Predicting cutting tool life: models, modelling, and monitoring

被引：5

作者：

Khadka, Sujan ^{[1
]}

Rashid, Rizwan Abdul Rahman ^{[1
,4
]}

Stephens, Guy ^{[2
]}

Papageorgiou, Angelo ^{[2
]}

Navarro-Devia, John ^{[2
]}

Hagglund, Soren ^{[3
]}

Palanisamy, Suresh ^{[1
,4
]}

机构：

[1] Swinburne Univ Technol, Sch Engn, Mfg Futures Res Platform, Hawthorn, Vic 3122, Australia

[2] Sutton Tools Pty Ltd, 378 Settlement Rd, Thomastown, Vic 3074, Australia

[3] Seco Tools AB, R&D, Bjornbacksvagen 10, S-73747 Fagersta, Sweden

[4] DMTC Ltd, Level 1-620 High St, Kew, Vic 3101, Australia

来源：

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY | 2025年 / 136卷 / 7-8期

关键词：

Tool wear; Tool life; Tool life models; Tool wear monitoring; Tool wear modelling; SURFACE-ROUGHNESS; ACOUSTIC-EMISSION; FLANK WEAR; MACHINING PARAMETERS; POWER-CONSUMPTION; COATED CARBIDE; SPEED; OPERATIONS; FORCES; TEMPERATURE;

D O I：

10.1007/s00170-024-14961-2

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This review presents a comprehensive examination of recent advancements in the modelling and monitoring of cutting tool life, emphasizing its critical role in enhancing manufacturing efficiency and cost-effectiveness. The paper discusses the primary wear mechanisms, such as abrasive, adhesive, diffusive, and chemical wear. Traditional and modern predictive models, including Taylor's, Colding's, and Usui's tool life models, are evaluated. The review also covers a range of modelling approaches from empirical to numerical and analytical methods, alongside cutting tool monitoring techniques. The paper concludes by identifying future research directions, hybrid models that combine empirical and analytical techniques, and the creation of comprehensive datasets. The goal is to provide practitioners and researchers with insights into the next wave of innovations in tool life optimization, fostering advancements in adaptive self-learning tool performance predictive systems and integrated monitoring technologies.

引用

页码：3037 / 3076

页数：40