Araştırma Makalesi
BibTex RIS Kaynak Göster

KAPLAMALI VE KAPLAMASIZ KESİCİ TAKIMLARLA İŞLENEN INCONEL 718 İŞ PARÇASININ YÜZEY PÜRÜZLÜLÜK DEĞERLERİNİN ANFIS İLE MODELLENMESİ

Yıl 2025, Cilt: 28 Sayı: 1, 369 - 379, 03.03.2025

Hüseyin Gürbüz , Şehmus Baday

Öz

Bu çalışmanın amacı, farklı işleme parametreleri kullanarak kaplamalı ve kaplamasız kesici takımlarla Inconel 718 süper alaşımının frezelenmesi sonucu oluşan yüzey pürüzlülük değerlerini incelemek ve deneysel sonuçların tahmini için Adaptif Sinir Ağına Dayalı Bulanık Çıkarım Sistemi (ANFIS) kullanarak bir model geliştirmektir. ANFIS modelinde, giriş parametreleri olarak kesici takım türü (kaplamalı ve kaplamasız), ilerleme hızı f (mm/diş) ve kesme hızı V (m/dak), çıkış parametresi olarak ise ortalama yüzey pürüzlülüğü Ra (μm) kullanılmıştır. Oluşturulan modelde, deneysel verilerin sırasıyla %70’i , %15’i ve %15’i eğitim, test verileri ve doğrulama verileri olarak girilmiştir. En uygun ANFIS modelinin belirlenmesinde giriş üyelik fonksiyonu ve bunların sayısı tek tek denenerek en düşük hata oranına sahip model seçilmiştir. En düşük hata oranına sahip model için çıkış üyelik fonksiyonu, üyelik fonksiyonu ve sayısı sırasıyla lineer, Gauss2mf ve 333 olarak belirlenmiştir. Deneysel sonuçlar ile ANFIS modelinin tahmin sonuçları karşılaştırıldığında, hata oranı değeri 0,069596 ve belirlilik katsayısı (R2) değeri ise 0,9902 hesaplanmıştır. Elde edilen sonuçlara bağlı olarak ANFIS modelinin Inconel 718 frezeleme işleminde yüzey pürüzlülük sonuçlarını tahmin edilmesinde başarılı bir yöntem olabileceği gösterilmiştir.

Anahtar Kelimeler

Frezeleme Inconel 718 kaplamalı ve kaplamasız kesici takımlar yüzey pürüzlülüğü ANFIS

Destekleyen Kurum

Batman Üniversitesi Bilimsel Araştırma Projeleri Birimi (BTÜBAP)

Proje Numarası

18.004

Teşekkür

Bu çalışmada, Batman Üniversitesi Bilimsel Araştırma Projeleri Birimi (BTÜBAP) tarafından “18.004” numaralı projeye sunmuş olduğu finansal destek için BTÜBAP'a teşekkür ederiz.

