Acoustic Emission-Based Tool Condition Classification in a Precision High-Speed Machining of Titanium Alloy: A Machine Learning Approach

2018 ◽  
Vol 17 (03) ◽  
pp. 1850017 ◽  
Author(s):  
P. Krishnakumar ◽  
K. Rameshkumar ◽  
K. I. Ramachandran
Keyword(s):  
Acoustic Emission ◽  
Titanium Alloy ◽  
Tool Wear ◽  
High Speed ◽  
Machining Process ◽  
Support Vector ◽  
Tool Condition ◽  
Wide Range ◽  
Cutting Zone ◽  
Tungsten Carbide Tool

Mechanical and chemical properties of titanium alloy have led to its wide range of applications in aerospace and biomedical industries. The heat generation and its transfer from the cutting zone are critical in machining of titanium alloys. The process of transferring heat from the primary cutting zone is difficult due to poor thermal conductivity of titanium alloy, and it will lead to rapid tool wear and poor surface finish. An effective tool monitoring system is essential to predict such variations during machining process. In this study, using a high-speed precision mill, experiments are conducted under optimum cutting conditions with an objective of maximizing the life of tungsten carbide tool. Tool wear profile is established and tool conditions are arrived on the basis of the surface roughness. Acoustic emission (AE) signals are captured using an AE sensor during machining of titanium alloy. Statistical features are extracted in time and frequency domain. Features that contain rich information about the tool conditions are selected using J48 decision tree (DT) algorithm. Tool condition classification abilities of DT and support vector machines are studied in time and frequency domains.

Tool Wear Identification in Turning Titanium Alloy Based on SVM

2014 ◽  
Vol 800-801 ◽  
pp. 446-450 ◽  
Author(s):  
Zhi Rong Liao ◽  
Sheng Ming Li ◽  
Yong Lu ◽  
Dong Gao
Keyword(s):  
Support Vector Machine ◽  
Acoustic Emission ◽  
Titanium Alloy ◽  
Tool Wear ◽  
Cutting Tool ◽  
Support Vector ◽  
Test Set ◽  
Tool Condition ◽  
Good Classification ◽  
Ae Signal

Titanium alloy is difficult cutting materials,the samples of toolwear features are hard to acquire because of short tool life. In terms of the characteristic, Support Vector Machine (SVM) is proposed in this paper to monitor tool condition, the energy ratio of six different frequency bands of acoustic emission (AE) signal are extracted as cutting tool state features , SVM is trained and tested using these features ,Good classification results were achieved by using test set.

Cryogenic Machining of Titanium Ti-5553 Alloy

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Yusuf Kaynak ◽  
Armin Gharibi
Keyword(s):  
Titanium Alloy ◽  
Tool Wear ◽  
Cutting Temperature ◽  
Cryogenic Cooling ◽  
Machining Process ◽  
Dry Machining ◽  
Cryogenic Machining ◽  
Machining Performance ◽  
Wide Range ◽  
New Generation

Titanium alloy Ti-5Al-5V-3Cr-0.5Fe (Ti-5553) is a new generation of near-beta titanium alloy that is commonly used in the aerospace industry. Machining is one of the manufacturing methods to produce parts that are made of this near-beta alloy. This study presents the machining performance of new generation near-beta alloys, namely, Ti-5553, by focusing on a high-speed cutting process under cryogenic cooling conditions and dry machining. The machining experiments were conducted under a wide range of cutting speeds, including high speeds that used liquid nitrogen (LN2) and carbon dioxide (CO2) as cryogenic coolants. The experimental data on the cutting temperature, tool wear, force components, chip breakability, dimensional accuracy, and surface integrity characteristics are presented and were analyzed to evaluate the machining process of this alloy and resulting surface characteristics. This study shows that cryogenic machining improved the machining performance of the Ti-5553 alloy by substantially reducing the tool wear, cutting temperature, and dimensional deviation of the machined parts. The cryogenic machining also produced shorter chips as compared to dry machining.

