CHAPTER 2 LITERATURE REVIEW OF HARD TURNING

Transcription

1 6 CHAPTER 2 LITERATURE REVIEW OF HARD TURNING The process of hard turning has been studied theoretically and experimentally for over 40 years (Tonshoff et al 2000). In the late 60s itself, Shaw and Nakayama (1967) studied the machining characteristics of high strength materials such as titanium alloy, beryllium, work hardened steel and refractory materials. While machining these materials, a lower feed and speed were recommended, since they imposed a large cutting force and high cutting temperature. Moreover it was identified that the machining of a hard material required high rigidity and high stability machine tools as they operate at high energy levels. In this chapter, the state of research in hard turning and its monitoring is presented, under three broad headings, namely (i) The science and technology of hard turning (ii) The various monitoring techniques that were developed for the process monitoring of hard turning and lastly (iii) Certain specific acoustic emission based monitoring techniques and signal processing, employed to monitor the conventional turning operations, as it is intended to use these techniques for monitoring hard turning. 2.1 HARD TURNING PROCESS The various studies conducted to technically establish the technology of hard turning and to understand the hard turning process, include studies on developing suitable tool materials, tool geometry and tool edge geometry, and coatings for enhanced tool life, identifying optimal cutting

2 7 conditions, understanding the process of chip formation and identifying the various tool wear mechanisms Development of Suitable Tool Material Cutting tools used for hard turning require high hardness, high compressive strength, high resistance to abrasive wear, thermal resistance and chemical stability at elevated temperatures. Cubic Boron Nitride (CBN) is the preferred cutting tool material rather than poly crystalline diamond (PCD) for machining hardened steel since it has very high hardness and hot hardness and is chemically inert particularly in the presence of carbon absorbing materials. Ceramic inserts are also being used for hard turning. Hodgson and Trendler (1981) carried out hard turning experiments to identify a suitable cutting tool material for machining hardened steel. In this study, they performed the turning tests on three different varieties of hardened steels, such as cold work steels (D2 and D6), HSS steel (M2) using CBN and ceramic cutting tools. The ceramic cutting tool did not perform satisfactorily, while machining cold worked steel (D6) resulting in gross fracture and chipping of the cutting edge. On the other hand, CBN inserts performed better while machining hardened steel, in terms of extended tool life even at higher cutting speeds. Konig et al (1990) conducted hard machining experiments in turning, milling and drilling using CBN tool inserts. In the turning of hard materials, such as high speed steel and bearing steel, they used both the CBN and mixed ceramics, to study the wear behavior and tool life. Even though the CBN and ceramics were tool materials frequently used for machining hardened steel, CBN had emerged a preferred tool material due to the slow and uniform tool wear characteristics, compared to ceramic tool inserts (Figure 2.1). From the surface finish point of view also, the CBN tools were found to perform better.

3 8 Figure 2.1 Tool Wear in Hard Turning for CBN and Mixed Ceramic Tool Inserts (Konig et al., 1990) The performance of the CBN tools depends on the CBN content, grain size, bonding material, and microstructure. In general, CBN tools can be classified into two types. The first type contains very high CBN content with more than 90% by volume of CBN, in which the CBN grains are directly bonded by themselves or with a metallic binder. The second type contains a lower CBN content, in the range of 50 to 70% by volume of CBN in which the CBN grains are bonded by a ceramic binder such as TiC or TiN. Kevin Chou et al (2002) experimentally investigated the wear behavior of low and high CBN tool material. The authors performed hard turning experiments on AISI steel material using high and low CBN cutting tool material. The tool wear examination showed that low CBN content tools generate a better surface finish and have lower flank wear rates than their high CBN counterparts, even though low CBN tool material had inferior mechanical properties compared to a high CBN tool. The performances of the low CBN content tools were found to be better at higher cutting speeds. The depth of cut was also found to have a minor effect on the

