Machining and Machine-tools

Research and Development

J. Paulo Davim, J Paulo Davim

• Publisher: Woodhead Publishing
• Year: 2013
• Print ISBN: 9780857091543
• eBook ISBN: 9780857092199
• Topic: Technology & Engineering
• Subtopic: Mechanical Engineering
• Index: Technology & Engineering

This book is the third in the Woodhead Publishing Reviews: Mechanical Engineering Series, and includes high quality articles (full research articles, review articles and case studies) with a special emphasis on research and development in machining and machine-tools. Machining and machine tools is an important subject with application in several industries. Parts manufactured by other processes often require further operations before the product is ready for application. Traditional machining is the broad term used to describe removal of material from a work piece, and covers chip formation operations including: turning, milling, drilling and grinding. Recently the industrial utilization of non-traditional machining processes such as EDM (electrical discharge machining), LBM (laser-beam machining), AWJM (abrasive water jet machining) and USM (ultrasonic machining) has increased. The performance characteristics of machine tools and the significant development of existing and new processes, and machines, are considered. Nowadays, in Europe, USA, Japan and countries with emerging economies machine tools is a sector with great technological evolution.- Includes high quality articles (full research articles, review articles and cases studies) with a special emphasis on research and development in machining and machine-tools- Considers the performance characteristics of machine tools and the significant development of existing and new processes and machines- Contains subject matter which is significant for many important centres of research and universities worldwide

1

Analysis of acoustic emission signal evolution for monitoring diamond-coated tool delamination wear in machining

P. Lu¹ and Y.K. Chou¹, ¹Mechanical Engineering Department, The University of Alabama, USA.

Abstract:

Diamond-coated cutting tools have been utilized as a cost-effective alternative to brazed polycrystalline diamond tools in applications such as machining lightweight high-strength materials (e.g. metal matrix composites). However, coating delamination is a major failure mode of diamond-coated tools, terminating tool life prematurely. Once delamination failure occurs, the tool substrate often suffers severe abrasive wear, leading to catastrophic tool failures thus interrupting machining operations. Accurate detection and forecasting of coating delamination events may thus prevent production losses and assist process planning. In this study, the characteristics of acoustic emission (AE) signals acquired during machining of an aluminum matrix composite using diamond-coated cutting tools were analyzed in various ways. The AE signals were analyzed in both the time and the frequency domains under various machining conditions and at different cutting times. The results from machining experiments and analysis indicate that it may be feasible to use AE signals to monitor the condition of diamond-coated tools in machining. AE root-mean-squared values decrease considerably once coating delamination occurs. The results also indicate a correlation between tool condition and the fast Fourier transformation (FFT) spectra of AE raw data. The AE-FFT spectra with cutting time generally show a decreased intensity for the low-frequency peaks, but increased intensity for the high-frequency peaks. In addition, AE-FFT analysis of data from various time periods during one cutting pass clearly indicate coating failure transition. Further research using the short-time Fourier transform (STFT) method shows that during the coating failure pass, there is a clear increase in the amplitude ratio (1/value change) of the high- vs. low-frequency component with cutting time, which captures the coating failure transition. Repeatable results indicate that the applied STFT method has the potential for monitoring of diamond-coated tool failure during machining. However, for coating failures associated with less tool wear (flank wear-land width < 0.8 mm), the amplitude ratio plot from the STFT analysis may not clearly identify the failure transition.

Key words

Acoustic emission

delamination

diamond-coated tool

machining

Acknowledgments

This material is based upon work supported by the National Science Foundation (NSF) under Grant No. CMMI 0728228.

