Revisiting machinability assessment: Towards total machining performance

I S Jawahir; Helmi Attia; Martin Dix; Hassan Ghadbeigi; Zhirong Liao; Julius Schoop; Alborz Shokrani

doi:10.1016/j.cirp.2025.05.003

Revisiting machinability assessment: Towards total machining performance

I S Jawahir, Helmi Attia, Martin Dix, Hassan Ghadbeigi, Zhirong Liao, Julius Schoop, Alborz Shokrani

Research output: Contribution to journal › Article › peer-review

2 Citations (SciVal)

Abstract

The term “machinability”, introduced over hundred years ago, is vague and cannot fully describe the performance of machining systems. Machinability databases established over many decades are outdated: missing recent advances, e.g., cutting tool grades, geometry, coatings, and cutting fluids effects. This keynote paper summarizes findings of a CIRP-sponsored three-year collaborative study in five interrelated topics. The paper presents a critical review of the state-of-the-art on these topics, the results of two major round robin tests, three industry-based case studies, and a novel predictive system of machining performance, utilizing advanced deep learning methods. Outlook and future directions are also presented.

Original language	English
Number of pages	27
Journal	CIRP Annals - Manufacturing Technology
Early online date	3 Jun 2025
DOIs	https://doi.org/10.1016/j.cirp.2025.05.003
Publication status	E-pub ahead of print - 3 Jun 2025

Acknowledgements

The authors gratefully acknowledge the contributions and support provided by the 62 participants of the STCsingle bondC Cooperative Working Group (CWG), IMPACT during the 2021–24 period. The authors also express their sincere thanks to STCsingle bondC officers, Professors Shreyes Melkote, Pedro Arrazola and Volker Schulze who continued to provide strong support and encouraged the IMPACT topic leaders and co-leaders to present the progress of their ongoing work in all STC-meetings during the last three years. Special thanks to all contributors and participants of two major round robin tests and industry case studies.

Keywords

Cutting tool
Machinability
Modeling

ASJC Scopus subject areas

Mechanical Engineering
Industrial and Manufacturing Engineering

Access to Document

10.1016/j.cirp.2025.05.003

Cite this

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title = "Revisiting machinability assessment: Towards total machining performance",

abstract = "The term “machinability”, introduced over hundred years ago, is vague and cannot fully describe the performance of machining systems. Machinability databases established over many decades are outdated: missing recent advances, e.g., cutting tool grades, geometry, coatings, and cutting fluids effects. This keynote paper summarizes findings of a CIRP-sponsored three-year collaborative study in five interrelated topics. The paper presents a critical review of the state-of-the-art on these topics, the results of two major round robin tests, three industry-based case studies, and a novel predictive system of machining performance, utilizing advanced deep learning methods. Outlook and future directions are also presented.",

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AU - Ghadbeigi, Hassan

AU - Liao, Zhirong

AU - Schoop, Julius

AU - Shokrani, Alborz

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AB - The term “machinability”, introduced over hundred years ago, is vague and cannot fully describe the performance of machining systems. Machinability databases established over many decades are outdated: missing recent advances, e.g., cutting tool grades, geometry, coatings, and cutting fluids effects. This keynote paper summarizes findings of a CIRP-sponsored three-year collaborative study in five interrelated topics. The paper presents a critical review of the state-of-the-art on these topics, the results of two major round robin tests, three industry-based case studies, and a novel predictive system of machining performance, utilizing advanced deep learning methods. Outlook and future directions are also presented.

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KW - Modeling

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Revisiting machinability assessment: Towards total machining performance

Abstract

Acknowledgements

Keywords

ASJC Scopus subject areas

Access to Document

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SENSYCUT- Sensor Enabled Systems for Precision Cutting

Cite this

Revisiting machinability assessment: Towards total machining performance

Abstract

Acknowledgements

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Embargoed Document

Fingerprint

Projects

SENSYCUT- Sensor Enabled Systems for Precision Cutting

Cite this