Morphological Filtering Algorithm for Estimation of Tool Wear in CNC Milling Machining

2011 ◽  
Vol 697-698 ◽  
pp. 566-569
Author(s):  
Qian Ning ◽  
Tai Yong Wang
Keyword(s):  
Tool Wear ◽  
Manufacturing System ◽  
Systematic Approach ◽  
Vibration Signal ◽  
Cnc Milling ◽  
Tool Condition ◽  
Morphological Filtering ◽  
Force Signal ◽  
Tool Wear Estimation ◽  
Feature Values

Estimation of tool condition has very important meaning to improve the product quality, continuous machining ability and reliability of the manufacturing system. Based on mathematical morphology, a systematic approach is developed to implement online estimation of tool wear in this paper. As the nonlinear filter, morphological filter is selected to reduce the higher frequency noises before feature values extraction. The feature vector consists of original characteristics of vibration signal and cutting force signal. Then, they are input into SVM for training and testing. Experiments show that this method can achieve tool wear estimation effectively.

Feature-Based Tool Condition Monitoring in a Gear Shaving Application

2008 ◽  
Author(s):  
A. J. Brzezinski ◽  
Y. Wang ◽  
D. K. Choi ◽  
X. Qiao ◽  
J. Ni
Keyword(s):  
Tool Wear ◽  
Condition Monitoring ◽  
Feature Fusion ◽  
Estimation Method ◽  
Principal Component ◽  
Time Frequency ◽  
Tool Condition ◽  
Tool Wear Estimation ◽  
Artificial Neural Network Ann ◽  
Feature Dimension

Condition monitoring (CM) is an effective way to improve the tool life of a cutting tool. However, CM techniques have not been applied to monitor tool wear in an industrial gear shaving application. Therefore, this paper introduces a novel, sensor-based, data-driven, tool wear estimation method for monitoring gear shaver tool condition. The method is applied on an industrial gear shaving machine and used to differentiate between four different tool wear conditions (new, slightly worn, significantly worn, and broken). This research focuses on combining, expanding, and implementing CM techniques in an application where no previous work has been done. In order to realize CM, this paper discusses each aspect of CM, beginning with data collection and pre-processing. Feature extraction (in the time, frequency, and time-frequency domains) is then explained. Furthermore, feature dimension reduction using principal component analysis (PCA) is described. Finally, feature fusion using a multi-layer perceptron (MLP) type of artificial neural network (ANN) is presented.

scholarly journals Comparing Vibration Sensor Positions in CNC Turning for a Feasible Application in Smart Manufacturing System

2018 ◽  
Vol 12 (3) ◽  
pp. 282-289 ◽  
Author(s):  
Jonny Herwan ◽  
◽  
Seisuke Kano ◽  
Ryabov Oleg ◽  
Hiroyuki Sawada ◽  
...  
Keyword(s):  
Tool Wear ◽  
Manufacturing System ◽  
Essential Feature ◽  
Vibration Sensor ◽  
Smart Manufacturing ◽  
Cnc Turning ◽  
Tool Condition ◽  
Tool Monitoring ◽  
Tool Shank ◽  
Sensor Positions

Tool condition monitoring, such as tool wear and breakage, is an essential feature in smart manufacturing system. One of most potential sensors that can be used in tool monitoring is vibration sensor, which usually assembled at tool shank. However, in case of CNC turning with rotating tool turret, it is impossible to assemble the vibration sensor at the tool shank because wire of the sensor will be damaged when the turret rotated. This paper is addressed to compare thoroughly alternative sensor positions. Ten sensor positions including tool shank, as a reference, are investigated. The signals from three types of cutting, namely; normal cutting, abnormal cutting with tool wear and abnormal cutting when tool breakage occurred, are investigated. Based on the magnitude of the output signals and their capability to predict tool wear and breakage, a suggestion on vibration sensor positions is proposed.