Kaynakça

  • Abdulshahed, A., & Badi, I. (2018). Prediction and control of the surface roughness for the end milling process using ANFIS. Operational Research in Engineering Sciences: Theory and Applications, 1(1), 1-12. https://doi.org/10.31181/oresta1901201011a
  • Asal, Ö., Dilipak, H., Yalçınkaya, A., & Ünal, Ş. (2021). Minimum Miktarda Yağlama Tekniği ile Frezeleme İşleminde Yüzey Pürüzlülüğünün Anfis ile Modellenmesi. International Journal of Innovative Engineering Applications, 5(2), 162-170. https://doi.org/10.46460/ijiea.952306
  • Cakir, M. V., Eyercioglu, O., Gov, K., Sahin, M., & Cakir, S. H. (2013). Comparison of soft computing techniques for modelling of the EDM performance parameters. Advances in Mechanical Engineering, 1-15. https://doi.org/10.1155/2013/392531
  • Çelik, A., Alağaç, M. S., Turan, S., Kara, A., & Kara, F. (2017). Wear behavior of solid SiAlON milling tools during high speed milling of Inconel 718. Wear, 378, 58-67. https://doi.org/10.1016/j.wear.2017.02.025
  • Dedeakayoğulları, H., Kaçal, A., & Keser, K. (2022). Modeling and prediction of surface roughness at the drilling of SLM-Ti6Al4V parts manufactured with pre-hole with optimized ANN and ANFIS. Measurement, 203, 112029. https://doi.org/10.1016/j.measurement.2022.112029
  • Dere, M., & Filiz, I. H. (2019). Experimental investigation of the effects of workpiece diameter and overhang length on the surface roughness in turning of free machining steel and modelling of surface roughness by using ANFIS. Journal of the Faculty of Engineering and Architecture of Gazi University, 34(2), 676-686. https://doi.org/10.17341/gazimmfd.416524
  • Fedai, Y., Ünüvar, A., Akın, H. K., & Başar, G. (2019). 316L Paslanmaz çeliklerin frezeleme işlemindeki yüzey pürüzlülüğün ANFIS ile modellenmesi. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 7(2), 98-110. https://doi.org/10.29130/dubited.466629
  • Gürbüz, H., & Baday, Ş. (2021). Milling Inconel 718 workpiece with cryogenically treated and untreated cutting tools. The International Journal of Advanced Manufacturing Technology, 116, 3135-3148. https://doi.org/10.1007/s00170-021-07688-x
  • Halim, N. H. A., Haron, C. H. C., Ghani, J. A., & Azhar, M. F. (2019). Tool wear and chip morphology in high-speed milling of hardened Inconel 718 under dry and cryogenic CO2 conditions. Wear, 426, 1683-1690. https://doi.org/10.1016/j.wear.2019.01.095
  • Hegab, H., Salem, A., Rahnamayan, S., & Kishawy, H. A. (2021). Analysis, modeling, and multi-objective optimization of machining Inconel 718 with nano-additives based minimum quantity coolant. Applied Soft Computing, 108, 107416. https://doi.org/10.1016/j.asoc.2021.107416
  • Jithendra, T., Basha, S. S., Divya, A., & Rajyalakshmi, G. (2024). Machine learning technique ANFIS-COA for enhancing micro-milling performance by investigating the surface roughness and material removal rate. International Journal on Interactive Design and Manufacturing, 1-22. https://doi.org/10.1007/s12008-024-02061-0
  • Kannan, S., & Kui, L. (2019). Experimental investigation of surface integrity during abrasive edge profiling of nickel-based alloy. Journal of Manufacturing Processes, 39, 40-51. https://doi.org/10.1016/j.jmapro.2019.01.052
  • Kasim, M. S., Hafiz, M. S. A., Ghani, J. A., Haron, C. H. C., Izamshah, R., Sundi, S. A., & Othman, I. S. (2019). Investigation of surface topology in ball nose end milling process of Inconel 718. Wear, 426, 1318-1326. https://doi.org/10.1016/j.wear.2018.12.076
  • Luo, M., Luo, H., Zhang, D., & Tang, K. (2018). Improving tool life in multi-axis milling of Ni-based superalloy with ball-end cutter based on the active cutting edge shift strategy. Journal of Materials Processing Technology, 252, 105-115. https://doi.org/10.1016/j.jmatprotec.2017.09.010
  • Ma, J. W., Wang, F. J., Jia, Z. Y., Xu, Q., & Yang, Y. Y. (2014). Study of machining parameter optimization in high speed milling of Inconel 718 curved surface based on cutting force. The International Journal of Advanced Manufacturing Technology, 75(1-4), 269-277. https://doi.org/10.1007/s00170-014-6115-x
  • Maher, I., Eltaib, M. E. H., Sarhan, A. A., & El-Zahry, R. M. (2014). Investigation of the effect of machining parameters on the surface quality of machined brass (60/40) in CNC end milling - ANFIS modeling. The International Journal of Advanced Manufacturing Technology, 74, 531-537. https://doi.org/10.1007/s00170-014-6016-z
  • Nath, C., Brooks, Z., & Kurfess, T. R. (2015). Machinability study and process optimization in face milling of some super alloys with indexable copy face mill inserts. Journal of Manufacturing Processes, 20, 88-97. https://doi.org/10.1016/j.jmapro.2015.09.006
  • Pandea, P. P., & Patilb, N. G. (2014). Investigations into Machining of Inconel 718 By Using Adaptive Fuzzy Based Inference System. International Journal of Engineering Research, 3(5).
  • Raju, R. U., Kottala, R. K., Varma, B. M., Barmavatu, P., & Aepuru, R. (2024). Precision enhancement in CNC face milling through vibration-aided AI prediction of surface roughness. International Journal on Interactive Design and Manufacturing, 1-15. https://doi.org/10.1007/s12008-024-01948-2
  • Rakesh, M., & Datta, S. (2019). Effects of Cutting Speed on Chip Characteristics and Tool Wear Mechanisms During Dry Machining of Inconel 718 Using Uncoated WC Tool. Arabian Journal for Science and Engineering, 1-18. https://doi.org/10.1007/s13369-019-03785-y
  • Sen, B., Mandal, U. K., & Mondal, S. P. (2017). Advancement of an intelligent system based on ANFIS for predicting machining performance parameters of Inconel 690–A perspective of metaheuristic approach. Measurement, 109, 9-17. https://doi.org/10.1016/j.measurement.2017.05.050
  • Stephen, D. S., & Sethuramalingam, P. (2024). ANFIS prediction modeling of surface roughness and cutting force of titanium alloy ground with carbon nanotube grinding wheel. Multiscale and Multidisciplinary Modeling, Experiments and Design, 1-16. https://doi.org/10.1007/s41939-024-00411-9
  • Yılmaz, B., Karabulut, Ş., & Güllü, A. (2018). Performance analysis of new external chip breaker for efficient machining of Inconel 718 and optimization of the cutting parameters. Journal of Manufacturing Processes, 32, 553-563. https://doi.org/10.1016/j.jmapro.2018.03.025
  • Zafar, R. R., Karim, M., & Rahman, K. B. (2014). Neuro-fuzzy inference system (ANFIS) for ball end milling operation. International Research Journal of Mechanical Engineering, 2(6),174-190.