1.1 Introduction

Synthetic polycrystalline diamond (PCD) is commonly used in industry to machine non-ferrous materials because of its exceptional tribological properties. However, the processing and fabrication of PCD tools are costly. On the other hand, diamond-coated tools created by chemical vapor deposition (CVD) on carbide substrates have been developed and evaluated in various machining applications (Kustas et al., 1997; Grzesik et al., 2002), for example for machining high-strength Al–Si alloys and aluminum matrix composites. Several previous experimental investigations have shown that coating delamination is the main mechanism that dictates the life of diamond-coated cutting tools (Hu et al., 2008a). Once delamination occurs, tool wear is rapid and can be catastrophic, causing parts to be rejected and possible damage to the machine tool. Moreover, delamination events are typically difficult to predict because of the complexity of the tool wear process. Thus, it is of interest for tool users to be able to detect coating delamination as a means of process monitoring. Among common sensors deployed in machining operations, acoustic emission (AE) sensors have been evaluated to monitor tool wear.

AE refers to the transient elastic waves generated during the rapid release of energy from a localised source within a solid. For metallic materials, acoustic emissions may be associated with plastic deformation, initiation and propagation of cracks, and frictional contacts between the tool, chip, workpiece, etc. Since the late 1960s, AE has been widely used to identify cracks in pressure vessels, bridges, hydroelectric dams, composite laminates, etc. (Beggan et al., 1999). Among the wide range of non-destructive evaluation techniques for detecting cutting tool conditions, AE has been recognized as a feasible method for monitoring of in-process tool wear due to its sensitivity to tool wear (Iwata and Moriwaki, 1977). In machining, AE signals can be easily distinguished from signals associated with machine vibrations and ambient noise because of its high-frequency nature, for example from 10 kHz to 1 MHz (Dornfeld and Lan, 1983).

Dornfeld’s group at the University of California at Berkeley have perhaps been the pioneers in the study of AE signals in cutting and exploring AE applications for monitoring of machining processes. Dornfeld and Kannatey (1980) performed orthogonal cutting tests, varied the process parameters and recorded the AE signals generated. They noted a strong dependence of the AE root-mean-squared (RMS) intensity on both the strain rate and the cutting speed. Moreover, Lan and Dornfeld (1982) reported that the AE power spectrum is of high amplitude at a specific frequency range during tool fracture. In a separate study, the same authors reported that tool fractures and catastrophic failures generate burst AE signals (Dornfeld and Lan, 1983).

It has also been observed that an abrupt transition of AE magnitudes may occur with progression of tool wear (Mukhopadhyay et al., 2006). By contrast, more recently, Feng et al. (2009) analyzed the influence of tool wear on a microgrinding process by analyzing AE signals, and the results showed that AE-RMS signals are not monotonic with tool wear magnitude, and thus may not be suitable for monitoring of tool wear. Haber et al. (2004) claimed that AE-RMS signals are robust and can provide a versatile means of detecting contact between the tool and the workpiece. In addition, it was pointed out that, from spectral analysis of AE signals, AE signals are very sensitive to the tool condition changes, with increasing amplitudes at a high frequency range, up to 160 kHz. More recently, Jemielniak and Arrazola (2008) used AE sensors to monitor tool condition in micromilling and reported that AE signals still show a dependence on tool wear in microscale cutting. Kanga et al. (2008) also applied AE sensors for tool condition monitoring in machining of small-scale parts. The authors argued that AE-RMS values can be used for tool condition monitoring.

Investigations of AE signals for tool wear monitoring of coated tools are relatively scarce, and even fewer liter...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• List of figures and tables
• List of abbreviations
• Preface
• About the contributors
• Chapter 1: Analysis of acoustic emission signal evolution for monitoring diamond-coated tool delamination wear in machining
• Chapter 2: High-performance machining of austenitic stainless steels
• Chapter 3: Forces monitoring in shape grinding of complex parts
• Chapter 4: Optimization of minimum quantity lubrication in grinding with CBN wheels
• Chapter 5: Electrical discharge machining: study on machining characteristics of WC/Co composites
• Chapter 6: Conventional and unconventional hole making in metal matrix composites
• Chapter 7: A laboratory machine for micro electrochemical machining
• Chapter 8: Cam-driven electromagnetic mechanical testing machine
• Index