I-KazTM-Based Analysis of Cutting Force Signals for Tool Condition Monitoring in Turning Process

2013 ◽  
Vol 471 ◽  
pp. 203-207
Author(s):  
Muhammad Rizal ◽  
Jaharah A. Ghani ◽  
Mohd Zaki Nuawi ◽  
Che Hassan Che Haron
Keyword(s):  
Tool Wear ◽  
Cutting Force ◽  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Machining Process ◽  
Machined Surface ◽  
Tool Condition ◽  
Feed Force ◽  
The Status ◽  
Force Signal

Cutting force is an important signal in machining process and has been widely used for tool condition monitoring. Monitoring the condition of the cutting tool in the machining process is very important to maintain the machined surface quality and consequently reduce inspection costs and increase productivity. This paper utilizes I-kaz-based analysis of cutting force signal to monitor the status of tool wear. The cutting force signals are measured by two channels of strain gauge that were mounted on the surface of tool holder. Experiments were carried out by turning hardened carbon steel and cutting force signals were analyzed using I-kazTM technique by integrating two component of signals (I-kaz 2D, Z2), I-kaz of cutting force (Z of Fy), and I-kaz of feed force (Z of Fx). The results show that I-kaz of feed force can be effectively used to monitor tool wear progression during turning operation.

scholarly journals Development of in-situ adaptive controller for end milling based on vibration feedback

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
L. Vamsi Krishna Reddy ◽  
Balla Srinivasa Prasad ◽  
N. Harsha Raghuram ◽  
M. Rajasekhar
Keyword(s):  
Adaptive Control ◽  
End Milling ◽  
Vibration Signal ◽  
Adaptive Controller ◽  
Rule Base ◽  
Cnc Milling ◽  
Related Information ◽  
Control Optimization ◽  
Tool Condition ◽  
Tool Performance

AbstractThis study establishes a tool condition monitoring methodology builds on the vibration signal attained via data acquisition system which is integrated with the in house developed adaptive controller for an end milling. As the quality of the products and the machine tool performance are the key parameters in maintaining machine stability. Proposed Adaptive control optimization system is validated with the experimentation trials and data analysis on 3 axis CNC milling machine. The rotational speed of the spindle and vibration signals is found to be reactive to milling cutter condition and therefore capable of sustaining the set-out methodology. A novel hybrid transformation, coupled with FFT and HHT is proposed to distinguish between a source of variation for adaptive control optimization, cutting region with the non-cutting region. In this study, decisions are made to evaluate the tool condition by combining all related information into a rule base. The investigation trajectories unveil the established system be able to accomplish the mechanism properly as anticipated.

Milling Tool Wear Condition Monitoring Based on Extension Theory

2012 ◽  
Vol 562-564 ◽  
pp. 523-527
Author(s):  
Dong Min Wu ◽  
Ming Shen
Keyword(s):  
Tool Wear ◽  
Vibration Signal ◽  
Extension Theory ◽  
Milling Force ◽  
Condition Evaluation ◽  
Milling Tool ◽  
Force Signal ◽  
Ae Signal ◽  
Wear Monitoring ◽  
Wear Condition

The method of mill tool wear condition evaluation based on the extension theory is put forward by the paper. Through researching, the author firstly designs mill tool wear monitoring system which can acquire milling force signal, AE signal, vibration signal and main motor power signal. Based on the matter-element of classics, joint domain and evaluation, and deducting the dependent functions, at last the objective and reasonable evaluation results are got. It is proved that the extension theory is the valid and reliable in evaluating the mill tool wear condition.

scholarly journals Prediction of shallow bit position based on vibration signal monitoring of bit broken rock

2021 ◽  
Vol 17 (1) ◽  
pp. 155014772199170
Author(s):  
Jinping Yu ◽  
Deyong Zou
Keyword(s):  
Acoustic Emission ◽  
Time Domain ◽  
Vibration Signal ◽  
Rock Breaking ◽  
Drill String ◽  
Mean Square ◽  
Drilling Force ◽  
Emission Signal ◽  
Force Signal ◽  
The Mean

