Effects of Cutting Parameters on The Surface Roughness in CNC Turning of AISI 1050 Steel

Öz

In this study, the effects of cutting parameters on surface roughness and chip types in CNC turning of AISI 1050 steel were investigated. For this purpose, cutting speed, feed rate, and depth of cut were varied to evaluate their relative influences on the measured responses. An experimental design based on the Taguchi L9 orthogonal array was employed. Four different types of chips were observed in accordance with the ISO 3685 standard. Analysis of variance (ANOVA) was conducted to determine the statistical significance of each cutting parameter in relation to surface roughness. Cutting speed was identified as the most influential factor affecting surface roughness. Feed rate was the second ranking factor. Depth of cut appeared to have no significant effect on the measured surface roughness. Additionally, the mathematical relationship between cutting parameters and surface roughness was established using regression analysis. The regression model demonstrated a high level of accuracy, with a coefficient of determination (R2) of 0.966.

Anahtar Kelimeler

• Turning
• surface roughness
• chip formation
• AISI 1050 steel
• taguchi design

AISI 1050 Çelik CNC Tornalamada Kesme Parametrelerinin Yüzey Pürüzlülüğüne Etkileri

Öz

Bu çalışmada, AISI 1050 çeliğinin CNC tornalanmasında kesme parametrelerinin yüzey pürüzlülüğü ve talaş tipleri üzerindeki etkileri araştırılmıştır. Kesme hızı, ilerleme miktarı ve talaş derinliği faktörlerinin çıktılar üzerindeki bağıl etkilerini belirlemek amacıyla faktör seviyeleri belirli sınırlar içerisinde değiştirilmiştir. Taguchi L9 ortogonal dizisine dayalı bir deneysel tasarım kullanılmıştır. ISO 3685 standardına göre dört farklı talaş tipi gözlemlenmiştir. Her bir kesme parametresinin yüzey pürüzlülüğüyle istatistiksel ilişkisini belirlemek için varyans analizi (ANOVA) yapılmıştır. Kesme hızı, yüzey pürüzlülüğünü etkileyen en etkili faktör olarak belirlenmiştir. İlerleme miktarı, istatistiksel olarak ikinci etkili faktör olarak tespit edilmiştir. Talaş derinliğinin ölçülen yüzey pürüzlülüğü üzerinde önemli bir etkisi olmadığı görülmüştür. Çalışmada, ayrıca regresyon analizi yöntemi kullanılarak kesme parametreleri ve yüzey pürüzlülüğü arasındaki matematiksel ilişkiler belirlenmiştir. Regresyon modeli, 0,966’ık bir doğruluk katsayısı (R2) ile yüksek bir tahmin seviyesi göstermiştir.

Anahtar Kelimeler

• Tornalama
• yüzey pürüzlülüğü
• talaş formu
• AISI 1050 çelik
• taguchi tasarımı

Kaynakça

1. Baday Ş, Başak H, Gural A. Analysis of spheroidized AISI 1050 steel in terms of cutting forces and surface quality. Kovove Mater 2016; 54:315–320.
2. Çelik YH, Kilickap E, Güney M. Investigation of cutting parameters affecting on tool wear and surface roughness in dry turning of Ti-6Al-4V using CVD and PVD coated tools. J Braz Soc Mech Sci Eng 2017; 39:085–2093.
3. Rao Dr. CJ, Rao Dr. DN, Srihari P. Influence of cutting parameters on cutting force and surface finish in turning operation. Procedia Eng 2013; 64: 1405–1415.
4. Khalil ANM, Alib MAM, Azmic AI. Effect of Al2O3 nanolubricant with SDBS on tool wear during turning process of AISI 1050 with minimal quantity lubricant. Procedia Manuf 2015; 2: 130–134.
5. Çakır MC, Isik Y. Detecting tool breakage in turning AISI 1050 steel using coated and uncoated cutting tools. J Mater Process Technol 2005; 159:191–198.
6. Kafkas F. Evaluation of the efficiency of an ultrasonic atomization-based coolant (uACF) spray system in external turning using different nozzle tips. J Manuf Process 2022; 81: 991–1004.
7. Sarıkaya M, Güllü A. Taguchi design and response surface methodology based analysis of machining parameters in CNC turning under MQL. J Clean Prod 2014; 65:604–616.
8. Kuntoğlu M, Sağlam H. Investigation of progressive tool wear for determining of optimized machining parameters in turning. Measurement 2019; 140:427–436.

9. Nizamuddin M, Agrawal SM, Patil N. The Effect of Karanja based Soluble Cutting Fluid on Chips Formation in Orthogonal Cutting Process of AISI 1045 Steel. Procedia Manuf. 2018; 20: 12–17.
10. Kilickap E, Yardimeden A, Çelik YH. Mathematical modelling and optimization of cutting force, tool wear and surface roughness by using artificial neural network and response surface methodology in milling of Ti-6242S. Appl Sci 2017; 7/10: 1064.
11. Çaydaş U, Hasçalık A. Modeling and analysis of electrode wear and white layer thickness in die-sinking EDM process through response surface methodology. Int J Adv Manuf Technol. 2008; 38: 1148–1156.
12. Palanikumar K, Kartikeyan R. Optimal machining conditions for turning of particulate metal matrix composites using Taguchi and response surface methodologies. Mach Sci Technol. 2006; 10: 417–433.
13. Das SR, Panda A, Dhupal D. Experimental investigation of surface roughness, flank wear, chip morphology and cost estimation during machining of hardened AISI 4340 steel with coated carbide insert. Mech Adv Mater Mod Process 2017; 3: 9.