ANFIS MODELING OF SURFACE ROUGHNESS VALUES OF INCONEL 718 WORKPIECE MACHINED WITH COATED AND UNCOATED CUTTING TOOLS

Yıl 2025, Cilt: 28 Sayı: 1, 369 - 379, 03.03.2025

Hüseyin Gürbüz , Şehmus Baday

Öz

The aim of this study is to investigate the surface roughness values resulting from milling of Inconel 718 super alloy with coated and uncoated cutting tools using different machining parameters and to develop a model using Adaptive Neuro Fuzzy Inference System (ANFIS) to predict the experimental results. In the ANFIS model, the cutting tool type (coated and uncoated), feed rate f (mm/tooth) and cutting speed V (m/min) were used as input parameters, and the average surface roughness Ra (μm) was used as output parameter. In the created model, 70%, 15% and 15% of the experimental data were entered as training, test data and validation data, respectively. In determining the most suitable ANFIS model, the input membership function and their number were tested one by one and the model with the lowest error rate was selected. For the model with the lowest error rate, the output membership function, membership function and number were determined as linear, Gauss2mf and 333, respectively. When the experimental results were compared with the prediction results of the ANFIS model, the error rate value was calculated as 0.069596 and the coefficient of determination (R2) value was calculated as 0.9902. Depending on the obtained results, it was shown that the ANFIS model can be a successful method in predicting the surface roughness results in the milling process of Inconel 718.