The speed of drilling has a great relationship with the rock breaking efficiency of the bit. Based on the above background, the purpose of this article is to predict the position of shallow bit based on the vibration signal monitoring of bit broken rock. In this article, first, the mechanical research of drill string is carried out; the basic changes of the main mechanical parameters such as the axial force, torque, and bending moment of drill string are clarified; and the dynamic equilibrium equation theory of drill string system is analyzed. According to the similarity criterion, the corresponding relationship between drilling process parameters and laboratory test conditions is determined. Then, the position monitoring test system of the vibration bit is established. The acoustic emission signal and the drilling force signal of the different positions of the bit in the process of vibration rock breaking are collected synchronously by the acoustic emission sensor and the piezoelectric force sensor. Then, the denoised acoustic emission signal and drilling force signal are analyzed and processed. The mean value, variance, and mean square value of the signal are calculated in the time domain. The power spectrum of the signal is analyzed in the frequency domain. The signal is decomposed by wavelet in the time and frequency domains, and the wavelet energy coefficients of each frequency band are extracted. Through the wavelet energy coefficient calculated by the model, combined with the mean, variance, and mean square error of time-domain signal, the position of shallow buried bit can be analyzed and predicted. Finally, by fitting the results of indoor experiment and simulation experiment, it can be seen that the stress–strain curve of rock failure is basically the same, and the error is about 3.5%, which verifies the accuracy of the model.

scholarly journals Artificial neural network based tool wear estimation on dry hard turning processes of AISI4140 steel using coated carbide tool

2017 ◽  
Vol 65 (4) ◽  
pp. 553-559 ◽  
Author(s):  
D. Rajeev ◽  
D. Dinakaran ◽  
S.C.E. Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Tool Wear ◽  
Hard Turning ◽  
Cutting Tool ◽  
Carbide Tool ◽  
Online Tool ◽  
Coated Carbide Tool ◽  
Tool Wear Estimation ◽  
Coated Carbide

AbstractNowadays, finishing operation in hardened steel parts which have wide industrial applications is done by hard turning. Cubic boron nitride (CBN) inserts, which are expensive, are used for hard turning. The cheaper coated carbide tool is seen as a substitute for CBN inserts in the hardness range (45–55 HRC). However, tool wear in a coated carbide tool during hard turning is a significant factor that influences the tolerance of machined surface. An online tool wear estimation system is essential for maintaining the surface quality and minimizing the manufacturing cost. In this investigation, the cutting tool wear estimation using artificial neural network (ANN) is proposed. AISI4140 steel hardened to 47 HRC is used as a work piece and a coated carbide tool is the cutting tool. Experimentation is based on full factorial design (FFD) as per design of experiments. The variations in cutting forces and vibrations are measured during the experimentation. Based on the process parameters and measured parameters an ANN-based tool wear estimator is developed. The wear outputs from the ANN model are then tested. It was observed that as the model using ANN provided quite satisfactory results, and that it can be used for online tool wear estimation.

Audio-Based Condition Monitoring in Milling of the Workpiece Material With the Hardness Variation Using Support Vector Machines and Convolutional Neural Networks

2018 ◽  
Author(s):  
Achyuth Kothuru ◽  
Sai Prasad Nooka ◽  
Rui Liu
Keyword(s):  
Neural Networks ◽  
Support Vector Machines ◽  
Tool Wear ◽  
Support Vector ◽  
Workpiece Material ◽  
Artificial Intelligent ◽  
Tool Condition ◽  
Hardness Variation ◽  
Vector Machines ◽  
Audible Sound

Machining industry has been evolving towards implementation of automation into the process for higher productivity and efficiency. Although many studies have been conducted in the past to develop intelligent monitoring systems in various application scenarios of machining processes, most of them just focused on cutting tools without considering the influence due to the non-uniform hardness of workpiece material. This study develops a compact, reliable, and cost-effective intelligent Tool Condition Monitoring (TCM) model to detect the cutting tool wear in machining of the workpiece material with hardness variation. The generated audible sound signals during the machining process will be analyzed by state of the art artificial intelligent techniques, Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs), to predict the tool condition and the hardness variation of the workpiece. A four-level classification model is developed for the system to detect the tool wear condition based on the width of the flank wear land and hardness variation of the workpiece. The study also involves comparative analysis between two employed artificial intelligent techniques to evaluate the performance of models in predicting the tool wear level condition and workpiece hardness variation. The proposed intelligent models have shown a significant prediction accuracy in detecting the tool wear and from the audible sound into the proposed multi-classification wear class in the end-milling process of non-uniform hardened workpiece.

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