4 9 tool wear. The performance of the CBN tool was found to depend not just on the gross mechanical properties of the tool material but the combined effect of gross mechanical property and the microstructure properties of CBN Tool Geometry and Tool Edge Geometry In 1980 s investigations were conducted for developing a suitable tool geometry and tool edge geometry, to improve the tool life of the CBN inserts. Hodgson and Trendler (1981) conducted experiments in hard turning, to study the effect of rake angle and edge preparation of the CBN tools on tool wear. The study showed that the negative rake angle performed better than the positive or zero rake angle from the tool life point of view. Providing a negative rake angle also increased the wedge strength, thereby preventing the chipping of the cutting edge. In hard turning, as the undeformed chip thickness, which is the feed, is very small, the actual cutting is carried out mostly at the chamfered region of the cutting edge. However, a larger negative rake increases the thrust force drastically, and this was shown in the experimental results of Nakayama et al (1988). The influence of the tool nose radius on the surface roughness, cutting forces, tool wear and white layer formation had been systematically investigated by Kevin Chou and Husi song (2004). The authors conducted finish hard turning experiments on hardened AISI steel, using mixed ceramic tool inserts. Inserts with nose radii of 0.8, 1.6 and 2.4 mm were used in the experiments. The experiments revealed that the larger nose radius to contributed to a better surface finish, but resulted in increased cutting forces and increased specific cutting energy. Along with the lower nose radius, worn out tool wear and higher feed rates were also found to have a strong impact on the higher depth of the white layer formation.

5 10 Since hard turning involves higher cutting forces and bigger shock loads, the chamfered cutting edge of CBN insert was used. The chamfered cutting edges had shown much higher tool life than the sharp cutting edge, though they led to an increase in the main cutting forces. Tugur Ozel et al (2005) performed the hard turning experiments on hardened AISI H13 steel, using CBN inserts to study the effect of tool edge geometry, workpiece hardness, feed rate and cutting speed on surface roughness and cutting force. Their objective was to identify suitable edge geometry, for a better surface finish and improved wedge strength. In the study sharp, chamfered and honed edges were used. The honed edges were found to perform much better than the sharp and chamfered edges in terms of surface finish, radial cutting force and tangential cutting force. Lalwani et al (2008) presented the effects of cutting conditions on surface roughness. They have carried out hard turning experiments and processed the data using response surface methodology and face-centered central composite design approach. Their non-linear quadratic model surface roughness showed the feed rate was the primary contributor and interactional effect of feed and depth of cut, cutting speed and depth of cut to be the secondary contributor for affecting the surface roughness of workpiece. Tugrul Ozel et al (2008) introduced a variable edge design on the cutting edge of CBN insert and experimentally studied its influence on cutting force, temperature distribution and tool wear. The results of experimentally obtained cutting forces were used to validate an FEA model. The investigation of the experiments concluded that variable edge tool inserts are better than the normally prepared edge in terms of reduced tool wear, less heat generation and less induced plastic strain on the work material during hard turning.

6 Tool Coating Reginaldo et al (2007) undertook studies to report the wear characteristics of a coated PCBN insert while machining hardened steel. Three different TiN and TiC-based coatings were used on the PCBN, to study the wear behavior and surface finish of the work piece. Finite element simulation model was used to estimate chip-tool interface temperature for studying thermal resistance of the coatings. The results show that the coatings acted as a thermal barrier between the work piece and PCBN tool, resulting in a small reduction at the substrate temperature. Youngsik Choi and Richard Liu (2009) used micro / nano CBN particle coated tungsten carbide tools in hard turning. The coated tools with a CBN grain size of less than 0.5 m were observed to produce more uniform residual compressive stresses on the machined workpiece. This resulted in an enhanced fatigue life of the component Identifying Optimal Cutting Conditions Hard turning is usually carried out at low feeds and depth of cuts with normal cutting speeds without applying any coolant. Tugrul Ozel and Karpat (2005) attempted to predict the surface roughness and tool flank wear using regression and artificial neural network models. The predictive models so developed suggested the surface roughness of the work piece to depend upon primarily, the hardness of work material, edge preparation of insert, feed and cutting speed. The surface finish of the work piece was found to improve by the use of chamfered and honed edges, and with the reduction in cutting speed and feed and with increased hardness in work material. But the tool wear was found to increase with the hardness of the work material.