Anahtar Kelimeler

Milling Inconel 718 coated and uncoated cutting tools surface roughness ANFIS

Proje Numarası

18.004

Kaynakça

  • Abdulshahed, A., & Badi, I. (2018). Prediction and control of the surface roughness for the end milling process using ANFIS. Operational Research in Engineering Sciences: Theory and Applications, 1(1), 1-12. https://doi.org/10.31181/oresta1901201011a
  • Asal, Ö., Dilipak, H., Yalçınkaya, A., & Ünal, Ş. (2021). Minimum Miktarda Yağlama Tekniği ile Frezeleme İşleminde Yüzey Pürüzlülüğünün Anfis ile Modellenmesi. International Journal of Innovative Engineering Applications, 5(2), 162-170. https://doi.org/10.46460/ijiea.952306
  • Cakir, M. V., Eyercioglu, O., Gov, K., Sahin, M., & Cakir, S. H. (2013). Comparison of soft computing techniques for modelling of the EDM performance parameters. Advances in Mechanical Engineering, 1-15. https://doi.org/10.1155/2013/392531
  • Çelik, A., Alağaç, M. S., Turan, S., Kara, A., & Kara, F. (2017). Wear behavior of solid SiAlON milling tools during high speed milling of Inconel 718. Wear, 378, 58-67. https://doi.org/10.1016/j.wear.2017.02.025
  • Dedeakayoğulları, H., Kaçal, A., & Keser, K. (2022). Modeling and prediction of surface roughness at the drilling of SLM-Ti6Al4V parts manufactured with pre-hole with optimized ANN and ANFIS. Measurement, 203, 112029. https://doi.org/10.1016/j.measurement.2022.112029
  • Dere, M., & Filiz, I. H. (2019). Experimental investigation of the effects of workpiece diameter and overhang length on the surface roughness in turning of free machining steel and modelling of surface roughness by using ANFIS. Journal of the Faculty of Engineering and Architecture of Gazi University, 34(2), 676-686. https://doi.org/10.17341/gazimmfd.416524
  • Fedai, Y., Ünüvar, A., Akın, H. K., & Başar, G. (2019). 316L Paslanmaz çeliklerin frezeleme işlemindeki yüzey pürüzlülüğün ANFIS ile modellenmesi. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 7(2), 98-110. https://doi.org/10.29130/dubited.466629
  • Gürbüz, H., & Baday, Ş. (2021). Milling Inconel 718 workpiece with cryogenically treated and untreated cutting tools. The International Journal of Advanced Manufacturing Technology, 116, 3135-3148. https://doi.org/10.1007/s00170-021-07688-x
  • Halim, N. H. A., Haron, C. H. C., Ghani, J. A., & Azhar, M. F. (2019). Tool wear and chip morphology in high-speed milling of hardened Inconel 718 under dry and cryogenic CO2 conditions. Wear, 426, 1683-1690. https://doi.org/10.1016/j.wear.2019.01.095
  • Hegab, H., Salem, A., Rahnamayan, S., & Kishawy, H. A. (2021). Analysis, modeling, and multi-objective optimization of machining Inconel 718 with nano-additives based minimum quantity coolant. Applied Soft Computing, 108, 107416. https://doi.org/10.1016/j.asoc.2021.107416
  • Jithendra, T., Basha, S. S., Divya, A., & Rajyalakshmi, G. (2024). Machine learning technique ANFIS-COA for enhancing micro-milling performance by investigating the surface roughness and material removal rate. International Journal on Interactive Design and Manufacturing, 1-22. https://doi.org/10.1007/s12008-024-02061-0
  • Kannan, S., & Kui, L. (2019). Experimental investigation of surface integrity during abrasive edge profiling of nickel-based alloy. Journal of Manufacturing Processes, 39, 40-51. https://doi.org/10.1016/j.jmapro.2019.01.052
  • Kasim, M. S., Hafiz, M. S. A., Ghani, J. A., Haron, C. H. C., Izamshah, R., Sundi, S. A., & Othman, I. S. (2019). Investigation of surface topology in ball nose end milling process of Inconel 718. Wear, 426, 1318-1326. https://doi.org/10.1016/j.wear.2018.12.076
