Monitoring coated carbide tool wear via chip analysis in hightemperature machining
ABSTRACT Stainless steels are pivotal materials in industry and find extensive application in various types of equipment due to their excellent chemical properties, such as high corrosion resistance and the ability to withstand elevated temperatures. However, they exhibit greater machining challenges compared to common carbon and low-alloy steels, mainly because of their high work-hardening rate during cutting operations. Consequently, the industry has a strong interest in understanding and monitoring machining techniques for these materials. This study introduces a novel approach by correlating tool wear progression with chip morphology in the dry turning of SAE 304 stainless steel, using coated carbide tools. Wear tests were carried out, and chip samples were collected at each stage of tool wear. Analyses of chip type and shape were performed, followed by metallographic evaluations. The results indicate a measurable correlation between wear stages and chip morphology, suggesting that chip analysis can serve as a practical method for real-time tool condition monitoring.This finding provides a cost-effective alternative for tool wear assessment, enhancing the efficiency and reliability of machining operations involving austenitic stainless steels.
Authors
- Luiz Leroy Thomé Vaughan (ORCID: https://orcid.org/0000-0002-0776-8253)
- Flávia Cristina Sousa e Silva (ORCID: https://orcid.org/0000-0002-6407-6106)
- Cláudio Gomes do Nascimento
- Feliciano José Ricardo Cangue (ORCID: https://orcid.org/0000-0003-0560-3815)
- José Aécio Gomes de Sousa (ORCID: https://orcid.org/0000-0003-4729-8353)
Institutions
- Total (France) (FR)
- Universidade Tecnológica Federal do Paraná (BR)
- Universidade Federal de Uberlândia (BR)
- Universidade Federal de Itajubá (BR)
Publication Details
- Journal
- Matéria (Rio de Janeiro)
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1590/1517-7076-rmat-2025-0114
- Citations
- 1
- Primary Topic
- Advanced machining processes and optimization
- Type
- article
- Field-Weighted Citation Impact
- 0.71