7 12 Experimental work carried out by Neo et al (2003) and Mohamed Athmane Yallese et al (2009) showed the surface roughness generated in finish hard turning to be mainly influenced by the cutting parameters such as cutting speed, cutting feed and depth of cut. Among these cutting parameters, cutting feed is the dominant factor contributing to the surface roughness. In their experimental study the authors had shown the cutting forces to increase rapidly with increase in feed rate which in turn affected the stability of the machine tool. The study also showed that cutting speed beyond 280 m/min to produce sparks which resulted in very poor surface finish and very rapid tool wear. The authors pointed out that the use of recommended range of cutting speed and cutting feed for better surface quality and for extended tool life. In the hard-turning process, tool geometry and cutting conditions determine the time and cost of production, which ultimately affect the quality of the final product. Optimization of the machining process not only improved the overall machining economics, but also the product quality to a great extent. In this context, Dibag singh et al (2007a) made an effort to estimate the optimum tool geometry and machining conditions using genetic algorithms to get the best possible surface finish within the constraints. A surface roughness model was developed using response surface methodology by incorporating nose radius, effective negative rake angle, cutting speed and feed on machining of bearing steel. The typical optimum machining conditions obtained were 200 m/min cutting speed, 0.1 mm feed, 6 effective rake angle, and 1.2 mm nose radius, leading to a minimum surface roughness value. The cost of manufacturing finished component is directly related machining time and how long the tool can be used for continuous machining with the pre-requisite surface quality. Hence the deeper understanding of tool

8 13 wear behavior, tool life, and surface finish by selecting appropriate cutting condition is the important criteria for the machining of hard material. Therefore the machinability characteristics of work-tool combination are required for all possible combinations and this is truer for the hard machining. Paulo Davim and Luis Figueria (2007) used the orthogonal array experiments to determine the influence of cutting speed, feed and cutting time on the specific pressure and surface roughness and tool flank wear. The authors were carrying out experiments on cold work tool steel D2 (AISI) using ceramic cutting tool. The study showed the tool wear to be primarily influenced by cutting speed and the surface roughness to be influenced by feed and cutting time which is an indirect measure of tool wear. Hard turning is usually performed without any cutting fluid as dry turning. However Vikram Kumar and Ramamoorthy (2007) had conducted experiments with a minimum fluid application technique to result in a sustainable hard turning process. The authors had reported a reduction in cutting force, temperature and surface roughness Cutting Force and Temperature When turning a soft steel material by feeding the cutting tool along the workpiece axial direction, the principal (tangential) cutting force is the largest among the three force components. However, because of the small depth of cut and large nose radius commonly used in hard turning, chip formation takes place only near the nose radius, and on the chamfer or hone of the cutting tool. As a result, the thrust (radial) force component can increase drastically and become the largest force component as the tool wear progresses.

9 14 Figure 2.2 Cutting Geometry and Cutting Forces in Hard Turning (Tonshoff at al 2000) Figure 2.2 shows the typical results of cutting forces during hard turning (Tonshoff 2000). The experimental results clearly show the radial force component (F P ) to be the largest, especially after a long distance of cut. A relatively steep rise in the radial force (F P ) can be observed, compared with the other two force components, due to a progressive increase in the tool flank wear. Young-woo park (2002) had also reported the radial force during hard machining to be the largest among the cutting forces, regardless of cutting conditions and tool material. The wear behavior of the CBN in machining hardened steel is determined by the workpiece hardness and cutting temperature. During hard machining, the energy consumed by the process is largely converted into heat. The generation of heat during machining will increase the temperature in the cutting zone. A higher cutting temperature creates a softening of the work material, but it promotes the accelerated diffusion wear rates on tool material.

