https://publikationen.bibliothek.kit.edu/1000084774/107735227

FEM simulation and acoustic emission based characterization of chip segmentation frequency in machining of Ti-6Al-4V

The use of aluminium alloys in manufacturing industry has increased substantially in current years. This is basically due to their ability to combine weightlessness and strength in a single material. The current paper focuses on a review of tool wear, power consumption, chip morphology, surface roughness, cutting temperature, cutting coolant, cutting forces and stresses during machining of aluminium alloy. Less work has been reported for use of non-edible/ionic fluids in machining of aluminium alloys we have covered. Thus, a lot of scope exists in the use of non-edible oil and ionic fluids for machining of aluminium alloys. This relevant information will give suitable choice for cutting conditions to the machine operators and engineers during machining of different aluminium alloys.

A Brief Study on Machinability of Aluminium Alloys

This paper presents the effect of different tool nose radii of 0.03, 0.1, 0.2 and 0.4 mm on tool wear, surface roughness, chip morphology, signals acquired from different sensor such as Acoustic Emission (AE), accelerometer and cutting force dynamometer in micro turning of stainless steel (SS 316L). Experiments have been carried out with coated tungsten carbide insert up to 60 minutes. Measurements and the signals are analysed for every 1st minute and 10th minute. It is observed that tool wear and chip width is found to be increasing, while Ra is found to be following non-uniform trend with increase in the nose radius. From the AE and vibration signal analysis, it is found that the dominant frequency of the signals increases with increase in the nose radius. Among the cutting forces, the tangential force (Fy) is found to be more sensitive to the tool wear status compared to that of the thrust force (Fx) and feed force (Fz). This study will be useful for the manufacturers to monitor the condition of the tool online while micro turning of SS 316L.

https://edlib.net/2015/icidret/icidret2015033.pdf

Studies On The Effect Of Different Nose Radius In Micro Turning Of Stainless Steel 316l

In this work, an online acoustic emission (AE) monitoring system is developed, to investigate the effect of tool wear during the microturning of titanium alloy with a tungsten carbide insert of nose radius 0.1 mm. The AE signal parameters were analyzed in time domain, frequency domain and discrete wavelet transformation (DWT) techniques to correlate with the tool wear status. The root mean square (AERMS) and specific AE energies are also computed for the decomposed AE signals, using the DWT. The results demonstrated that dominant frequency and DWT techniques are found to be most suitable for online tool condition monitoring, using AE sensors in the microturning of titanium alloy.

Acoustic Emission Signal Analysis for Tool Condition Monitoring in Microturning of Titanium Alloy

In the present work, an attempt has been made to monitor the tool condition status during microturning of aluminium alloy (AA 6061) using multiple sensors such as cutting force dynamometer, acoustic emission (AE) and accelerometer. The tool wear (nose wear) is correlated with surface roughness (Ra), chip width, thrust force (Fx), tangential force (Fy), feed force (Fz), AERMS and vibration signals. It is observed that Ra, chip width and cutting forces are increased with increase in the tool wear. Among the cutting forces, the tangential force (Fy) is found to be more sensitive to the tool wear status compared to that of the thrust force (Fx) and feed force (Fz). From the signal analysis, it is observed that during machining with good tool condition, the dominant frequency of the AERMS and vibration signals are found to be 81 kHz-110 kHz and 2.07 kHz-3.84 kHz respectively, whereas with the worn out tool the dominant frequencies are shifted to higher levels. Chip morphological studies indicated that favourable type of chips are formed upto 40th minute and unfavourable chips are observed from 41st minute to 60th minute.

A. Gopikrishnan

Papers

Acoustic Emission Signal Analysis for Tool Condition Monitoring in Microturning of Titanium Alloy

Tool Condition Monitoring in Microturning of Aluminium Alloy Using Multiple Sensors