On-line chatter detection and identification based on wavelet and support vector machine

Intelligent spindles are core components of the next-generation of intelligent/smart machine tools in the Industry 4.0 Era. The purpose of this paper is to clarify the concept of intelligent spindles and provide an in-depth review of the state-of-the-art of related technologies. A new integrated concept for intelligent spindles is proposed, followed by descriptions of required characteristics, key enabling technologies and expected intelligent functions. Relevant research that may be beneficial to the development of intelligent spindles is reviewed from six thrust areas, which include monitoring and control of tool condition, chatter, spindle collision, temperature/thermal error, spindle balance, and spindle health. Finally, current limitations and challenges are discussed, and future trends of intelligent spindles are prospected from various perspectives.

The concept and progress of intelligent spindles: A review

Vibration analysis is widely used to reveal the fundamental cutting mechanics in machining condition monitoring. In this work, vibration signals generated in different chatter conditions as well as stable cutting are studied to understand chatter characteristics. Considering the nonlinear and non-stationary properties of chatter vibration in milling process, a self-adaptive analysis method named ensemble empirical mode decomposition (EEMD) is adopted to analyze vibration signals and two nonlinear indices are extracted as chatter indicators. Firstly, the vibration signal is preprocessed with a comb filter to eliminate the interference of rotation frequency, tooth passing frequency and their harmonics. Secondly, EEMD is applied to decompose the filtered signal into a set of intrinsic mode functions (IMFs). Sensitive IMFs containing rich chatter information are selected. With the development of chatter, an accumulation phenomenon appears in the spectrum of sensitive IMFs and chatter frequencies are modulated by the rotation frequency and tooth passing frequency. Finally, two nonlinear dimensionless indices within the range of [0, 1], i.e., C0 complexity and power spectral entropy, are extracted from the sensitive IMFs in both time domain and frequency domain. The proposed method is verified with well-designed cutting tests. It is found that, the stochastic noise dominates in the sensitive IMFs of stable cutting and both the C0 complexity value and power spectral entropy are the largest; with the increase of chatter severity level, the periodic chatter components dominate gradually and the proportion of stochastic noise decreases, and thus these two indicators decrease.

Chatter identification in end milling process based on EEMD and nonlinear dimensionless indicators

In modern industry, milling is an important tool when a high material removal rate is required. Chatter detection in this situation is a crucial step for improving surface quality and reducing both noise and rapid wear of the cutting tool. This paper proposes a new methodology for the chatter detection in computer numerical control milling machines. This methodology is based on vibratory signal analysis and artificial intelligence. The methodology consists of five major steps: (1) data acquisition, (2) signal processing, (3) features generation, (4) features selection and (5) classification. As chatter components occur around system resonance frequencies, a multiband resonance filtering method is proposed at the processing step. The process is then followed by envelope analysis. This allows the signal-to-noise ratio to be increased and the sensitivity of generated features to be increased. Extracted features are then ranked based on their entropy in which only best features are selected and presented to t...

Chatter detection in milling machines by neural network classification and feature selection

Recent developments in grinding machines

Chatter often occurs during precision hole boring, it results in low quality of finished surface and even damages the cutting tool. In order to identify chatter rapidly and gain the precious time for chatter suppression, a chatter monitoring system was established and an effective feature extraction method for boring chatter recognition was presented. According to the characteristic of chatter signal, empirical mode decomposition (EMD) was introduced into chatter feature extraction, and its basic theories were investigated. The vibration signal was decomposed by EMD, then the intrinsic mode functions (IMF) was got. Finally, the feature of chatter symptom was extracted by analyzing the energy spectrum of each IMF. The results show that feature extracted from vibration of boring bar by EMD can indicate chatter outbreak symptom, and it can be used as feature vectors for rapidly recognizing chatter.

