Drilling performance and hole quality optimization of h-BNNS-reinforced Al-nanocomposites using NSGA-II and TLBO algorithm
- 1Department of Mechanical and Aerospace Engineering, Institute of Infrastructure Technology Research and Management
- 2Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management
- 3Department of Mechanical Engineering, Manipal University Jaipur
Abstract
In the present research, drilling operations were performed on h-BNNS-reinforced Al-6082 nanocomposites to investigate the influence of key drilling parameters rotational speed (RS), feed rate (f), and reinforcement content of h-BNNS (Rein) on hole quality characteristics, including circularity error (CE), cylindricity (Cy), and conicity (Ct). The nanocomposites were fabricated using the stir casting method, and a total of 20 experiments were designed and executed using a Rotatable Central Composite Design (CCD). A full quadratic model was developed, and Analysis of Variance (ANOVA) was employed to evaluate model adequacy. The results showed that CE initially decreased with increasing RS and feed rate up to mid-levels, followed by an increase, with feed having a more pronounced effect. Cylindricity increased with all parameters up to a point, then decreased to a minimum. The three hole quality parameters were simultaneously optimized using NSGA-II and TLBO algorithms for realizing pareto optimal solutions. Both algorithms showed considerable efficiency in predicting optimal drilling conditions for improved hole characteristics such as RS = 0.205, Feed = − 0.922, and Rein = 1.630, yielding CE = 0.1435, Ct = − 0.0070, and Cy = 0.9245. The percentage deviations between experimental and predicted values were found to be 9.45%, 22.22%, and 7.94% for NSGA-II, and 4.08%, 15.09%, and 5.07% for TLBO in terms of CE, Cy, and Ct, respectively.
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BibTeX
@article{dikshit2026drilling,
title = {{Drilling performance and hole quality optimization of h-BNNS-reinforced Al-nanocomposites using NSGA-II and TLBO algorithm}},
author = {Mithilesh K. Dikshit and Halakarnimath Prasanna Totayya and Pihu Saharan and Pushpam Kunal and Vimal Kumar Pathak},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-61514-6},
url = {https://doi.org/10.1038/s41598-026-61514-6},
note = {CC BY},
}
RIS
TY - JOUR
TI - Drilling performance and hole quality optimization of h-BNNS-reinforced Al-nanocomposites using NSGA-II and TLBO algorithm
AU - Mithilesh K. Dikshit
AU - Halakarnimath Prasanna Totayya
AU - Pihu Saharan
AU - Pushpam Kunal
AU - Vimal Kumar Pathak
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-61514-6
UR - https://doi.org/10.1038/s41598-026-61514-6
AB - In the present research, drilling operations were performed on h-BNNS-reinforced Al-6082 nanocomposites to investigate the influence of key drilling parameters rotational speed (RS), feed rate (f), and reinforcement content of h-BNNS (Rein) on hole quality characteristics, including circularity error (CE), cylindricity (Cy), and conicity (Ct). The nanocomposites were fabricated using the stir casting method, and a total of 20 experiments were designed and executed using a Rotatable Central Composite Design (CCD). A full quadratic model was developed, and Analysis of Variance (ANOVA) was employed to evaluate model adequacy. The results showed that CE initially decreased with increasing RS and feed rate up to mid-levels, followed by an increase, with feed having a more pronounced effect. Cylindricity increased with all parameters up to a point, then decreased to a minimum. The three hole quality parameters were simultaneously optimized using NSGA-II and TLBO algorithms for realizing pareto optimal solutions. Both algorithms showed considerable efficiency in predicting optimal drilling conditions for improved hole characteristics such as RS = 0.205, Feed = − 0.922, and Rein = 1.630, yielding CE = 0.1435, Ct = − 0.0070, and Cy = 0.9245. The percentage deviations between experimental and predicted values were found to be 9.45%, 22.22%, and 7.94% for NSGA-II, and 4.08%, 15.09%, and 5.07% for TLBO in terms of CE, Cy, and Ct, respectively.
SN - 2045-2322
ER -