Tool Condition Monitoring in machining for the workpiece surface quality evaluation

Tool Condition Monitoring in machining for the workpiece surface quality evaluation

DEL PRETE Antonio, NYBORG Lars, FRANCHI Rodolfo, PRIMO Teresa

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Abstract. Achieving high surface quality is crucial in manufacturing, impacting product functionality and appearance. Poor quality can lead to defects, friction, and safety risks. Cutting tools endure harsh conditions and wear over time, affecting surface quality and increasing costs. Monitoring tool condition is vital for efficiency, reducing cycle times and downtime. Industries like aerospace and automotive require tight quality control for meeting standards. Historically, manual inspections and scheduled changes were used, but advanced technology now allows more efficient tool condition monitoring. The paper outlines a tool condition monitoring approach using sensors and machine learning to predict and classify tool conditions and workpiece surface quality. It integrates acoustic emission, accelerometer, and thermal infrared camera sensors into a lathe machine. Various machine learning algorithms are trained and validated to accurately predict tool and surface conditions. The most effective model is identified and presented.

Tool Condition Monitoring, Machining, Surface Quality

Published online 4/24/2024, 10 pages
Copyright © 2024 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: DEL PRETE Antonio, NYBORG Lars, FRANCHI Rodolfo, PRIMO Teresa, Tool Condition Monitoring in machining for the workpiece surface quality evaluation, Materials Research Proceedings, Vol. 41, pp 2011-2020, 2024


The article was published as article 222 of the book Material Forming

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[1] Çelik, Y.H., Kilickap, E. & Güney, M. Investigation of cutting parameters affecting on tool wear and surface roughness in dry turning of Ti-6Al-4V using CVD and PVD coated tools. J Braz. Soc. Mech. Sci. Eng. 39, 2085–2093 (2017).
[2] Saini, S., Ahuja, I.S. & Sharma, V.S. Influence of cutting parameters on tool wear and surface roughness in hard turning of AISI H11 tool steel using ceramic tools. Int. J. Precis. Eng. Manuf. 13, 1295–1302 (2012).
[3] H.K. Tönshoff, I. Inasaki, Sensors in Manufacturing, Volume 1, 2001, Wiley‐VCH Verlag GmbH.
[4] M. S. H. Bhuiyan, I. A. Choudhury, Y. Nukman, Tool Condition Monitoring using Acoustic Emission and Vibration Signature in Turning, Proceedings of the World Congress on Engineering 2012 Vol III WCE 2012, July 4 – 6, 2012, London, U.K.
[5] J. Ratava, M. Lohtander, J. Varis, Tool condition monitoring in interrupted cutting with acceleration sensors, Robotics and Computer-Integrated Manufacturing, Volume 47, 2017, Pages 70-75, ISSN 0736-5845,
[6] B. S. Prasad, K. A. Prabha, P.V.S. Ganesh Kumar, Condition monitoring of turning process using infrared thermography technique – An experimental approach, Infrared Physics & Technology, Volume 81, 2017, Pages 137-147, ISSN 1350-4495.
[7] K. Jemielniak, P.J. Arrazola, Application of AE and cutting force signals in tool condition monitoring in micro-milling, CIRP Journal of Manufacturing Science and Technology, Volume 1, Issue 2, 2008, Pages 97-102, ISSN 1755-5817,
[8] G. Serin & B. Sener & A. M. Ozbayoglu & H. O. Unver, Review of tool condition monitoring in machining and opportunities for deep learning, The International Journal of Advanced Manufacturing Technology (2020) 109:953–974.
[9] Krzysztof Jemielniak & Tomasz Urbański & Joanna Kossakowska & Sebastian Bombiński, Tool condition monitoring based on numerous signal features, Int J Adv Manuf Technol (2012) 59:73–81.
[10] Mustafa Kuntoglu, Hacı Saglam, Investigation of signal behaviors for sensor fusion with tool condition monitoring system in turning, Measurement Volume 173, March 2021, 108582.
[11] Mustafa Kuntoglu, Abdullah Aslan, Hacı Saglam, Danil Yurievich Pimenov, Khaled Giasin and Tadeusz Mikolajczyk, Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140, 2020 Sensors.
[12] Pauline Ong, Woon Kiow Lee & Raymond Jit Hoo Lau, Tool condition monitoring in CNC end milling using wavelet neural network based on machine vision, The International Journal of Advanced Manufacturing Technology (2019) 104:1369–1379.
[13] P.G. Benardos, G.-C. Vosniakos, Predicting surface roughness in machining: a review, International Journal of Machine Tools and Manufacture, Volume 43, Issue 8, 2003, Pages 833-844, ISSN 0890-6955.