10 15 Liu et al (2002) studied the performance of poly crystalline Cubic Boron Nitride (PCBN) tool in finish hard turning of GCr15 bearing steel with different hardness values. They examined the influence of work material hardness and cutting conditions on the cutting temperature and tool wear. A natural thermocouple was used to measure tool temperature during machining. The cutting temperature was found to increase along with the workpiece hardness till the workpiece hardness reached a value of 50 HRC and then as the hardness was further increased the temperature was found to reduce. This was attributed to material softening at elevated temperatures. The increase in cutting speed and feed was also found to increase the tool temperature. Ren et al (2004) had conducted experiments by hard turning the hard-faced high chromium layers using PCBN tool material. The temperature at the tool - shim interface was measured using thermocouple and the temperature at the wear zone was estimated using finite element analysis. The cutting temperature was found increase with increasing cutting speed and feed Mechanism of Chip Formation In hard turning a localized plastic deformation was found to occur at the primary shear zone (Nakayama et al 1988). Since the work material didn t have enough ductility, the crack was initiated at the surface where there was no hydrostatic pressure. This was the primary reason identified for the formation of saw-toothed chips, observed in the cutting experiments of hardened brass materials. The high brittleness of the work material prevented the plastic flow under the high compressive stress and led to the formation of a crack. The crack enabled the release of the pent up energy and provided a sliding surface

11 16 for the segmented material. Simultaneously the plastic deformation and the heating of work material occurred at the vicinity of the cutting edge of the tool, facilitating the sliding of chip segment. This cycle repeated continuously leading to the severely segmented chip formation (Konig et al 1990). Through experimental investigations Shaw et al (1993, 1998) showed that saw toothed, wavy and segmented chips were formed in machining of hardened steel, due to catastrophic shear and plastic flow. Rapid tool wear is the basic difficulty in hard turning and wear significantly affects on the quality of surface finish. Davies et al (1997) conducted the experiments to study the mechanism of segmented chip formation in AISI bearing steel and electro plated nickel-phosphorous on copper substrates using CBN tool with relatively low cutting speed. The average spacing of shear band in the chip during machining was found to increase with the increasing cutting speed and reached a limiting value determined by the cutting feed and depth of cut and work material properties. The authors used a simple one dimensional thermo mechanical model of a continuous homogeneous material getting sheared by an impinging rigid wedge to explain this behavior Tool Wear Mechanism Since rapid tool wear is the basic problem in hard turning, efforts are being made to understand the wear mechanism operating between the tool and work material, in conditions prevailing during hard turning. One major difficulty in turning of hardened steels is the tool-wear caused by the hardness of the material. The wear behavior of the CBN is influenced by many factors, such as the composition of the CBN material, work piece hardness, cutting condition and nature of cutting operation etc. The experimental investigation of Kevin Chou et al (1997) showed the wear rate of the CBN tool to depend

12 17 upon the carbide particle in the work material and size of the CBN grain in tool. The results of the experiments clearly indicated the wear rate to be directly proportional to the size of the carbide particle in the work material and inversely proportional to the size of the CBN grains. Finish hard turning experiments were carried out by Penalva et al (2002) to understand the effect of tool wear on surface roughness. Finish turning was performed on AISI hardened steel using CBN insert. The examination of tool wear profile and surface roughness profiles showed the replication of worn-out cutting edge on the surface of the work piece. The tool wear of a cutting tool is also dependent on the type of hardened steel being machined, namely the physical and chemical interactions between the workpiece material and the tool. Tool and die steels typically contain a variety of carbide particles based on alloying elements like tungsten, molybdenum, vanadium and chromium that can greatly accelerate abrasive wear. Gerard Poulachon et al (2003) carried out hard turning experiments on specimens of hardened steel having different material composition but having same hardness to understand wear behavior of CBN. Dry turning experiments were conducted on four different hardened steels namely X155CrMoV12 cold work steel (AISI D2), X38CrMoV5 (AISI H11) hot work steel, 35NiMo16 hot work steel and 100Cr6 bearing steel (AISI 52100).Their results suggested the various carbides present in steel such as primary carbides M 7 C 3 and secondary carbides M 3 C in the microstructure to influence the tool wear. Higher tool wear rates were attributed to the presence of high quantities of primary carbides in steels. An extensive literature review was presented by Cora Lahiff et al (2007) on the wear modes and wear mechanism of various grades of CBN

