Kurumun Atıf Alan Makalesi
Atıf Alan Yayın
Modeling of cutting parameters and tool geometry for multi-criteria optimization of surface roughness and vibration via response surface methodology in turning of AISI 5140 steel
Scopus
Open Access Toplam 119 atıf DOI
AISI 5140 is a steel alloy used for manufacturing parts of medium speed and medium load such as gears and shafts mainly used in automotive applications. Parts made from AISI 5140 steel require machining processes such as turning and milling to achieve the final part shape. Limited research has been reported on the machining vibration and surface roughness during turning of AISI 5140 in the open literature. Therefore, the main aim of this paper is to conduct a systematic study to determine the optimum cutting conditions, analysis of vibration and surface roughness under different cutting speeds, feed rates and cutting edge angles using response surface methodology (RSM). Prediction models were developed and optimum turning parameters were obtained for averaged surface roughness (Ra) and three components of vibration (axial, radial and tangential) using RSM. The results demonstrated that the feed rate was the most affecting parameter in increasing the surface roughness (69.4%) and axial vibration (65.8%) while cutting edge angle and cutting speed were dominant on radial vibration (75.5%) and tangential vibration (64.7%), respectively. In order to obtain minimum vibration for all components and surface roughness, the optimum parameters were determined as Vc = 190 m/min, f = 0.06 mm/rev, Κ = 60° with high reliability (composite desirability = 90.5%). A good agreement between predicted and measured values was obtained with the developed model to predict surface roughness and vibration during turning of AISI 5140 within a 10% error range.
Atıf Kaynağı
Atıf Yapan Yayın
ANOVA and fuzzy rule based evaluation and estimation of flank wear, temperature and acoustic emission in turning
Scopus
Havuzumuzda 51 atıf almış
Prediction of tool wear plays a key role in machining world due to its considerable impact on total costs in terms of fabricated part quality and disposal of cutting insert before reaching the tool life limit. In addition, comprehensive evaluation of machining characteristics provides a perspective for improved quality. In this study, during turning of AISI 5140 steel, on-line measurements of cutting tool tip temperature and acoustic emission (AE) and off-line measurement of flank wear (VB) of the cutting tool were performed. Very limited study has been published on the machinability characteristics of AISI 5140, while no research has been published on flank wear characteristics in addition to AE and temperature sensors before. Therefore, in this context, AE and tool tip temperature were measured with adapted sensor systems while VB measurement was performed with microscope when the machining was stopped. Three levels of cutting speed, feed rate, depth of cut and cutting-edge angle, experimental design was composed based on Taguchi's L27 orthogonal array. The main aim of the paper is to predict the flank wear i.e., main criteria for tool life, cutting temperature and AE with the help of fuzzy inference model. In addition, for providing a holistic approach and comprehensive point of view to the machinability, statistical analysis and optimization of the input parameters were given in detail for temperature, AE and VB. According to results, the combined effect of depth of cut and cutting-edge angle having effect (40%) on VB while cutting edge angle and cutting speed have dominance on temperature (45.4%) and AE (46.23%) respectively. Considering the complexity of turning operations, obtained findings reveal the superiority of the fuzzy inference model which was found the estimations highly close to the test results (90–97%) for each machining characteristic. The applicability of fuzzy rule system for different types of variables in turning is promising for the new generation of materials utilized in manufacturing sector.
Atıf Yapan Makale Bilgileri
Kurumlar (1)
Selçuk Üniversitesi
Selçuklu, Turkey