  • Luo, M., Luo, H., Zhang, D., & Tang, K. (2018). Improving tool life in multi-axis milling of Ni-based superalloy with ball-end cutter based on the active cutting edge shift strategy. Journal of Materials Processing Technology, 252, 105-115. https://doi.org/10.1016/j.jmatprotec.2017.09.010
  • Ma, J. W., Wang, F. J., Jia, Z. Y., Xu, Q., & Yang, Y. Y. (2014). Study of machining parameter optimization in high speed milling of Inconel 718 curved surface based on cutting force. The International Journal of Advanced Manufacturing Technology, 75(1-4), 269-277. https://doi.org/10.1007/s00170-014-6115-x
  • Maher, I., Eltaib, M. E. H., Sarhan, A. A., & El-Zahry, R. M. (2014). Investigation of the effect of machining parameters on the surface quality of machined brass (60/40) in CNC end milling - ANFIS modeling. The International Journal of Advanced Manufacturing Technology, 74, 531-537. https://doi.org/10.1007/s00170-014-6016-z
  • Nath, C., Brooks, Z., & Kurfess, T. R. (2015). Machinability study and process optimization in face milling of some super alloys with indexable copy face mill inserts. Journal of Manufacturing Processes, 20, 88-97. https://doi.org/10.1016/j.jmapro.2015.09.006
  • Pandea, P. P., & Patilb, N. G. (2014). Investigations into Machining of Inconel 718 By Using Adaptive Fuzzy Based Inference System. International Journal of Engineering Research, 3(5).
  • Raju, R. U., Kottala, R. K., Varma, B. M., Barmavatu, P., & Aepuru, R. (2024). Precision enhancement in CNC face milling through vibration-aided AI prediction of surface roughness. International Journal on Interactive Design and Manufacturing, 1-15. https://doi.org/10.1007/s12008-024-01948-2
  • Rakesh, M., & Datta, S. (2019). Effects of Cutting Speed on Chip Characteristics and Tool Wear Mechanisms During Dry Machining of Inconel 718 Using Uncoated WC Tool. Arabian Journal for Science and Engineering, 1-18. https://doi.org/10.1007/s13369-019-03785-y
  • Sen, B., Mandal, U. K., & Mondal, S. P. (2017). Advancement of an intelligent system based on ANFIS for predicting machining performance parameters of Inconel 690–A perspective of metaheuristic approach. Measurement, 109, 9-17. https://doi.org/10.1016/j.measurement.2017.05.050
  • Stephen, D. S., & Sethuramalingam, P. (2024). ANFIS prediction modeling of surface roughness and cutting force of titanium alloy ground with carbon nanotube grinding wheel. Multiscale and Multidisciplinary Modeling, Experiments and Design, 1-16. https://doi.org/10.1007/s41939-024-00411-9
  • Yılmaz, B., Karabulut, Ş., & Güllü, A. (2018). Performance analysis of new external chip breaker for efficient machining of Inconel 718 and optimization of the cutting parameters. Journal of Manufacturing Processes, 32, 553-563. https://doi.org/10.1016/j.jmapro.2018.03.025
  • Zafar, R. R., Karim, M., & Rahman, K. B. (2014). Neuro-fuzzy inference system (ANFIS) for ball end milling operation. International Research Journal of Mechanical Engineering, 2(6),174-190.
Toplam 24 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Makine Mühendisliğinde Optimizasyon Teknikleri
Bölüm Makine Mühendisliği
Yazarlar

Hüseyin Gürbüz 0000-0003-1391-172X

Şehmus Baday 0000-0003-4208-8779

Proje Numarası 18.004
Yayımlanma Tarihi 3 Mart 2025
Gönderilme Tarihi 17 Ekim 2024
Kabul Tarihi 15 Şubat 2025
Yayımlandığı Sayı Yıl 2025Cilt: 28 Sayı: 1

Kaynak Göster

APA Gürbüz, H., & Baday, Ş. (2025). KAPLAMALI VE KAPLAMASIZ KESİCİ TAKIMLARLA İŞLENEN INCONEL 718 İŞ PARÇASININ YÜZEY PÜRÜZLÜLÜK DEĞERLERİNİN ANFIS İLE MODELLENMESİ. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 369-379.
 Kapak Resmi İndir