Knowledge & Skills
Materials Engineering, 20th of July 2022, 020005

Detection and prediction of the surface defect in carbide cutting tools treated by laser using artificial intelligence

Kafayat Hazzan , Manuela Pacella

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Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough, Leicestershire, LE11 3TU, UK

Associate editor: J. C. Outeiro

*Corresponding author: [email protected]

Creative Commons License




Laser processing applied to cemented carbide cutting tools can induce various thermal and mechanical surface defects including porosity, splatter, cracks, balling, spherical pores, voids, and dissociation, which could be detrimental to the integrity of the tool. To limit and control possible post-processing defects, parametric optimization is needed. In this presentation, Hazzan identifies and classifies surface defects in post laser processed carbides to better understand the relationship between parameters and resultant surface integrity. A region convolutional neural network (R-CNN) was trained to identify and classify these surface defects using scanning electron microscopy images (SEM) as inputs. The R-CNN provided a quantitative analysis of each defect with an average accuracy of 91%. Using the data from the R-CNN matched with the laser parameters, a back propagation neural network (BPNN) was trained to act as a predictive network. The network predicts the number and proportion of defects when the tool grain size, roughness and laser parameters are entered. The predictive network has an accuracy of 96.6%. The effect of individual laser parameters on the surface integrity is estimated by this method, enabling the optimization of laser processing in cutting tools. This approach is the first to predict tool performance based on tool’s surface integrity.


Laser Processing,

Cutting Tools,

Tungsten Carbide,

Defects Detection,

Convolutional Neural Networks,

Machine Learning


15 minutes video  


This presentation was given during the 6th CIRP Conference on Surface Integrity and was runner out for the Best Young Scientist Award

(1) Hazzan, K.E. and Pacella, M., Surface defect detection and prediction in carbide cutting tools treated by lasers. Procedia CIRP, 108, pp.851-856 (2022).

(2) Hazzan, K.E., Pacella, M. & See, T.L. Understanding the surface integrity of laser surface engineered tungsten carbide. Int J Adv Manuf Technol 118, 1141–1163 (2022)

(3) Hazzan KE, Pacella M, See TL, Laser Processing of hard and ultra-hard materials for micro-machining and surface engineering applications, Micromachines, 12:895 (2021)

(4) Bergs T, Holst C, Gupta P, Augspurger T. Digital image processing with deep learning for automated cutting tool wear detection. Procedia Manufacturing 48:947–58 (2020).

(5) Zhou X, Liu X, Zhang D, Shen Z, Liu W. Balling phenomena in selective laser melted tungsten. J. Materials Processing Technology 222:33– 42 (2015).

(6) Hazzan KE, Pacella M., A novel laser machining strategy for cutting tool repair, Manufacturing Letters, 32C:87-91 (2022)

(7) Guimares B, Figueiredo D, Fernandes CM, Silva FS, Miranda G, Carvalho O. Laser machining of WC-Co green compacts for cutting tools manufacturing. Int J Refract Met Hard Mater 2019;81:316–24.

Full presentation


K. Hazzan and M. Pacella, 2022, « Detection and prediction of the surface defect in carbide cutting tools treated by laser», Knowledge and Skills, ISSN 2800-2083, Materials Engineering, 020005,

Detection and prediction of the surface defect in carbide cutting tools treated by laser using artificial intelligence
presented by Kafayat Hazzan


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