Optimization of End Mill Geometry for Machining 1.2379 Cold-Work Tool Steel Through Hybrid RSM-ANN-GA Coupled FEA Approach

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Etyemez A., Şirin T. B., Der O., Gökbulut Aydan Ç., Yüksel S.

PROCEEDINGS (MDPI), cilt.14, sa.15, ss.1-46, 2025 (Hakemli Dergi)

• Yayın Türü: Makale / Tam Makale
• Cilt numarası: 14 Sayı: 15
• Basım Tarihi: 2025
• Doi Numarası: 10.3390/machines14010015
• Dergi Adı: PROCEEDINGS (MDPI)
• Derginin Tarandığı İndeksler: Directory of Open Access Journals
• Sayfa Sayıları: ss.1-46
• Marmara Üniversitesi Adresli: Evet

Özet

Abstract

Optimizing end mill geometry is critical for improving performance and reducing costs

in the high-volume manufacturing of tools, dies and molds. This study demonstrates a

successful optimization framework for solid end mills machining 1.2379 cold-work tool

steel, integrating Finite Element Analysis (FEA), Artificial Neural Networks (ANN), and

Genetic Algorithms (GA). The optimized tool geometry, derived from four key design

parameters, delivered substantial performance gains over an industrial reference (parent)

tool. Our ANN-GA model achieved a remarkable predictive accuracy (R = 0.75–0.98) over

the RSM model (R = 0.17–0.63) and identified an optimal design that reduced the resultant

cutting force by approximately 11% (to 142.8 N) and improved surface roughness by 21%

(to 0.1637 μm) compared to experimental baselines. Crucially, the new geometry halved

the tool breakage rate from 50% to ~25%. Parameter analysis revealed the width of the

land as the most influential geometric factor. This work provides a validated, high-performance

tool design and a powerful modeling framework for advancing machining efficiency

in tool, mold and die manufacturing.

Keywords: tool geometry; end mill; RSM; ANOVA; ANN; GA