status based on chip morphology.

show abstract

A Review of Physics-Based, Data-Driven, and Hybrid Models for Tool Wear Monitoring

Zhang,

Jiang,

Gao

et al. 2024

Machines

View full text Add to dashboard Cite

Tool wear is an inevitable phenomenon in the machining process. By monitoring the wear state of a tool, the machining system can give early warning and make advance decisions, which effectively ensures improved machining quality and production efficiency. In the past two decades, scholars have conducted extensive research on tool wear monitoring (TWM) and obtained a series of remarkable research achievements. However, physics-based models have difficulty predicting tool wear accurately. Meanwhile, the diversity of actual machining environments further limits the application of physical models. Data-driven models can establish the deep mapping relationship between signals and tool wear, but they only fit trained data well. They still have difficulty adapting to complex machining conditions. In this paper, physics-based and data-driven TWM models are first reviewed in detail, including the factors that affect tool wear, typical data-based models, and methods for extracting and selecting features. Then, tracking research hotspots, emerging physics–data fusion models are systematically summarized.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Research on tool wear classification of milling 508III steel based on chip spectrum feature

Cited by 3 publications

References 29 publications

Experimental investigation of different machine learning approaches for tool wear classification based on vision system of milled surface

Experimental investigation of different machine learning approaches for tool wear classification based on vision system of milled surface

Analysis of Chip Morphology in Heavy Milling of 508III Steel Considering Different Tool Wear Conditions

A Review of Physics-Based, Data-Driven, and Hybrid Models for Tool Wear Monitoring

Contact Info

Product

Resources

About