Control of Machining Process for Titanium Alloy Based on Green Cooling and Lubricating Technology

2012 ◽  
Vol 580 ◽  
pp. 7-11
Author(s):  
Yue Zhang ◽  
Li Han ◽  
You Jun Zhang ◽  
Xi Chuan Zhang
Keyword(s):  
Titanium Alloy ◽  
Water Vapor ◽  
High Speed ◽  
Cutting Temperature ◽  
Small Diameter ◽  
Machining Process ◽  
Machined Surface ◽  
Cutting Zone ◽  
Cutting Experiments ◽  
Special Control

The machining process of titanium alloys always need special control by using coolant and lubricant as it is one of the difficult-to-cut materials. The cutting experiments are carried out based on green cooling and lubricating technology. To achieve green cutting of titanium alloy Ti-6Al-4V with water vapor cooling and lubricating, a minitype generator is developed. Compared to dry and wet cutting, the using of water vapor decreases the cutting force and the cutting temperature respectively; enhances the machined surface. And it can help to chip forming and breaking. Water vapor application also improves Ti-6Al-4V machinability. The excellent cooling and lubricating action of water vapor could be summarized that water molecule has polarity, small diameter and high speed, can be easily and rapidly to proceed adsorption in the cutting zone. The results indicate that the using of water vapor has the potential to attain the green cutting of titanium alloy.

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

2014 ◽  
Vol 984-985 ◽  
pp. 31-36 ◽  
Author(s):  
A. Gopikrishnan ◽  
A.K. Nizamudheen ◽  
M. Kanthababu
Keyword(s):  
Acoustic Emission ◽  
Titanium Alloy ◽  
Tool Wear ◽  
Condition Monitoring ◽  
Dominant Frequency ◽  
Tool Condition Monitoring ◽  
Discrete Wavelet ◽  
Nose Radius ◽  
Emission Signal ◽  
Tool Condition

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.

scholarly journals Research on Cutting Performance in High-speed Milling of TC11 Titanium Alloy Using Self-propelled Rotary Milling Cutters

2021 ◽  
Author(s):  
Yujiang Lu ◽  
Tao Chen
Keyword(s):  
Titanium Alloy ◽  
Tool Wear ◽  
Surface Quality ◽  
Wear Mechanism ◽  
High Speed ◽  
Cutting Edge ◽  
Machining Process ◽  
High Speed Milling ◽  
Tc11 Titanium Alloy ◽  
Milling Forces

Abstract Titanium alloy materials, with excellent chemical and physical properties, are widely applied to the manufacture of key components in the aerospace industry. Nevertheless, its hard-to-machine characteristic causes various problems in the machining process, such as severe tool wear, difficulty to ensure good surface quality, etc. To achieve high efficiency and quality of machining titanium alloy materials, this paper conducted an experimental research on the high-speed milling of TC11 titanium alloy with self-propelled rotary milling cutters. In the work, the wear mechanism of self-propelled rotary milling cutters was explored, the influence of milling velocity was analyzed on the cutting process, and the variation laws were obtained of milling forces, chip morphology and machined surface quality with the milling length. The results showed that in the early and middle stages of milling, the insert coating peeled off evenly under the joint action of abrasive and adhesive wear mechanisms. As the milling length increased, the dense notches occurred on the cutting edge of the cutter, the wear mechanism converted gradually into fatigue wear, and furthermore coating started peeling off the cutting edge with the occurrence of thermal fatigue cracks on the insert. As the milling length was further extended, the milling forces tended to intensify, the chip deformation worsened, and the obvious cracks occurred at the bottom of chips. Moreover, the rise in milling velocity reduced the tool wear resistance, increased obviously the milling forces and the surface roughness.

scholarly journals Type-2 Fuzzy Modeling for Acoustic Emission Signal in Precision Manufacturing

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Qun Ren ◽  
Luc Baron ◽  
Marek Balazinski
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Fuzzy Modeling ◽  
Machining Process ◽  
Precision Machining ◽  
Precision Manufacturing ◽  
Emission Signal ◽  
Tool Condition ◽  
Ae Signal

This paper presents an application of type-2 fuzzy logic on acoustic emission (AE) signal modeling in precision manufacturing. Type-2 fuzzy modeling is used to identify the AE signal in precision machining. It provides a simple way to arrive at a definite conclusion without understanding the exact physics of the machining process. Moreover, the interval set of the output from the type-2 fuzzy approach assesses the information about the uncertainty in the AE signal, which can be of great value for investigation of tool wear conditions. Experiments show that the development of the AE signal uncertainty trend corresponds to that of the tool wear. Information from the AE uncertainty scheme can be used to make decisions or investigate the tool condition so as to enhance the reliability of tool wear.

