Tornalamada Akustik Emisyon ve Motor Akımı Optimizasyonu ve Analizi

Öz

Tornalamada kesme parametrelerinin optimizasyonu, iş parçasının yüzey kalitesinin, üretim verimliliğinin ve takım ömrünün artırılması için oldukça önemlidir. Endüstriyel uygulamalarda oldukça fazla sayıda malzemenin talaşlı imalat alanında kullanılması sebebiyle her malzeme için optimizasyon çalışması yapılması önemlidir. Ancak özellik birbirinden farklı birçok sektörde kritik yük taşıyan parçalarda kullanılan malzemelerin optimizasyonu elzemdir. Bu nedenle, bu çalışma kapsamında 5140 çeliğinin tornalanması için optimum kesme parametreleri belirlenmiştir. Deneyler kuru işleme koşullarında gerçekleştirilmiş olup 2 farklı kesme hızı, talaş derinliği, ilerleme hızı ve yaklaşma açısı incelenmiştir. Kesme parametreleri, akustik emisyon ve akım sensörlerinden alınan verilerle değerlendirilmiş olup optimizasyon tekniği olarak Taguchi yöntemi kullanılmıştır. Ayrıca kesme parametrelerinin akustik emisyon ve akım üzerindeki etkisi incelenmiştir. Sonuçlara göre kesme hızı motor akımı (70.8%) ve akustik emisyon (89.5%) üzerinde en etkili parametre olurken, istatistiksel analiz sonuçlarının hem motor akımı (92.5%) hem de akustik emisyon (95.9%) için yüksek güven aralığında olduğu görümüştür. Optimizasyon sonuçlarına göre motor akımı ve akustik emisyonu minimum yapacak parametreler sırası ile v1f1d1κ1 ve v1f1d2κ2 olarak tespit edilmiştir.

Anahtar Kelimeler

• Akustik emisyon
• Motor akımı
• Optimizsyon
• Taguchi

Acoustic Emission and Motor Current Optimization and Analysis in Turning

Abstract

Optimization of cutting parameters in turning is very important for increasing the surface quality, production efficiency and tool life of the workpiece. In industrial applications, it is important to make an optimization study for each material because a large number of materials are used in the field of machining. However, the optimization of materials used in parts carrying critical loads in many different sectors is essential. Therefore, in the scope of this study, optimum cutting parameters for turning 5140 steel were determined. The experiments were carried out under dry machining conditions and 2 different cutting speeds, depth of cut, feed rate and approach angle were investigated. The cutting parameters were evaluated with the data obtained from the acoustic emission and flow sensors, and Taguchi method was used as the optimization technique. In addition, the effect of cutting parameters on acoustic emission and current has been investigated. According to the results, while cutting speed was the most influential parameter on engine current (70.8%) and acoustic emission (89.5%), statistical analysis results were found to be in a high confidence interval for both motor current (92.5%) and acoustic emission (95.9%). According to the optimization results, the parameters that will minimize the motor current and acoustic emission were determined as v1f1d1κ1 and v1f1d2κ2, respectively.

Keywords

• Acoustic emission
• Motor Current
• Optimization
• Taguchi

References

1. Acar, O., Sağlam, H. ve Şaka, Z., 2021, Measuring Curvature of Trajectory Traced by Coupler of An Optimal Four-Link Spherical Mechanism, Measurement, 109189.
2. Ahmed, Y. S., Alam, M. S., Arif, A. ve Veldhuis, S., 2019, Use of acoustic emission and cutting force signals to monitor built-up edge formation in stainless steel turning, The International Journal of Advanced Manufacturing Technology, 103 (5), 2257-2276.
3. Al-Habaibeh, A. ve Gindy, N., 2000, A new approach for systematic design of condition monitoring systems for milling processes, Journal of materials processing technology, 107 (1-3), 243-251.
4. Aslan, A., Salur, E., Gunes, A., Sahin, O., Karadag, H. ve Akdemir, A., 2019, The mechanical properties of composite materials recycled from waste metallic chips under different pressures, International Journal of Environmental Science and Technology, 16 (9), 5259-5266.
5. Aslan, A., 2020, Optimization and analysis of process parameters for flank wear, cutting forces and vibration in turning of AISI 5140: A comprehensive study, Measurement, 163, 107959.
6. Başaltın, M. ve Yaman, K., 2016, Tornalamada Takım Yanak Aşınmasının Akustik Emisyon (AE) Yöntemiyle Analizi.
7. Bhuiyan, M., Choudhury, I. ve Nukman, Y., 2012, Tool condition monitoring using acoustic emission and vibration signature in turning, Proceedings of the world congress on engineering, 1-5.
8. Bhuiyan, M., Choudhury, I. A., Dahari, M., Nukman, Y. ve Dawal, S., 2016, Application of acoustic emission sensor to investigate the frequency of tool wear and plastic deformation in tool condition monitoring, Measurement, 92, 208-217.

