Milling Process Monitoring Based on Vibration Analysis Using Hilbert-Huang Transform

2018 ◽  
Vol 12 (5) ◽  
pp. 688-698 ◽  
Author(s):  
Agus Susanto ◽  
Chia-Hung Liu ◽  
Keiji Yamada ◽  
Yean-Ren Hwang ◽  
Ryutaro Tanaka ◽  
...  
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Process Monitoring ◽  
Signal Analysis ◽  
Vibration Analysis ◽  
Milling Process ◽  
Machining Process ◽  
Hilbert Huang Transform ◽  
Thin Walled ◽  
Tool Damage

Vibration analysis is one method of machining process monitoring. The vibration obtained in machining is often nonlinear and of a nonstationary nature. Therefore, an appropriate signal analysis is needed for signal processing and feature extraction. In this research, vibrations obtained in the milling of thin-walled workpieces were analyzed using the Hilbert-Huang transform (HHT). The features obtained by the HHT served as machining-state indicators for machining process monitoring. Experimental results showed the effectiveness of the HHT method for detecting chatter and tool damage.

Stability Prediction during Thin-Walled Workpiece High-Speed Milling

2009 ◽  
Vol 69-70 ◽  
pp. 428-432 ◽  
Author(s):  
Qing Hua Song ◽  
Yi Wan ◽  
Shui Qing Yu ◽  
Xing Ai ◽  
J.Y. Pang
Keyword(s):  
High Speed ◽  
Dynamic Properties ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Milling Process ◽  
Machining Process ◽  
High Speed Milling ◽  
Thin Walled ◽  
Optimization Of Cutting Parameters ◽  
Free Material

A method for predicting the stability of thin-walled workpiece milling process is described. The proposed approach takes into account the dynamic characteristics of workpiece changing with tool positions. A dedicated thin-walled workpiece representative of a typical industrial application is designed and modeled by finite element method (FEM). The workpiece frequency response function (FRF) depending on tool positions is obtained. A specific 3D stability chart (SC) for different spindle speeds and different tool positions is then elaborated by scanning the dynamic properties of workpiece along the machined direction throughout the machining process. The dynamic optimization of cutting parameters for increasing the chatter free material removal rate and surface finish is presented through considering the chatter vibration and forced vibration. The investigations are compared and verified by high speed milling experiments with flexible workpiece.

scholarly journals Model-Based Segmentation and Analysis of Knee Sound Signals

2021 ◽  
Author(s):  
Farhana Parveen
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Knee Joint ◽  
Classification Accuracy ◽  
Signal Analysis ◽  
Early Stage ◽  
Sound Signal ◽  
Noninvasive Diagnosis ◽  
Model Based ◽  
Leave One Out

The motivation of the work is to develop a signal processing methodology for noninvasive diagnosis of knee osteoarthritis in an early stage. The sound signal that is emitted from knee when it moves is called Vibroathrographic (VAG) signal. Analysis of this sound signal will help in diagnosis of the knee joint problems. In this project a model based approach for sementing the VAG signals, followed by feature extraction and classification is proposed. This could be used to get some indication whether the signal is from a normal knee or from an abnormal knee. The proposed scheme also has the capability for finding the depth of severity of the damage and it can also localize the angle range of the knee swing, where the damage has occurred. As a result, the project gave an accuracy of 70.4% with leave-one-out method. After doing the classification using the segments, finally it has been calculated how many segments from each signal has been correctly identified. A total of 30 knee sound signals from normal and abmoraml knees has been used in this work and out of that 26 signals has been classified properly (either normal or abnormal) and 4 signals got misclassified with a successful classification accuracy of 86.7%.

Machining Allowance Optimal Distribution of Thin-Walled Structure Based on Deformation Control

2017 ◽  
Vol 868 ◽  
pp. 158-165 ◽  
Author(s):  
Yu Zhi Chen ◽  
Wei Fang Chen ◽  
Rui Jun Liang ◽  
Ting Feng
Keyword(s):  
Cutting Force ◽  
Element Model ◽  
Optimization Method ◽  
Milling Process ◽  
Machining Process ◽  
Machining Accuracy ◽  
Side Milling ◽  
Deformation Control ◽  
Thin Walled ◽  
Relationship Of