Investigating the impact of tool inertia on machinability of a β-titanium alloy using tool deflection and acoustic emission

2018 ◽  
Vol 233 (7) ◽  
pp. 1745-1760 ◽  
Author(s):  
Asif Iqbal ◽  
Dirk Biermann ◽  
Hussein M Ali ◽  
Juliana Zaini ◽  
Maximilian Metzger
Keyword(s):  
Acoustic Emission ◽  
Titanium Alloy ◽  
Energy Consumption ◽  
Tool Wear ◽  
Cutting Speed ◽  
Machining Process ◽  
Material Strength ◽  
Tool Deflection ◽  
Cutting Energy ◽  
Work Material

Finding sustainable ways of machining exotic materials is gaining more and more importance in the manufacturing industry. Application of advanced measuring instruments for quantifying performance measures is a crucial requirement for making machining processes viable. The presented work aims to ameliorate machining of a high-strength β-titanium alloy using information from measurements of key responses, such as cutting energy consumption, tool deflection, and tool damage. Acoustic emission data and tool’s acceleration data are utilized to work out the magnitudes of energy consumed and deflection undergone by the tool, respectively. The article focuses on quantifying the effects of tool’s inertia, strength of work material, and two cutting parameters on the aforementioned responses. A total of 54 continuous cutting experiments are performed in which a fixed volume of material per experimental run is removed. Tool deflection method helped to determine the significant effects of varying tool inertia, work material strength, and cutting speed on the machining process. Likewise, acoustic emission method highlighted the strong effects of material strength and cutting speed caused on the cutting energy consumption. The effect of feed rate is found to be significant regarding tool wear only. Finally, the tool wear data are tested for correlation against the corresponding data sets of the other two responses. It is found that both tool deflection and cutting energy possess strong uphill relationships with tool wear.

Machining Process of Titanium Alloy Based on Green Cooling and Lubricating Technology

2010 ◽  
Vol 139-141 ◽  
pp. 681-684
Author(s):  
Yue Zhang ◽  
Li Han ◽  
Qi Dong Li ◽  
Tai Li Sun ◽  
Xi Chuan Zhang
Keyword(s):  
Titanium Alloy ◽  
Water Vapor ◽  
High Speed ◽  
Cutting Temperature ◽  
Small Diameter ◽  
Machining Process ◽  
Machined Surface ◽  
Surface Appearance ◽  
Cutting Zone ◽  
Special Control

The machining process of titanium alloys always need special control by using coolant and lubricant as it is one of the difficult-to-cut materials. To achieve green cutting of titanium alloy Ti-6Al-4V with water vapor cooling and lubricating, a minitype generator is developed. Compared to dry and wet cutting, the using of water vapor decreases the cutting force and the cutting temperature respectively; enhances the machined surface appearance. Water vapor application also improves Ti-6Al-4V machinability. The excellent cooling and lubricating action of water vapor could be summarized that water molecule has polarity, small diameter and high speed, can be easily and rapidly to proceed adsorption in the cutting zone. The results indicate that the using of water vapor has the potential to attain the green cutting of titanium alloy instead of cutting floods.

A Fracture Mechanics Approach to the Prediction of Tool Wear in Dry High Speed Machining of Aluminum Cast Alloys: Part 2 — Model Calibration and Verification

Tribology ◽  
2005 ◽  
Author(s):  
Alexander Bardetsky ◽  
Helmi Attia ◽  
Mohamed Elbestawi
Keyword(s):  
Fracture Mechanics ◽  
Tool Wear ◽  
Model Calibration ◽  
High Speed ◽  
Cutting Conditions ◽  
Depth Of Cut ◽  
High Speed Machining ◽  
Wear Model ◽  
Wide Range ◽  
Cast Alloys

Experimental study has been carried out to establish the effect of cutting conditions (speed, feed, and depth of cut) on the cutting forces and time variation of carbide tool wear data in high-speed machining (face milling) of Al-Si cast alloys that are commonly used in the automotive industry. The experimental setup and force measurement system are described. The test results are used to calibrate and validate the fracture mechanics-based tool wear model developed in Part 1 of this work. The model calibration is conducted for two combinations of cutting speed and a feed rate, which represent a lower and upper limit of the range of cutting conditions. The calibrated model is then validated for a wide range of cutting conditions. This validation is performed by comparing the experimental tool wear data with the tool wear predicted by calibrated cutting tool wear model. The prediction errors were found to be less then 7%, demonstrating the accuracy of the object oriented finite element (OOFE) modeling of the crack propagation process in the cobalt binder. It also demonstrates its capability in capturing the physics of the wear process. This is attributed to the fact that the OOF model incorporates the real microstructure of the tool material.

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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