Analysis of Vibration Signals in Monitoring Titanium End Milling Process Using Triaxial Accelerometer

2021 ◽  
Author(s):  
John Henry Navarro-Devia ◽  
Dzung Viet Dao ◽  
Yun Chen ◽  
Huaizhong Li
Keyword(s):  
Titanium Alloys ◽  
Frequency Domain ◽  
Single Channel ◽  
End Milling ◽  
Cutting Parameters ◽  
Frequency Component ◽  
Spindle Speed ◽  
Triaxial Accelerometer ◽  
Vibration Signals ◽  
Time Frequency

Abstract Vibrations during milling of hard-to-cut materials can cause low productivity, inferior quality and short tool life. It is one of the common issues in the machining of hard-to-cut materials employed in aerospace applications, such as titanium alloys. This paper presents an analysis of the vibration signals in the 3 axes of movement during titanium end milling, under diverse cutting parameters, manipulating spindle speed and feed rate. Signals were obtained using a triaxial accelerometer and processed in MATLAB. The analysis was conducted in the frequency-domain and the time-frequency domain. The results show that high-frequency vibration could occur in any direction with different amplitudes. Response on each axis depends on spindle speed, feed, and type of milling. A frequency component continually appeared in each axis regardless of cutting conditions and is located near the natural frequencies. Finally, the triaxial accelerations were compared for the milling cases with a new and a worn tool. Results highlight the importance and need for continuous monitoring of vibration in the 3 axes, instead of only using a single-channel signal, providing experimental data which could expand knowledge relating to the milling of titanium alloys.

Improvement of Surface Roughness in End Milling of Ti6Al4V by Coupling RSM with Genetic Algorithm

2011 ◽  
Vol 264-265 ◽  
pp. 1154-1159
Author(s):  
Anayet Ullah Patwari ◽  
A.K.M. Nurul Amin ◽  
S. Alam
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Titanium Alloys ◽  
End Milling ◽  
Fitness Function ◽  
Cutting Speed ◽  
Weight Ratio ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Resistance Surface

Titanium alloys are being widely used in the aerospace, biomedical and automotive industries because of their good strength-to-weight ratio and superior corrosion resistance. Surface roughness is one of the most important requirements in machining of Titanium alloys. This paper describes mathematically the effect of cutting parameters on Surface roughness in end milling of Ti6Al4V. The mathematical model for the surface roughness has been developed in terms of cutting speed, feed rate, and axial depth of cut using design of experiments and the response surface methodology (RSM). Central composite design was employed in developing the surface roughness models in relation to primary cutting parameters. The experimental results indicate that the proposed mathematical models suggested could adequately describe the performance indicators within the limits of the factors that are being investigated. The developed RSM is coupled as a fitness function with genetic algorithm to predict the optimum cutting conditions leading to the least surface roughness value. MATLAB 7.0 toolbox for GA is used to develop GA program. The predicted results are in good agreement with the experimental one and hence the model can be efficiently used to achieve the minimum surface roughness value.

Tool Vibration due to High Speed Micro End Milling Parameters

2013 ◽  
Vol 372 ◽  
pp. 364-368 ◽  
Author(s):  
Abdul Rahman Mohamed ◽  
Nur Atiqah ◽  
Mohammad Yeakub Ali ◽  
M.S.H. Chowdhury
Keyword(s):  
High Speed ◽  
End Milling ◽  
Signal To Noise Ratio ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Signal To Noise ◽  
Tool Vibration ◽  
Milling Parameters ◽  
Micro End Milling

This paper presents the effect of high speed micro end milling parameters on tool vibration during machining of poly (methyl methacrylate) (PMMA). The main focus is to achieve minimum tool vibration by controlling the cutting parameters; spindle speed, feed rate and depth of cut. An empirical model for tool vibration has been developed using Taguchi method. The orthogonal array, signal-to-noise ratio and analysis of variance revealed that high spindle speed is the most influential parameter to increase the level of tool vibration.

Estimation of fractal dimension and surface roughness based on material characteristics and cutting conditions in the end milling of carbon steels

2015 ◽  
Vol 231 (8) ◽  
pp. 1423-1437 ◽  
Author(s):  
Xue Zuo ◽  
Hua Zhu ◽  
Yuankai Zhou ◽  
Jianhua Yang
Keyword(s):  
Surface Roughness ◽  
Fractal Dimension ◽  
Feed Rate ◽  
End Milling ◽  
Cutting Parameters ◽  
Carbon Steels ◽  
Spindle Speed ◽  
Numerical Control ◽  
Material Hardness ◽  
Milled Surface

