Abstract
Titanium alloys are the difficult to cut metals due to their low thermal conductivity and chemical affinity with tool material. Since the tool vibration is a replica of tool wear and surface roughness, the present study has proposed a methodology for estimating tool wear and surface roughness based on tool vibration for milling of Ti-6Al-4V alloy using cemented carbide mill cutter. Experiments are conducted at optimum levels of cutting speed, feed per tooth, and depth of cut, and experimental results for the tool vibration, tool wear, and surface roughness are collected until the flank wear reached 0.3 mm (ISO3685:1993). In the next stage, an optimization model of grey prediction GM(1,N) system and support vector machine (SVM) are used and estimated tool wear and surface roughness related to tool vibration. The predicted values of tool wear and surface roughness are compared with the experimental results. The optimization model of GM(1,N) predicted the tool wear and surface roughness with an average error of 3.03% and as 0.7% respectively while the SVM predicted with an average error of 7.67% and 4.45%, respectively.
Highlights
Vibration based tool condition monitoring in Milling of Ti-6Al-4V using an optimization model of GM(1,N) and support vector machine (SVM)
Since the tool vibration is a replica of tool wear and surface roughness, the present study has proposed a methodology for estimating tool wear and surface roughness based on tool vibration for milling of Ti-6Al-4V alloy using cemented carbide mill cutter
Experiments are conducted at optimum levels of cutting speed, feed per tooth and depth of cut and experimental results for the tool vibration, tool wear and surface roughness are collected until the flank wear reached 0.3 mm (ISO3685:1993)
Summary
An optimized grey model of GM(1,N) and SVM are used to forecast the tool wear and surface roughness using the tool vibration data. The OGM(1,N) model is proposed to predict tool wear and surface roughness considering tool vibration data and length of machining. It has been summarized that the process parameters are optimized to improve machining performance like reducing the surface roughness, tool wear, tool vibration, cutting forces and power consumption. The OGM(1,N) model and SVM techniques are used and predicted tool wear and surface roughness considering to the tool vibration data and length of machining in milling of Ti-6Al-4V.
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