Kurumun Atıf Alan Makalesi
Atıf Alan Yayın
Optimisation of parameters affecting surface roughness of Co28Cr6Mo medical material during CNC lathe machining by using the Taguchi and RSM methods
Scopus
Toplam 121 atıf DOI
This study involves modelling of experimental data of surface roughness of Co28Cr6Mo medical alloy machined on a CNC lathe based on cutting parameters (spindle rotational speed, feed rate, depth of cut and tool tip radius). In order to determine critical states of the cutting parameters variance analysis (ANOVA) was applied while optimisation of the parameters affecting the surface roughness was achieved with the Response Surface Methodology (RSM) that is based on the Taguchi orthogonal test design. The validity of the developed models necessary for estimation of the surface roughness values (Ra, Rz), was approximately 92%. It was found that for Ra 38% of the most effective parameters is on the tool tip radius, followed by 33% on the feed rate whereas for Rz tool tip radius occupied 43% with the feed being at 33% rate. To achieve the minimum surface roughness, the optimum values obtained for spindle rpm, feed rate, depth of cut and tool tip radius were respectively, 318 rpm, 0.1 mm/rev, 0.7 mm and 0.8 mm.
Atıf Kaynağı
Atıf Yapan Yayın
Examination of the wear behavior of cu-based brake pads used in high-speed trains and prediction through statistical and neural network models
Scopus
Havuzumuzda 2 atıf almış
The aim of this study is to provide insights into the performance of copper-based brake pads used in high-speed trains and contribute to a more predictable braking system by leveraging mathematical and artificial intelligence (AI) models. The wear behavior of Cu-based brake pads in high-speed trains was investigated using a pin-on-disc test setup under different speeds, temperatures, and loads with a constant sliding distance. Additionally, mathematical and AI models were developed to predict the friction coefficient and wear rate values obtained from the experiments. This innovative approach initiates a significant discussion in line with a current need, and the sharing and publication of the obtained results are currently essential to address the knowledge gap in this field. The results revealed that an increase in temperature led to an increase in both the friction coefficient and wear rate. Conversely, an increase in load resulted in a decrease in both the friction coefficient and wear rate. The transition from abrasive wear to adhesive wear occurred due to the softening of copper between friction surfaces, leading to material transfer. According to the results obtained from the models, both the artificial neural network (ANN) and multiple regression models demonstrated comparable accuracy, predicting the friction coefficient with approximately 94% accuracy in both cases, indicating reliable predictions. For the wear rate, the models achieved approximately 90% and 92% accuracy, respectively.
Atıf Yapan Makale Bilgileri
Kurumlar (3)
Necmettin Erbakan Üniversitesi
Meram, Turkey
Niğde Ömer Halisdemir University
Nigde, Turkey
Selçuk Üniversitesi
Selçuklu, Turkey