Cutting parameters and material properties have important effects on the quality of milled surface, which can be characterized by fractal dimension and surface roughness. The relationships between two surface parameters (surface roughness and fractal dimension) and material hardness, elongation, spindle speed and feed rate were investigated, respectively, in this study. Four carbon steels, that is, AISI 1020, Gr 50, 1045 and 1566, were milled with five spindle speeds and four feed rates on a computer numerical control machine. The surface topographies were measured with a three-dimensional profiler. The surface profiles were obtained by re-sampling the data points on the surface topography in the measurement direction. The surface roughness and fractal dimension were calculated from the two-dimensional profiles, where the fractal dimension was obtained by the root-mean-square method. The results showed that for specific spindle speed and feed rate, the roughness of the milled surface decreased with the workpiece hardness, whereas the elongation and fractal dimension increased with the hardness. Based on the material hardness and elongation, spindle speed and feed rate, empirical formulae were established to quantitatively estimate the surface roughness and fractal dimension. Moreover, the spindle speed and feed rate can be easily calculated from the empirical formulae to achieve a surface with the desired surface roughness and fractal dimension. The empirical formulae have been demonstrated with the experiments and were shown to be applicable in estimating the surface roughness and fractal dimension for all carbon steels in end milling. The results are instructive for the fractal dimension estimation of the machined surfaces of carbon steel, which has not been previously studied.

An Experimental Study of Hard Dry Milling Based on Multi-Sensor Process Monitoring and Analysis

2010 ◽  
Vol 108-111 ◽  
pp. 1086-1091
Author(s):  
Li Bing Liu ◽  
Xi Wang ◽  
Wei Wu Zhong ◽  
Hui Yu ◽  
Dong Ting Liao ◽  
...  
Keyword(s):  
Virtual Instrument ◽  
Wavelet Packet ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Machining Process ◽  
Depth Of Cut ◽  
Dry Milling ◽  
Hard Milling ◽  
Vibration Signals ◽  
Ae Signals

This paper aims to collect the acoustic emission (AE), the vibration and the temperature signals produced in the hard dry milling of the die steel by using a signal collecting system based on multi-sensor and virtual instrument. Then the signals are processed by the wavelet transform and the wavelet packet transform. So we could pick up some regular pattern which could reflect the characteristic of the machining process from the results. The cutting parameters are set with single factor method and the experiment primarily focuses on researching the effect of changing the cutting parameters on the three signals mentioned forward. Through the experiment, some conclusions could be drawn as follow. In the process of the die steel’s hard dry milling, the spindle speed has a great effect on the AE signals. The temperature is mainly involved with the spindle speed and the depth of cut. The vibration signals have any clear pattern when the cutting parameter changes, but the energy of the vibration signals concentrate mostly on the first frequency band. Furthermore, the process of the hard milling is more stable.

Design of Adaptive Filter for Health Monitoring on a Gearbox

2006 ◽  
Vol 321-323 ◽  
pp. 1237-1240
Author(s):  
Sang Kwon Lee ◽  
Jung Soo Lee
Keyword(s):  
Signal Processing ◽  
Frequency Domain ◽  
Adaptive Filter ◽  
Health Monitoring ◽  
Background Noise ◽  
Adaptive Signal Processing ◽  
Vibration Signals ◽  
Time Frequency ◽  
Frequency Representation ◽  
Adaptive Signal

Impulsive vibration signals in gearbox are often associated with faults, which lead to due to irregular impacting. Thus these impulsive vibration signals can be used as indicators of machinery faults. However it is often difficult to make objective measurement of impulsive signals because of background noise signals. In order to ease the measurement of impulsive signal embedded in background noise, we enhance the impulsive signals using adaptive signal processing and then analyze them in time and frequency domain by using time-frequency representation. This technique is applied to the diagnosis of faults within laboratory gearbox.

Effect of Cutting Parameters on Surface Roughness When Milling Hardened AISI D2 Steel (56 HRC) Using Taguchi Techniques

2012 ◽  
Author(s):  
Ahmed Zaidan Mohammed Shammari ◽  
Kamal Ati Amwead ◽  
Auday Shaker Hadi
Keyword(s):  
Surface Roughness ◽  
End Milling ◽  
Signal To Noise Ratio ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Cold Extrusion ◽  
Depth Of Cut ◽  
Control Factors ◽  
Aisi D2 ◽  
Steel Aisi

The tool steel identifying AISI D2 is commonly used for cold working operations, such as sheet metal forming, cold extrusion and forging operation. To perform in these applications, they must have excellent strength, hardness, and wear resistance. The aim of the present study is to find optimal process parameters for end milling of hardened steel AISI D2 (56 HRC) using Taguchi method. A L25 array, Taguchi’s signal-to-noise ratio and ANOVA are employed to determine effects of many control factors (spindle speed, feed rate, and depth of cut) on surface roughness. In this paper, results show that the spindle speed is most influencing parameters.

scholarly journals Optimization of Cutting Parameters for Milling Process of (4032) Al-Alloy using Taguchi-Based Grey Relational Analysis

2021 ◽  
Vol 17 (3) ◽  
pp. 1-12
Author(s):  
Sami Abbas Hammood
Keyword(s):  
Process Parameters ◽  
Grey Relational Analysis ◽  
End Milling ◽  
Cutting Parameters ◽  
Milling Process ◽  
Spindle Speed ◽  
Al Alloy ◽  
Relational Analysis ◽  
Grey Relational ◽  
End Milling Process