An Effective EMD-Based Feature Extraction Method for Boring Chatter Recognition

Self-chatter is a serious problem in cutting process. This paper aims to solve the problem by establishing time series model of vibration acceleration signal in cutting process based on Hidden Markov Model (HMM) technology and achieve the purpose of chatter recognition and prediction. Features which can indicate cutting state are extracted from the acceleration signal. HMM parameters are obtained by model training, and the reference models database is built. Then cutting state recognition is performed according to the feature matching level. Simulations and experiments are conducted, and the results show that the proposed method is feasible and it could get high recognition

Prediction of Cutting Chatter Based on Hidden Markov Model

Wavelet denoising method was introduced to improve the validity of identifying the guide rail irregularity in elevator. The horizontal acceleration signal measured from a test elevator was processed by wavelet denoising. Then the denoised signal was decomposed up to four levels using Daubechies 4 mother wavelet to identify the guide rail irregularity. The results indicate that the impulses due to the guide rail irregularity are prominent in wavelet decompositions. And the causes resulting in those characteristics of the vibration signal can be well revealed. So discrete wavelet transform (DWT) can be used as an effective tool for denoising the horizontal vibration signal of elevator and diagnosing faults in the guide rail.

Wavelet Denoising of the Horizontal Vibration Signal for Identification of the Guide Rail Irregularity in Elevator

A 6-degree-of-freedom vibration isolation system for an ultra-precision grinding machine has been developed. This system uses air springs as passive vibration isolation elements and giant magnetostrictive actuators as active vibration isolation elements. The displacement and velocity of the ultra-precision grinding machine can be chosen as the output feedback variables for the controller. With the H /μ frequency shaping synthesis theory, a robust controller can be designed for the ultra-precision grinding machine, which is a nonlinear control system with uncertain disturbances. The method has an advantage comparing with the linear feedback control method, with which the dynamic performances and control accuracy of the system may become worse while the nonlinearity and disturbances exist. The simulation results show that the new vibration control method has stronger robustness, better dynamic performances and higher control accuracy than the common linear feedback control method. Introduction The ultra-precision machine tool is the key of the ultra-precision machining, and the environment vibration is the important factor affecting the precision of the ultra-precision machining. In most of the cases, a passive vibration isolation system using soft spring elements is adopted. In order to raise the vibration isolation performance, the springs of the passive system need to set up as lower stiffness, so that the accuracy of positioning is not satisfactory. Active vibration control technology is suggested to break off such problem. It is well known that the control performance depends on the characteristics of controller. There are many control system design methods for active vibration control system. The H /μ robust control synthesis methods are new and powerful tool to design the controller for active vibration control system [1-2]. In this paper, a 6-degree-of-freedom vibration isolation system for the ultra-precision grinding machine has been developed. This system is a hybrid vibration isolation system adopting the passive vibration isolation technology and the active vibration control technology. It uses air springs as passive vibration isolation elements and giant magnetostrictive actuators as active vibration isolation elements. By using the H /μ robust control theory, try to synthesize the robust controller to deal with the uncertainty and the nonlinear problems. Simulation tests were carried out to examine the performance of the vibration isolation system. It is confirmed that the developed system has excellent isolation performance against vibrations over wide frequency area. Model of Vibration Isolation System Structure of Vibration Isolation System. The simplified diagram of the vibration isolation system for an ultra-precision grinding machine is shown in Fig.1. This system includes an ultra-precision grinding machine, four air springs and eight giant magnetostrictive actuators. Three height control valves are used to adjust the height of the air springs. Four giant magnetostrictive actuators are set vertically and the other four ones are set horizontally. The giant magnetostrictive actuators and the air springs are arranged vertically in parallel. Four air springs support almost all of the weight of the ultra-precision grinding machine whilst the giant magnetostrictive actuators support almost no Key Engineering Materials Online: 2004-03-15 ISSN: 1662-9795, Vols. 259-260, pp 682-686 doi:10.4028/www.scientific.net/KEM.259-260.682 © 2004 Trans Tech Publications Ltd, Switzerland All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of Trans Tech Publications Ltd, www.scientific.net. (Semanticscholar.org-13/03/20,19:03:50) Journal Title and Volume Number (to be inserted by the publisher) 683 weight. Consequently, the specifications of the giant magnetostrictive actuators can be selected only by the maximum displacement necessary for the actuators, regardless of the weight of the operating load. Therefore, the actuators can work under the optimal pre-load condition, and the readjustment of the system also becomes quite easy even if the operating load on the machine tool is changed [1-3]. The displacement and velocity of the ultra-precision grinding machine are used as the output feedback variables for the controller. This vibration isolation system can be effective in isolating the vibration disturbances of different frequencies transmitting from the floor. Also it can isolate the low or ultra-low frequency vibrations, which are hard to be isolated by the passive vibration isolation system. Mathematical Model of Vibration Isolation System. The motion of the ultra-precision grinding machine as a rigid body can be described as a 6-degree-of-freedom system. Six variables are transition of the gravity center of the ultra-precision grinding machine in X, Y and Z direction (xg, yg and zg) and rotation around the X-axis, Y-axis and Z-axis ( X, Y and Z). The air springs are represented by spring elements (KXi, KYi, and KZi, i=1-4) and damper elements (CXi, CYi, and CZi, i=1-4) in X, Y and Z direction. The active control forces fai produced by the giant magnetostrictive actuators are denoted as follows: i ai ai I k f = , i=1, 2,,8. (1) where, Ii, kai are the control electric current and displacement factor of the ith actuator respectively. The equation of motion for the vibration isolation system is represented as follows: } u ]{ D ][ K [ } x ]{ K [ } x ]{ C [ } X ]{ K [ } X ]{ C [ } X ]{ M [ a 0 0 + + = + + & & & & . (2) where, [M] is the mass matrix(6×6), [C] is the damping matrix(6×6), [K] is the stiffness matrix(6×6), {X} is the displacement vector of center of gravity(6×1), {x0} is the displacement vector of the floor disturbance (6×1), {u} is the control electric current vector for the actuators(8×1), [Ka] is the stiffness matrix considering the actuator location(6×8), [D] is the displacement factor matrix of the actuators (8×8). Applying the normalized modal matrix [A], Eq.2 can be converted into the equation of motion for modal coordinates as follows: ) t ( ) t ( u ) t ( q ) t ( q 2 ) t ( q i mi i 2 i i i i i + = + + & & & , ) 6 , , 2 , 1 i ( L = . (3) where, qi(t) is the modal displacement of ith mode, Ci is the modal damping ratio of ith mode, Di is the modal circular frequency of ith mode, umi(t) is the modal control electric current of ith mode, Ci(t) is the modal disturbance of ith mode. Eq.3 shows that the control system for each mode can be designed independently. Therefore, the targeted control performance for the whole system can be designed and adjusted easily. H /μ Robust Control Fig.1 Simplified diagram of the vibration isolation system for an ultra-precision grinding machine Giant magnetostrictive actuator Air spring Disturbance {x0} Base Key Engineering Materials Vols. 259-260 683