Multilayer cutting is widely used in finish machining process of thin-walled parts to improve the machining precision. The paper presents a cutting allowance optimization method using genetic algorithm to improve the machining quality and efficiency of thin-walled parts in the field of aerospace. Considering the coupling relationship of the deformation between the layers in layered milling, the parameterized finite element model of thin-walled parts in side milling process is established. The best relationship between the workpiece stiffness and the cutting force is determined though iterative calculations, and the deformation caused by the cutting force can be minimized. The results show that the optimized distribution of the depth of finishing cutting was better than the experience. The method proposed in this paper can reduce the deformation of the workpiece during the machining process, and thus improve the machining accuracy.

scholarly journals Position-Dependent Stability Prediction for Multi-Axis Milling of the Thin-Walled Component with a Curved Surface

2020 ◽  
Vol 10 (24) ◽  
pp. 8779
Author(s):  
Xiaojuan Wang ◽  
Qinghua Song ◽  
Zhanqiang Liu
Keyword(s):  
Material Removal ◽  
Variable Mass ◽  
Milling Process ◽  
Machining Process ◽  
Curved Surface ◽  
Structural Dynamic ◽  
Milling Stability ◽  
Integrated Method ◽  
Thin Walled ◽  
Cutting Path

Time-varying dynamic behaviors are essential to investigate the stability in the thin-walled workpiece milling process, which is usually affected by material removal and position-dependent characteristics of the workpiece along with the tool feed direction. To predict the milling stability with position-dependent, thin-walled component multi-axis milling, an improved structural dynamic modification method with variable mass is proposed in the paper. Firstly, the extraction of multi-axis milling material and the removal process of thin-walled parts with a complex curved surface and variable thickness is completed with CAM software. Then, the material removal of one cutting path as a modification of the structure is divided into multi-cutting steps with equal length to obtain the corrected FRFs in the machining process on the basis of the extended Sherman-Morrison-Woodbury formula. Furthermore, the dynamic characteristics of the initial un-machined workpiece and final machined workpiece are calculated by both experimental modal analysis and FEM. Finally, the multi-axis milling stability is predicted using the extended numerical integrated method, and an aero-engine blade is used to validate the accuracy and effectiveness of the proposed method for multi-axis milling molding parts.

Deformation Analysis of Thin-Walled Workpiece under Multi-Stress Coupled Effect

2010 ◽  
Vol 426-427 ◽  
pp. 284-288
Author(s):  
Dong Lu ◽  
Guo Hua Qin ◽  
Yi Ming Rong ◽  
C.M. Peng
Keyword(s):  
Initial Stress ◽  
Coupled Model ◽  
Element Model ◽  
Milling Process ◽  
Machining Process ◽  
Deformation Analysis ◽  
Milling Force ◽  
Thin Walled ◽  
And Control ◽  
The Finite Element Model

This document Cutting stress coupled with clamping stress and initial stress affects the workpiece deformation. To analyze the workpiece deformation the initial stress model is developed. The finite element model of milling process is established and the milling force and milling heat is predicted. The multi-stress coupled model is developed and the workpiece deformation during machining process and deformation after fixtures released are predicted. This study is helpful to predict and control the deformation for thin-walled workpiece.

Stability Prediction of Milling Process With Closed Machining System Dynamics With Flexible Thin-Walled Workpiece

2015 ◽  
Author(s):  
Chao Xu ◽  
Pingfa Feng ◽  
Dingwen Yu ◽  
Zhijun Wu ◽  
Jianfu Zhang
Keyword(s):  
Support System ◽  
Milling Process ◽  
Machining Process ◽  
Stability Lobe Diagram ◽  
Closed Model ◽  
Flexible Support ◽  
Stability Lobe ◽  
Machining System ◽  
Thin Walled ◽  
The Stability

