Journals / Konya Mühendislik Bilimleri Dergisi / 2022 / Cilt: 10 Sayı: 2

OPTIMIZATION OF SURFACE ROUGHNESS OF ALUMINIUM 6013-T6 ALLOY IN THE TURNING PROCESS

Alüminyum 6013-T6 Alaşımlarının Tornalama İşlemlerinde Yüzey Pürüzlülüğünün Optimizasyonu

Pages
337–345
Publication Date
2022-06-01
DOI
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AI Summary & Key Takeaways

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

The analysis results showed that feed rate is a dominant parameter on surface roughness.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:One of the most common methods of machining is turning.
📊
Key Findings & Data:Cutting speed, depth of cut, and feed rate are the most effective cutting parameters on the surface roughness.
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Conclusion & Impact:The analysis results showed that feed rate is a dominant parameter on surface roughness.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Tornalama · Yüzey pürüzlülüğü · Optimizasyon

Abstract

One of the most common methods of machining is turning. Cutting speed, depth of cut, and feed rate are the most effective cutting parameters on the surface roughness. In addition to cutting parameters, the use of cooling type, the cutting tool is also essential on the surface roughness of materials. In this study, the surface roughness properties of Al 6013-T6 material were investigated depending on feed rate and cutting speed in turning process. Experiments were planned according to L9 orthogonal array. Optimum conditions were found via Taguchi’s Signal/Noise analysis. Variance analysis (ANOVA) was performed to determine the parameters that affect the turning process. As a result of experimental studies surface roughness values increased as feed rate increased and decreased as cutting speed increased. The analysis results showed that feed rate is a dominant parameter on surface roughness. It was also observed that the cutting parameters had a significant effect on the machining time. As the machining time decreases, the surface roughness increases.

Özet

Tornalama işlemi, parçaların işlenmesinde en yaygın kullanılan yöntemlerden biridir. Devir hızı, kesme derinliği ve ilerleme hızı, yüzey pürüzlülüğü üzerinde en etkili kesme parametreleridir. Ayrıca kesme parametrelerinin yanında kesici takım özellikleri ve soğutma tipi de yüzey pürüzlülüğü üzerinde etkilidir. Al 6013-T6 malzemesinin tornalama işlemi sonucu yüzey pürüzlülüğü, ilerleme hızına ve devir hızına bağlı olarak incelenmiştir. Adım başına ilerleme arttıkça yüzey pürüzlülüğü artmış ve devir hızı azaldıkça azalmıştır. Çalışmada taguchi deney tasarımı ve varyans analizi (ANOVA) de yapılmıştır. Analiz sonuçlarına göre, yüzey pürüzlülüğü üzerinde ilerleme hızının en baskın parametre olduğu belirlenmiştir. Aynı zamanda kesme parametrelerinin işleme süresi üzerinde önemli bir etkiye sahip olduğu görülmüştür. Işleme süresi azalırken yüzey pürüzlülüğü artmıştır.

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Cite This Article

EKŞİ, S., KARAKAYA, Ç. (2022). OPTIMIZATION OF SURFACE ROUGHNESS OF ALUMINIUM 6013-T6 ALLOY IN THE TURNING PROCESS. Konya Mühendislik Bilimleri Dergisi, Cilt: 10 Sayı: 2, 337–345. https://ajindex.com/articles/938485

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References (12)

  1. Abasa, M., Sayda, L., Akhtara, R., Khalida, Q.S., Khanb, A.M., Pruncu, C.I., 2020, “Optimization of machining parameters of aluminum alloy 6026-T9 under MQL-assisted turning process”, Journal of Materials Research and Technology, 9(5), 10916–10940.
  2. Acayaba, G.M.A., Escalona, P.M., 2015, “Prediction of surface roughness in low speed turning of AISI316 austenitic stainless steel”, CIRP Journal of Manufacturing Science and Technology, 11, 62-67.
  3. Ajay, C.V., Vinoth V., 2021, “Optimization of process parameters in turning of aluminum alloy using response surface methodology”, Materials Today: Proceedings, 46, 9462–9468.
  4. Asilturk, I., Akkus, H., 2011, “Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method”, Measurement, 44, 1697-1704.
  5. Bilgic, H.H., Guvenc, M.A., Cakir, M., Mistikoglu, S., 2019, “A study on prediction of surface roughness and cutting tool temperature after turning for s235jr steel“, Konya Journal of Engineering Sciences, 7, 966-974.
  6. Bindhushree, B.S., Arunadevi, M., Prakash, C.P.S., 2021, “Experimental investigation of process parameters for turning aluminum using ANOVA”, Materials Today: Proceedings, 46, 5060-5066.
  7. Davim, J.P., Figueira, L., 2007, “Machinability evaluation in hard turning of cold work tool steel (D2) with ceramic tools using statistical techniques,” Materials and Design, 28(4), 1186-1191.
  8. Hariharan, M.K., Anderson, A., Kumar, K. R., Senthilkumar, A., 2021, “Optimization of machining parameters in turning of LM6 high silicon aluminium alloy using aluminium titanium nitride and diamond like carbon coated carbide tool,” Materials Today: Proceedings, 45, 5853–5859.
  9. Jeyaprakash, N., Yang, C.H., Kumar, D.R., 2020, “Minimum cutting thickness and surface roughness achieving during micromachining of aluminium 19000 using CNC machine,” Materials Today: Proceedings, 21, 755-761.
  10. Kim, Y.S., Kwon, W.T., 2015, “The effect of cutting parameters for the finest surface roughness during the turning of AISI 1045 with a WC insert”, Journal of Materials Processing Technology, 29(8), 3437-3445.
  11. Kumar, S.P.L., 2019, “Measurement and uncertainty analysis of surface roughness and material removal rate in micro turning operation and process parameters optimization” Measurement, 140, 538-547.
  12. Lalwani, D.I., Mehta, N.K., Jain, P.K., 2008, “Experimental investigations of cutting parameters influence on cutting forces and surface roughness in finish hard turning of MDN250 steel”, Journal of Materials Proce