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Journals / Yenilikçi Mühendislik ve Doğa Bilimleri / 2026 / Volume: 5 Issue: 1

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

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

Melih Savran(İZMİR KATİP ÇELEBİ ÜNİVERSİTESİ)0000-0001-8343-1073
Mücahit Osman Türkan(İZMİR KATİP ÇELEBİ ÜNİVERSİTESİ)0009-0002-9056-4012
Mustafa Öncül(İZMİR KATİP ÇELEBİ ÜNİVERSİTESİ)0000-0002-4441-6353
Levent Aydın(İZMİR KATİP ÇELEBİ ÜNİVERSİTESİ)0000-0003-0483-0071
Pages
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Publication Date
2025-01-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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).
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Key Findings & Data: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.
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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Drilling · Biocomposites · Neuro regression

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.

Özet

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.

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Cite This Article

Savran, M., Türkan, M. O., Öncül, M., Aydın, L. (2025). Optimization of drilling parameters for minimizing delamination factor in biocomposites using multiple nonlinear neuro-regression and stochastic methods. Yenilikçi Mühendislik ve Doğa Bilimleri, Volume: 5 Issue: 1. https://doi.org/10.61112/jiens.1564284

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