Yumuşak çelikte malzeme kaldırma süreçlerinin analizi: iş mili hızı, ilerleme hızı ve kesme derinliğinin işleme performansı üzerindeki etkisi

Yıl 2025, Cilt: 2 Sayı: 1, 17 - 24, 26.06.2025

Dıckson Davıd Olodu , Andrew Erameh , Osagie Imevbore Ihenyen

Öz

Bu çalışma, yumuşak çelik işleme sırasında iş mili hızı, ilerleme hızı ve talaş derinliğinin işleme performansı üzerindeki etkilerini incelemektedir. Deneyler, 105-225 rpm aralığında iş mili hızı, 0.10-0.34 mm/dev ilerleme hızı ve 1.0-3.4 mm talaş derinliği değerleriyle gerçekleştirilmiştir. Sonuçlar, bu parametrelerin artırılmasının malzeme kaldırma oranını (MRR) iyileştirdiğini göstermiştir; en yüksek MRR değeri 261.30 mm³/dak olarak kaydedilmiştir. Ancak, takım aşınma oranı da orantılı olarak artmış ve maksimum 7.65 mm³/dak değerine ulaşarak, yüksek verimlilik ile takım ömrü arasında bir denge olduğunu ortaya koymuştur. İşleme süresi ise artan iş mili hızıyla azalmış ve en kısa süre 1.35 dakika olarak ölçülmüştür. Eşleştirilmiş örneklem t-testi ve korelasyon katsayıları kullanılarak yapılan istatistiksel analizler, iş mili hızı ile MRR arasında güçlü pozitif (r=0.973), iş mili hızı ile takım aşınma oranı arasında güçlü pozitif (r=0.994) ve iş mili hızı ile işleme süresi arasında güçlü negatif (r=-0.935) korelasyonlar ortaya koymuştur. Bu bulgular, üretkenlik, takım aşınması ve verimlilik arasında denge sağlamak için kesme parametrelerinin optimize edilmesinin önemini vurgulamaktadır. Çalışma, endüstriyel yumuşak çelik işleme uygulamaları için değerli bilgiler sunmakta ve benzer koşullarda gelişmiş soğutma yöntemleri ile takım malzemelerinin etkisinin araştırılmasını önermektedir. Bu sonuçlar, hassas üretim süreçlerinin ve kaynak optimizasyonunun geliştirilmesine katkı sağlar.

Anahtar Kelimeler

Kesme parametreleri , Talaş derinliği , İlerleme hızı , Malzeme kaldırma oranı (MRR) , İş mili hızı , Takım aşınma oranı

Influence of spindle speed, feed rate, and depth of cut on machining performance: an analysis of material removal processes in mild steel

Öz

This study examines the effects of spindle speed, feed rate, and depth of cut on machining performance during mild steel machining. Experiments were conducted with spindle speeds ranging from 105-225 rpm, feed rates of 0.10-0.34 mm/rev, and depths of cut between 1.0-3.4 mm. Results showed that increasing these parameters improved the material removal rate (MRR), with the highest MRR reaching 261.30 mm³/min. However, tool wear rate also rose proportionally, peaking at 7.65 mm³/min, highlighting a trade-off between productivity and tool durability. Machining time decreased with higher spindle speeds, with the shortest time recorded at 1.35 minutes. Statistical analysis using paired sample t-tests and correlation coefficients revealed strong positive correlations between spindle speed and MRR (r=0.973), spindle speed and tool wear rate (r=0.994), and a strong negative correlation between spindle speed and machining time (r=-0.935). These findings highlight the importance of optimizing cutting parameters to balance productivity, tool wear, and efficiency. The study provides practical insights for industrial machining of mild steel and recommends future exploration into advanced cooling methods and tool materials to enhance machining performance under similar conditions, contributing to improved precision and resource efficiency.

Anahtar Kelimeler

Cutting parameters , Depth of cut , Feed rate , Material removal rate (MRR) , Spindle speed , Tool wear rate

