Optimization of drilling parameters for minimizing delamination factor in biocomposites using multiple nonlinear neuro-regression and stochastic methods

Abstract

In this study, a novel design optimization strategy is proposed to enhance the drilling performance of HDPE/Washingtonia fiber biocomposites, considering operational parameters such as drill diameter (d), feed rate (f), and spindle speed (N). A detailed investigation utilizing multiple nonlinear neuro-regression analyses is conducted to predict the delamination factor (Fd), based on a dataset obtained from a literature study. 14 candidate mathematical functions are suggested for modelling, and their accuracy is assessed through R2training, R2testing, and R2validation metrics, and boundedness check. The identified effective models are subsequently employed in optimization using modified version of four methods: Nelder-Mead (NM), Simulated Annealing (SA), Random Search (RS), and Differential Evaluation (DE). The optimum values for parameters f, N and d were determined to be 50 mm/min, 355 rev/min, and 5.00 mm respectively, resulting with the minimum Fd values as 1.11615.

Keywords

• Drilling
• Biocomposites
• Neuro regression
• Stochastic optimization
• Delamination factor

Çoklu doğrusal olmayan nöro-regresyon ve stokastik yöntemler kullanılarak biyokompozitlerde delaminasyon faktörünü en aza indirmek için delme parametrelerinin optimizasyonu

Abstract

Bu çalışmada, matkap çapı (d), ilerleme hızı (f) ve mil hızı (N) gibi operasyonel parametreler göz önünde bulundurularak HDPE/Washingtonia elyaf biyokompozitlerinin delme performansını artırmak için yeni bir tasarım optimizasyon stratejisi önerilmiştir. Bir literatür çalışmasından elde edilen veri setine dayanarak delaminasyon faktörünü (Fd) tahmin etmek için çoklu doğrusal olmayan nöro-regresyon analizlerini kullanan ayrıntılı bir araştırma yürütülmüştür. Modelleme için 14 aday matematiksel fonksiyon önerilmiş ve doğrulukları R2training, R2testing ve R2validation metrikleri ve sınırlılık kontrolü ile değerlendirilmiştir. Delaminasyon faktörünü tanımlamada etkili modeller daha sonra Nelder-Mead (NM), Simulated Annealing (SA), Random Search (RS) ve Differential Evaluation (DE) metotlarının değiştirilmiş versiyonları kullanılarak optimize edilmiştir. Parametreler f, N ve d için optimum değerler sırasıyla 50 mm/dak, 355 dev/dak ve 5,00 mm olarak belirlenmiş ve minimum Fd değerleri 1,11615 olarak elde edilmiştir.

Keywords

• Biyokompozitler
• Delme prosesi
• Deleminasyon faktörü
• Nöro regresyon
• Stokastik optimizasyon

References

1. Lotfi, A., Li, H., & Dao, D. V. (2019). Effect of drilling parameters on delamination and hole quality in drilling flax fiber reinforced bio-composites. In Sustainable Design and Manufacturing 2018: Proceedings of the 5th International Conference on Sustainable Design and Manufacturing (KES-SDM-18) 5 (pp. 71-81)
2. Bharath KN, Basavarajappa S (2016) Applications of biocomposite materials based on natural fibers from renewable resources: a review. Sci Eng Compos Mater 23(2):123–133. https://doi.org/10.1515/secm-2014-0088
3. Nassar MMA, Alzebdeh KI, Alsafy MMM, Piya S (2023) Optimizing drilling parameters for minimizing delamination in polypropylene-date palm fiber bio-composite materials. J Braz Soc Mech Sci Eng 45(11). https://doi.org/10.1007/s40430-023-04528-9
4. Belaadi A, Boumaaza M, Amroune S, Bourchak M (2020) Mechanical characterization and optimization of delamination factor in drilling bidirectional jute fibre-reinforced polymer biocomposites. Int J Adv Manuf Technol 111(7):2073–2094. https://doi.org/10.1007/s00170-020-06217-6
5. Benyettou R, Amroune S, Slamani M, Kılıç A (2023) Investigation of machinability of biocomposites: modeling and ANN optimization. Acad J Manuf Eng 21(1).
6. Geng D, Liu Y, Shao Z, Lu Z, Cai J, Li X, Zhang D (2019) Delamination formation, evaluation and suppression during drilling of composite laminates: a review. Compos Struct 216:168–186. https://doi.org/10.1016/j.compstruct.2019.02.099
7. AL-Oqla FM (2023) Manufacturing and delamination factor optimization of cellulosic paper/epoxy composites towards proper design for sustainability. Int J Interact Des Manuf 17(2):765–773. https://doi.org/10.1007/s12008-022-00980-4
8. Adda B, Belaadi A, Boumaaza M, Bourchak M (2021) Experimental investigation and optimization of delamination factors in the drilling of jute fiber–reinforced polymer biocomposites with multiple estimators. Int J Adv Manuf Technol 116(9):2885–2907. https://doi.org/10.1007/s00170-021-07628-9

