Dimensional accuracy improvement by parametric optimization in pulsed Nd:YAG laser cutting of Kevlar-29/basalt fiber-reinforced hybrid composites

  • Girish Dutt GautamEmail author
  • Dhananjay R. Mishra
Technical Paper


Laser cutting proves its suitability for cutting of single fiber-reinforced polymer (FRP) composites compared with conventional cutting techniques due to reduced fiber delamination, matrix cracking and fiber pullout. However, the performance of the laser cutting process of hybrid FRP composites is yet to be revealed, which paves the way of the present study. In this article, an experimental investigation of the laser cutting process is carried out on 1.35-mm-thick Kevlar-29 and basalt fiber-reinforced polymer (KBFRP) hybrid composite laminates using 250 W pulsed Nd:YAG laser system. The performance of laser cutting was evaluated by quantifying different kerf quality characteristics such as top and bottom kerf width, top and bottom kerf deviation and kerf taper. These kerf quality characteristics define the geometrical accuracy of the laser cut. Response surface methodology-based Box–Behnken design was adopted for conducting the experiments with varied settings of laser cutting parameters, viz. lamp current, pulse width, pulse frequency, compressed air pressure and cutting speed. Second-order regression models of each response were developed and validated by using standard error plots. A parametric effect analysis was carried out by using the variation of performance measures predicted through developed mathematical models. In order to achieve the optimal levels of the process parameters for all kerf quality characteristics, a self-developed python language-coded TLBO algorithm was used. Finally, the confirmation experiments were performed at obtained optimal levels of laser cutting parameters. An overall improvement of 22.23% in multiple kerf quality characteristics was achieved through optimal settings of laser cutting parameters. The individual improvement of 11.44%, 8.47%, 17.65%, 15.22% and 58.87% was recorded in top kerf width, bottom kerf width, top kerf deviation, bottom kerf deviation and kerf taper, respectively. The developed mathematical models and suggested optimal conditions are able to provide direction to the researchers for obtaining higher-dimensional accuracy in Nd:YAG laser cutting of KBFRP hybrid composites.


Laser beam cutting Hybrid FRP composite Pulsed Nd:YAG laser Kerf quality characteristics Optimization TLBO algorithm 

List of symbols

b0, bii and bij

Regression coefficients


Pulse frequency (Hz)

F value

F test value


Lamp current (A)

K1, K2, …, K6

Replications of kerf width measurement


Result of the best learner for a particular subject


Number of subjects


Number of learners


Mean of results for particular subject ‘j’ (j = 1, 2, 3, …, m)


Compressed air pressure (kg/cm2)

P value

Probability value


Pulse width (ms)


Random number between 0 and 1


Regression coefficient of determination

R2 adj.

Adjusted regression coefficient of determination


Cutting speed (mm/min)

S value

Standard deviation


Teaching factor in the range between 1 and 2

W1, W2, W2, W3, W4 and W5

Assigned weights for functions Z1, Z2, Z3, Z4 and Z5, respectively


Values of laser parameters for ith observation and jth level

Xj, kbest, i

Result of the best learner in subject j


Output response


Normalized combined objective function

Z1, Z2, Z3, Z4 and Z5

Regression models of TKW, BKW, TKD, BKD and KT, respectively

Z1(min), Z2(min), Z3(min), Z4(min) and Z5(min)

Minimum values of TKW, BKW, TKD, BKD and KT, respectively



Analysis of variances


Bottom kerf deviation


Bottom kerf width


Carbon fiber-reinforced polymer


Design of experiments


Fiber-reinforced polymer


Glass fiber-reinforced polymer


Grey relational analysis


Heat-affected zone


Kevlar-29/basalt fiber-reinforced polymer


Kevlar fiber-reinforced polymer


Kerf taper


Laser beam cutting


Neodymium-doped yttrium aluminum garnet


Response surface methodology


Top kerf deviation


Top kerf width


Teaching–learning-based optimization algorithm



The authors sincerely express their heartiest thanks to Dr B.N. Upadhayay, SOF, Solid State Division of RRCAT (Raja Ramanna Centre for Advanced Technology), Indore (MP), for providing the experimental support for this work. The authors are also grateful to the management of Jaypee University of Engineering and Technology, Guna (MP), India, for their laboratory and financial assistance to carry out this research work.


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Copyright information

© The Brazilian Society of Mechanical Sciences and Engineering 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringJaypee University of Engineering & TechnologyGunaIndia

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