Kurumun Atıf Alan Makalesi
Atıf Alan Yayın
Investigation of signal behaviors for sensor fusion with tool condition monitoring system in turning
Scopus
Toplam 185 atıf DOI
Monitoring of the cutting area with different type of sensors requires confirmation for composing sensor fusion to obtain longer tool life and high-quality product. The complex structure of machining and interaction between variables affect the influence of parameters on quality indicators. Using multiple sensors provide comparison of information acquired from different resources and make easier to decide about tool and workpiece condition. In this experimental research for the first time, five different sensors were adopted to a lathe for collecting data to measure the capability of each sensor in reflecting the tool wear. Cutting forces, vibration, acoustic emission, temperature and current measurements were carried out during turning of AISI 5140 with coated carbide tools. Considering the graphical investigation, the successes of sensors on detection of progressive flank wear and tool breakage were investigated. Besides, the effects of cutting parameters on measured variables were interpreted considering graphs. According to results, temperature and acoustic emission signals seem to be effective about 74% for flank wear. In addition, fuzzy logic based prediction of flank wear was performed with the assistance of temperature and acoustic emission sensors with high accuracy which demonstrates their availability for sensor fusion. Tool breakage occurs instantly which can prevent with the assistance of sensor signals and tangential and feed cutting forces, acoustic emission and vibration signals seem as reliable indicators for approaching major breakage. Sensor fusion based turning provides confirmed information which enables more reliable, robust and consistent machining.
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