H∞/μ Robust Control of Vibration for an Ultra-Precision Grinding Machine

The development of pressure sensors of high sensitivity and stable robustness over a broad range is indispensable for the future progress of electronic skin applicable to the detection of normal and shear pressures of various dynamic human motions. Herein, we present a flexible capacitive tactile sensing array that incorporates a porous dielectric layer with micro-patterned structures on the surface to enable the sensitive detection of normal and shear pressures. The proposed sensing array showed great pressure-sensing performance in the experiments, with a broad sensing range from several kPa to 150 kPa of normal pressure and 20 kPa of shear pressure. Sensitivities of 0.54%/kPa at 10 kPa and below, 0.45%/kPa between 10 kPa and 80 kPa, and 0.12%/kPa at 80 kPa and above were achieved for normal pressures. Meanwhile, for shear pressures, sensitivities up to 1.14%/kPa and 1.08%/kPa in x and y directions, respectively, and below 10 kPa, 0.73%/kPa, and 0.75%/kPa under shear pressure over 10 kPa were also validated. The performance of the finger-attached sensing array was also demonstrated, demonstrating which was a potential electronic skin to use in all kinds of wearable devices, including prosthetic hands, surgical robots, and other pressure monitoring systems.

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Zhe He Yao

Papers

An Effective EMD-Based Feature Extraction Method for Boring Chatter Recognition

Prediction of Cutting Chatter Based on Hidden Markov Model

Wavelet Denoising of the Horizontal Vibration Signal for Identification of the Guide Rail Irregularity in Elevator

H∞/μ Robust Control of Vibration for an Ultra-Precision Grinding Machine

Fabrication and Experimental Validation of a Sensitive and Robust Tactile Sensing Array with a Micro-Structured Porous Dielectric Layer