Kaynakça

• Acevedo, C., Penaloza., Guillermo, E., Valencia., Milton, F., Coba, Salcedo. (2018). A comparative study of the cutting parameter in milling process under different material, Contemporary Engineering Sciences, 11(41): 2001-2008.
• Ankit, S., Anoop, S., Kamaljeet, S., Abhishek, S., Amrinder, S., Uppal. (2024). Parametric optimization for material removal rate during face milling: using experimental and mathematical modelling approach, Lecture Notes in Mechanical Engineering, 1(1): 25-38.
• Bharat, S. (2020). Effect of parameters like spindle speed, depth of cut, and feed rate on the cutting force of a single point cutting tool, International Journal of Engineering and Advanced Science Technology, 4(12): 94-101.
• B., Krishna, Murthy. (2018). Prediction of process parameters for optimal material removal rate using artificial neural network (ANN) technique. International Journal for Research in Applied Science and Engineering Technology, 6(1): 237-242.
• Fnides, M. (2023). Optimization and mathematical modelling of surface roughness criteria and material removal rate when milling C45 steel using rsm and desirability approach, Journal of Mechanical Engineering, 20(3): 45-62.
• Fredrick, J., Otim., Seong, J., Choi. (2015). Influence of cutting parameters on energy consumption and material removal rate in turning process, Applied Mechanics and Materials, 799: 282-287.
• Imhade, P., Okokpujie., Lagouge, K., Tartibu. (2023). Material removal rate optimization under ANN and QRCCD, Advances in Manufacturing Engineering, 1(1): 215-227.
• Irawan, M., Azharuddin, A., Slamet, R. (2019). Pengaruh spindle speed, feed rate, dan depth of cut terhadap akurasi hasil permesinan pada mesin cnc router 3 sumbu, Indonesian Journal of Mechanical Engineering, 5(2): 67-75.
• Ival, I., Zaldy, K., Erwanto, E. (2021). Analisis material removal rate pada proses cnc milling terhadap material AISI 1045, Jurnal Teknik Mesin Indonesia, 9(1): 55-62.
• Jaiganesh, V., Yokesh, B., Kumar, P., Sevvel, A.J., Balaji. (2017). Optimization of process parameters on commercial mild steel using taguchi technique, International Journal of Engineering and Technology, 7(1): 30-37.
• János, K., Viktor, M., István, D. (2018). Comparative analysis of machining procedures,. Machines, 6(2): 13-21.
• Karlapudi, G., Gopi, N. (2017). Optimization of cylindrical grinding process parameters on material removal rate of En21am steel, International Journal of Research, 6(7): 48-53.
• Krisko, G., Aziz, A. (2019). Effect of cutting speed, feed-rate, and depth of cut on the surface roughness level of ST-37 steel in shaping process, Indonesian Journal of Engineering Technology, 2(2): 25-31.
• Latif, A.A., Ibrahim, M., Amran, A.Z., Erween, AB. (2017). A study on the effect of feed rate and cutting speed on surface roughness and material removal rate of mild steel, IOP Conference Series: Materials Science and Engineering, 257(1): 012025.
• Madan, V., Suparmaniam, L., Ahmad, R., Yusoff. (2016). Investigation of surface roughness and tool wear length with varying combination of depth of cut and feed rate, IOP Conference Series: Materials Science and Engineering, 114(1): 012010.
• Mohapatra, C.R., Nayak, N.C. (2015). Modeling and analysis of effect of cutting parameters on product quality in dry turning operation of mild steel using carbide & high speed steel tool, International Journal of Engineering Research and Technology, 4(4): 189-193.
• Mohd, R., Ibrahim., Tharmaraj, S., Nurul, A., Fadhlul, H., Mohammad, S., Mustapa., Ahmad, E., Ismail., Mohd, F., Hassan. (2017). The effect of cutting speed and feed rate on surface roughness and tool wear when machining D2 steel, Materials Science Forum, 909: 80-86.
• Noor, A., Mohd-Lair., M., F., I., Mohd-Shahdun., A., Mohd-Tahir., J., Paulus-Dua. (2019). The effects of spindle speed and feed rate on hole quality in drilling operation, Mechanical Engineering Research, 5(1): 187-189.
• Obojoh, E., Francis-Akilaki, T. (2024). Effect of some cutting parameters on surface finish of bright mild steel, Journal of Applied Sciences and Environmental Management, 28(4): 217-225.
• Phan, V., Nghi. (2024). An experimental investigation of cutting condition influences on hard milling of AISI D2 tool steel, Engineering and Technology Journal, 9(5): 312-319.
• Ritesh, K., Singh. (2018). Concurrent optimization and an experimental analysis of face milling operation parameters for optimal performance on mild steel, International Journal for Research in Applied Science and Engineering Technology, 6(2): 91-96.
• Yıldız, S., Yıldız, A. (2022). Investigation of the effects of different parameters on the performance of a solar air heater with artificial roughness, Pamukkale University Journal of Engineering Sciences, 28(5): 1011–1019.
• Lalor, N., Petrosino, A. (2002). A comparison of the performance of several time-frequency representations for the detection of transient signals, Journal of Sound and Vibration, 246(3): 467–487.
• Baraniuk, R. G., Jones, D. L. (2003). A signal-dependent time-frequency representation: Optimal kernel design, Journal of Sound and Vibration, 267(3): 603–627.
• Olodu, D. D. (2018). Optimization and analysis of cutting tool geometrical parameters using Taguchi method, Journal of Applied Sciences and Environmental Management, 22(3): 346–349.
• Olodu, D. D. (2021). Modelling and validation of the production parameters of unalloyed aluminium sheets, Gazi University Journal of Science Part A: Engineering and Innovation, 8(1): 94-108.
• Olodu, D. D., Okagbare, G. (2021). Modelling and experimental investigation of copper-zinc alloy using split-split plot design, International Journal of Engineering and Innovative Research, 3(3): 175-186.
• Olodu, D. D., Erameh, A. (2023). Optimization of the effects of process parameters on the tensile strength of developed aluminium roofing sheets using taguchi method, The International Journal of Materials and Engineering Technology, 6(2): 31-40.
• Akkuş, Harun, Yaka, Harun. (2018). Optimization of turning process by using taguchi method, Sakarya University Journal of Science, 22(5): 1444–1448.
• Yaka, Harun, Akkuş, Harun, Uğur, Levent. (2016). AISI 1040 çeliğininin tornalamasinda kesme parametrelerinin yüzey pürüzlülüğüne etkisinin taguchi metodu ile optimizasyonu, Celal Bayar University Journal of Science, 12(2).
• Somatkar, A. A., Dwivedi, R., Chinchanikar, S. S. (2024). Enhancing surface integrity and quality through roller burnishing: A comprehensive review of parameters optimization and applications, Communications on Applied Nonlinear Analysis, 31(1s): 151–169.
• Dwivedi, R., Somatkar, A. A., Chinchanikar, S. S. (2024). Modeling and optimization of roller burnishing of Al6061-T6 process for minimum surface roughness, better microhardness, and roundness, Obrabotka Metallov (Tekhnologiya, Oborudovanie, Instrumenty) = Metal Working and Material Science, 26(3): 52–65.