The objective of this work is to study the influence of end milling cutting process parameters, tool material and geometry on multi-response outputs for 4032 Al-alloy. This can be done by proposing an approach that combines Taguchi method with grey relational analysis. Three cutting parameters have been selected (spindle speed, feed rate and cut depth) with three levels for each parameter. Three tools with different materials and geometry have been also used to design the experimental tests and runs based on matrix L9. The end milling process with several output characteristics is solved using a grey relational analysis. The results of analysis of variance (ANOVA) showed that the major influencing parameters on multi-objective response were spindle speed and cutting tool with contribution percentage (52.75%, 24%), respectively. In addition, the optimum combination of end milling process parameters was then validated by performing confirmation tests to determine the improvement in multi-response outputs. The confirmation tests obtained a minimum (surface roughness and micro-hardness) and maximum metal removal rate with grey relational grade of 0.784 and improvement percentage of 2.3%.

scholarly journals Multi-channel spectrograms for speech processing applications using deep learning methods

2020 ◽  
Author(s):  
T. Arias-Vergara ◽  
P. Klumpp ◽  
J. C. Vasquez-Correa ◽  
E. Nöth ◽  
J. R. Orozco-Arroyave ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Speech Processing ◽  
Single Channel ◽  
Audio Signal ◽  
Frequency Component ◽  
Feature Maps ◽  
Time Frequency ◽  
Convolutional Networks ◽  
Channel Input

Abstract Time–frequency representations of the speech signals provide dynamic information about how the frequency component changes with time. In order to process this information, deep learning models with convolution layers can be used to obtain feature maps. In many speech processing applications, the time–frequency representations are obtained by applying the short-time Fourier transform and using single-channel input tensors to feed the models. However, this may limit the potential of convolutional networks to learn different representations of the audio signal. In this paper, we propose a methodology to combine three different time–frequency representations of the signals by computing continuous wavelet transform, Mel-spectrograms, and Gammatone spectrograms and combining then into 3D-channel spectrograms to analyze speech in two different applications: (1) automatic detection of speech deficits in cochlear implant users and (2) phoneme class recognition to extract phone-attribute features. For this, two different deep learning-based models are considered: convolutional neural networks and recurrent neural networks with convolution layers.

scholarly journals A Method Combining Fractal Analysis and Single Channel ICA for Vibration Noise Reduction

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Quanbo Lu ◽  
Mei Li
Keyword(s):  
Noise Reduction ◽  
Fractal Analysis ◽  
Single Channel ◽  
Vibration Signal ◽  
Wavelet Denoising ◽  
Vibration Signals ◽  
Time Frequency ◽  
Engineering Vibration ◽  
Ica Algorithm ◽  
Vibration Noise

Aiming at the problem that real engineering vibration signals are interfered by strong noise, this paper proposes a method combining single channel-independent component analysis (SCICA) and fractal analysis (FD) to reduce the effect of noise on the time-frequency analysis of vibration signals. First, phase space reconstruction is performed on the vibration signal to make the proper input for ICA algorithm. The original is then decomposed into several component signals. The fractal dimension of each component signals is calculated to determine whether the signal should be considered noise. Noisy component signals are then processed by wavelet denoising. Finally, the output signal after noise reduction is reconstructed using the filtered “right” component signals. This paper uses the method to analyze real noisy vibration signal. Experimental results show the effectiveness of the proposed method.

scholarly journals Joint Dedusting and Enhancement of Top-Coal Caving Face via Single-Channel Retinex-Based Method with Frequency Domain Prior Information

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2097
Author(s):  
Chengcai Fu ◽  
Fengli Lu ◽  
Xiaoxiao Zhang ◽  
Guoying Zhang
Keyword(s):  
Frequency Domain ◽  
Prior Information ◽  
Single Channel ◽  
Coal Dust ◽  
Low Frequency ◽  
Frequency Component ◽  
White Balance ◽  
Local Means ◽  
Non Local ◽  
Color Restoration

Affected by the uneven concentration of coal dust and low illumination, most of the images captured in the top-coal caving face have low definition, high haze and serious noise. In order to improve the visual effect of underground images captured in the top-coal caving face, a novel single-channel Retinex dedusting algorithm with frequency domain prior information is proposed to solve the problem that Retinex defogging algorithm cannot effectively defog and denoise, simultaneously, while preserving image details. Our work is inspired by the simple and intuitive observation that the low frequency component of dust-free image will be amplified in the symmetrical spectrum after adding dusts. A single-channel multiscale Retinex algorithm with color restoration (MSRCR) in YIQ space is proposed to restore the foggy approximate component in wavelet domain. After that the multiscale convolution enhancement and fast non-local means (FNLM) filter are used to minimize noise of detail components while retaining sufficient details. Finally, a dust-free image is reconstructed to the spatial domain and the color is restored by white balance. By comparing with the state-of-the-art image dedusting and defogging algorithms, the experimental results have shown that the proposed algorithm has higher contrast and visibility in both subjective and objective analysis while retaining sufficient details.

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