Despite recent advances and improvements in modeling and prediction of the dynamics of the machining process, an efficient machining process is limited due to chatter and instability of machining system. In fact, the machining system contains various kinds of joints, which cause difficulties in dynamics modeling, simulation and prediction. Moreover, the flexible support system results in large deformation and violent vibration of the workpiece when machining, and the thin-walled workpiece easily gives rise to the chatter of the machining system. Therefore, the dynamics of the flexible support system was considered to calculate stability lobe diagram in the modeling of milling process. The whole machining system was regarded as a closed loop composed by the machine tool structures, support, workpiece and machining process. In this paper, the receptance coupling (RC) method was introduced to predict the dynamics of the closed machining system. A milling process was taken for example to predict the chatter limitations using the dynamics of closed model. The mathematical model of the machining system (machine tool structures, spindle, holder and tool), together with the details of joint contacts, was given based on the RC method. The RC model was used to obtain the dynamics of the system, while receptance of the tool point was coupled. Based on the coupling model of the machining system, the depth limitations under different speeds were estimated for the technology parameter optimization in milling process. The response was considered to be the sum of the cutting point and the support system. The flexibility of the support system was considered to be the feedback of the cutting stiffness. By this means, the traditional model was modified to calculate the stability lobe diagram based on the dynamics of the spindle and support system. Furthermore, the milling experiment was carried out to verify the prediction results, and the dominant natural frequencies of receptance at tool point were obtained by modal testing to define the stability lobe diagram. It was found that the chatter results matched well with the stability lobes. It was concluded that the support system with poor stiffness might cause violent chatter especially when the workpiece was thin-walled. The cutting depth limitations of the flexible support system were lower than that of the rigid one. Moreover, this closed model of the machining system is appropriate for the chatter prediction of the flexible support system or thin-walled workpiece, so it is helpful for a better parameter optimization.

Modular CNC Design for Intelligent Machining, Part 2: Modular Integration of Sensor Based Milling Process Monitoring and Control Tasks

1996 ◽  
Vol 118 (4) ◽  
pp. 514-521 ◽  
Author(s):  
Y. Altintas¸ ◽  
W. K. Munasinghe
Keyword(s):  
Process Control ◽  
Real Time ◽  
Process Monitoring ◽  
Tool Path ◽  
Position Control ◽  
Milling Process ◽  
Machining Process ◽  
Monitoring And Control ◽  
Process Monitoring And Control ◽  
And Control

Modular integration of sensor based milling process monitoring and control functions to a proposed CNC system architecture is presented. Each sensor based process control algorithm resides in a dedicated processor in the AT bus with a modular software. The CNC system’s motion control module has been designed to accomodate rapid manipulation of feeds, cutting conditions and NC tool path which may be demanded by machining process control modules in real time. Modular integration of adaptive control of cutting forces, tool condition monitoring, chatter detection and suppression tasks are illustrated as examples. The process control and monitoring modules are serviced in the real-time multi-tasking environment within one millisecond time intervals without disturbing the position control system. The paper present constraints and guidelines in designing CNC systems which allow modular integration of user developed real time machining process control and monitoring applications.

Modeling and Analysis Effects of Material Removal on Machining Dynamics in Milling of Thin-Walled Workpiece

2011 ◽  
Vol 223 ◽  
pp. 671-678 ◽  
Author(s):  
Ming Luo ◽  
Ding Hua Zhang ◽  
Bao Hai Wu ◽  
Ming Tang
Keyword(s):  
Material Removal ◽  
Dynamic Properties ◽  
Removal Rate ◽  
Milling Process ◽  
Machining Process ◽  
Machining Accuracy ◽  
Modeling And Analysis ◽  
Process System ◽  
Material Choice ◽  
Thin Walled

In aerospace industry, thin-walled workpieces are widely used in order to reduce the weight and to fulfill the high demands of their later applications. These workpieces are usually highly sophisticated and difficult to machine according to their geometry and material choice. In this paper, influence of material removal within the thin-walled workpiece machining operation on the dynamic properties of the workpiece and the machining process system is discussed. Aiming at learning about dynamic properties evolution during the machining operation, different milling processes of thin-walled plate are studied. Numerical simulation methods are employed in the study to investigate the dynamic properties evolution and machining stability with the material removal process in the milling process of thin-walled workpiece. The investigation results are expected to be used for designing optimized material removal sequence, which will guarantee highly material removal rate as well as highly machining accuracy of thin-walled workpiece.

scholarly journals Methods for the Analysis of EEG signals: A Review

2021 ◽  
Vol 9 (9) ◽  
pp. 873-876
Author(s):  
Malika Garg
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Electrical Activity ◽  
Signal Analysis ◽  
Eeg Signal ◽  
Eeg Analysis ◽  
Eeg Signals ◽  
Processing Techniques ◽  
The Brain ◽  
Eeg Signal Analysis

Abstract: Electroencephalography (EEG) helps to predict the state of the brain. It tells about the electrical activity going on in the brain. Difference of the surface potential evolved from various activities get recorded as EEG. The analysis of these EEG signals is of utmost importance to solve the problems related to the brain. Signal pre-processing, feature extraction and classification are the main steps of the EEG signal analysis. In this article we discussed various processing techniques of EEG signals. Keywords: EEG, analysis, signal processing, feature extraction, classification

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