9. Belaadi A, Boumaaza M, Alshahrani H, Bourchak M, Jawaid M (2022) Drilling performance prediction of HDPE/Washingtonia fiber biocomposite using RSM, ANN, and GA optimization. Int J Adv Manuf Technol 123(5–6):1543–1564. https://doi.org/10.1007/s00170-022-10248-6
10. Tsao CC, Hocheng H (2004) Taguchi analysis of delamination associated with various drill bits in drilling of composite material. Int J Mach Tools Manuf 44(10):1085–1090. https://doi.org/10.1016/j.ijmachtools.2004.02.019
11. Ameur MF, Habak M, Kenane M, Aouici H, Cheikh M (2017) Machinability analysis of dry drilling of carbon/epoxy composites: cases of exit delamination and cylindricity error. Int J Adv Manuf Technol 88(9–12):2557–2571. https://doi.org/10.1007/s00170-016-8967-8
12. Wang Q, Jia X (2021) Optimization of cutting parameters for improving exit delamination, surface roughness, and production rate in drilling of CFRP composites. Int J Adv Manuf Technol 117(11):3487–3502. https://doi.org/10.1007/s00170-021-07918-2
13. Başar G, Kırlı Akın H, Kahraman F (2020) Modelling and analysis of thrust force in drilling of nanocomposites using response surface methodology. Gazi Univ J Sci Part C Des Technol 8(2):293–305. https://doi.org/10.29109/gujsc.648188
14. Fedai Y, Basar G, Kirli Akin H (2022) Multi-response optimization in drilling of MWCNTs reinforced GFRP using grey relational analysis. Tech Vjesnik 29(3):742–751
15. Der O, Ordu M, Basar G (2024) Optimization of cutting parameters in manufacturing of polymeric materials for flexible two-phase thermal management systems. Mater Test 66(10):1700–1719
16. Kaushik D, Singh I (2024) Analysis of drilling behavior of flax/PP composites. Mater Manuf Process 39(8):1061–1073
17. Aydin L, Oterkus S, Artem HS (2020) Designing engineering structures using stochastic optimization methods. CRC Press, Boca Raton. https://doi.org/10.1201/9780429289576
18. Polatoğlu İ, Aydın L, Nevruz BÇ, Özer S (2020) A novel approach for the optimal design of a biosensor. Anal Lett 53(9):1428–1445. https://doi.org/10.1080/00032719.2019.1709075
19. Aydin L, Artem HS (2011) Comparison of stochastic search optimization algorithms for the laminated composites under mechanical and hygrothermal loadings. J Reinf Plast Compos 30(14):1197–1212. https://doi.org/10.1177/0731684411415138
20. Savran M, Aydin L, Ayaz A, Uslu T (2024) A new strategy for manufacturing, modeling, and optimization of 3D printed polylactide based on multiple nonlinear neuro regression analysis and stochastic optimization methods. Proc Inst Mech Eng Part E J Process Mech Eng. https://doi.org/10.1177/09544089241272909
21. Savran M, Aydin L (2023) Natural frequency and buckling optimization considering weight saving for hybrid graphite/epoxy-sitka spruce and graphite-flax/epoxy laminated composite plates using stochastic methods. Mech Adv Mater Struct 30(13):2637–2650. https://doi.org/10.1080/15376494.2022.2061656
22. Savran M, Sayi H, Aydin L (2020) Mathematica and optimization. In: Designing engineering structures using stochastic optimization methods, 1st Edition, CRC Press, Boca Raton, pp 24–43.