9. Chen, X. ve Li, B., 2007, Acoustic emission method for tool condition monitoring based on wavelet analysis, The International Journal of Advanced Manufacturing Technology, 33 (9-10), 968-976.
10. Chethan, Y., Ravindra, H. ve Krishnegowda, Y., 2019, Optimization of machining parameters in turning Nimonic-75 using machine vision and acoustic emission signals by Taguchi technique, Measurement, 144, 144-154.
11. Fadare, D., Bonney, J. ve Ezugwu, E., 2012, Influence of cutting parameters and tool wear on acoustic emission signal in high-speed turning of Ti-6Al-4V Alloy, Journal of Emerging Trends in Engineering and Applied Sciences, 3 (3), 547-555.
12. Jemielniak, K., Kwiatkowski, L. ve Wrzosek, P., 1998, Diagnosis of tool wear based on cutting forces and acoustic emission measures as inputs to a neural network, Journal of Intelligent Manufacturing, 9 (5), 447-455.
13. Kuntoğlu, M. ve Sağlam, H., 2019, Investigation of progressive tool wear for determining of optimized machining parameters in turning, Measurement, 140, 427-436.
14. Kuntoğlu, M., Aslan, A., Sağlam, H., Pimenov, D. Y., Giasin, K. ve Mikolajczyk, T., 2020, Optimization and analysis of surface roughness, flank wear and 5 different sensorial data via Tool Condition Monitoring System in turning of AISI 5140, Sensors, 20 (16), 4377.
15. Kuntoğlu, M. ve Sağlam, H., 2020, Investigation of Signal Behaviors for Sensor Fusion with Tool Condition Monitoring System in Turning, Measurement, 108582.
16. Kuntoğlu, M., Aslan, A., Pimenov, D. Y., Usca, Ü. A., Salur, E., Gupta, M. K., Mikolajczyk, T., Giasin, K., Kapłonek, W. ve Sharma, S., 2021, A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends, Sensors, 21 (1), 108. Kuntoğlu, M. ve Aslan, A., 2021
17. AISI 5140 Çeliğinin Tornalanması Esnasında Yaklaşma Açısı ve Kesme Parametrelerinin İşlenebilirliğe Etkisinin İncelenmesi, Politeknik Dergisi, 1-1.
18. Li, X., 2002, A brief review: acoustic emission method for tool wear monitoring during turning, International Journal of Machine Tools and Manufacture, 42 (2), 157-165.
19. Li, X., 2005, Development of current sensor for cutting force measurement in turning, IEEE Transactions on Instrumentation and Measurement, 54 (1), 289-296.
20. Pimenov, D. Y., Hassui, A., Wojciechowski, S., Mia, M., Magri, A., Suyama, D. I., Bustillo, A., Krolczyk, G. ve Gupta, M. K., 2019, Effect of the relative position of the face milling tool towards the workpiece on machined surface roughness and milling dynamics, Applied Sciences, 9 (5), 842.
21. Salur, E., Aslan, A., Kuntoglu, M., Gunes, A. ve Sahin, O. S., 2019, Experimental study and analysis of machinability characteristics of metal matrix composites during drilling, Composites Part B: Engineering, 166, 401-413.
22. Salur, E., Acarer, M. ve Şavkliyildiz, İ., 2021, Improving mechanical properties of nano-sized TiC particle reinforced AA7075 Al alloy composites produced by ball milling and hot pressing, Materials Today Communications, 102202.
23. Sap, E., 2020, Microstructural and Mechanical Properties of Cu-Based Co-Mo-Reinforced Composites Produced by the Powder Metallurgy Method, Journal of Materials Engineering and Performance, 29 (12), 8461-8472.
24. Sharma, V. S., Sharma, S. ve Sharma, A. K., 2008, Cutting tool wear estimation for turning, Journal of Intelligent Manufacturing, 19 (1), 99-108.
25. Şap, E., 2021, Güçlendirilmiş Bakır Esaslı Kompozit Malzemelerin Mikroyapı ve Sertlik Özellikleri, Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 11 (1), 590-598.
26. Tönshoff, H., Jung, M., Männel, S. ve Rietz, W., 2000, Using acoustic emission signals for monitoring of production processes, Ultrasonics, 37 (10), 681-686.
27. Uzun, M., Munis, M. M. ve Usca, U., 2018, Different ratios CrC particle-reinforced Cu matrix composite materials and investigation of wear performance, Journal of Engineering Research and Application, 8 (7), 1-7.
28. Uzun, M. ve Usca, U. A., 2018, Effect of Cr particulate reinforcements in different ratios on wear performance and mechanical properties of Cu matrix composites, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 40 (4), 1-9.