13 18 inserts. Tool wear mechanism is a complex phenomena and no single wear mechanism can satisfactorily provide a full explanation. The authors identified two body abrasion caused by hard carbide particles and martensite in the workpiece, three body abrasion caused by the loosened CBN grains, diffusion wear and adhesion because of high temperature to be the predominant wear modes. They also observed the tool wear to depend on the composition CBN content and binder, tool edge geometry and machine tool stability. Yong Huang et al (2005) had modeled the depth of crater wear as a function of cutting temperature, stress and other cutting conditions taking in to consideration tool work material properties. The model predicted the crater depth and this predicted crater depth was validated experimentally. According to the authors modeling of crater depth is important as increased crater depth will weaken the cutting edge leading to micro chipping and catastrophic fracture of the cutting edge. The authors claim this model to help the user to select the cutting conditions and tool manufacturer to optimize the tool geometry for improved wear resistance. Lin et al (2008) in their experimental study on hard turning machined AISI 4340 alloy steel using cubic boron nitride tool. They used CBN inserts having % of CBN and TiC based binder. The experiments were conducted at various cutting speeds in the range of 58 to 180 m/min. In all the experiments the feed rate and depth of cut remained constant at 0.1 mm/min and 0.2 mm respectively. An infrared photography unit was mounted on side of the carriage adjacent to tool tip to measure the temperature of the back side chip. In their experiments cutting forces were measured and tool wear was studied using SEM analysis. During the experiments the authors observed the average cutting temperature to be low at very low cutting speeds and it gradually increased with increase in cutting speed. But the Principal

14 19 cutting force decreased as the cutting speed was increased. In low speed cutting, the binder of the hard particles of the cutting tool was observed to be removed from the substrate due to high cutting force resulting from low cutting temperature Surface Roughness Model Since hard turning is the finishing process, the quality of the surface finish is the most expected outcome of the process. The surface finish in hard turning is influenced by the cutting conditions, tool geometry, edge preparation, tool wear and rigidity of the machine tool. The generation of the surface roughness in turning process has been modeled for several decades. Many researchers had developed theoretical and empirical models to predict the surface roughness (Tugrul Ozel and Karpet 2005 and Lalwani et al 2008). Knuefermann and McKeown (2004) attempted to develop a numerical model of surface roughness in hard turning based on variables such as machine tool vibration, tool geometry, material deformation, chip removal and tool path. In their model, the authors employed the material partition equation for defining chip removal. Cutting experiments were carried out in an ultra precision lathe using CBN tool to compare measured surface roughness with simulated roughness. The analysis of results found the simulated surface roughness to be fairly comparable with measured roughness. 2.2 MONITORING HARD TURNING As hard turning is an advanced manufacturing process close monitoring and control of the process is essential. Tool wear is a complex phenomenon occurring due to different mechanisms and is stochastic. In general, a worn tool affects the surface quality of the work material, and

15 20 therefore there is a need to alert the operator to stop further machining to avoid undesirable consequences. The modern machine tool has many sensing systems as an integral part of it. Using the data from these sensors to obtain useful information about the process is an important domain of current research. One of the major issues that need to be addressed is evolving an online, reliable and robust monitoring methodology, is to predict the process condition in hard turning Monitoring Strategies The basic strategy of process monitoring can be classified into (i) signal based method (ii) Model based method and (iii) classifying method, depending on the complexity of manufacturing process (Tonshoff et al., 2000a) Figure 2.3 Classification of Monitoring methods (Tonshoff et al 2000a) The three approaches are schematically explained in Figure 2.3. Any monitoring system attempts to measure the condition of a machine tool or of the process itself. In the signal based method, the measured signal values are compared with the predefined signal or the ranges of signals. In modelbased monitoring, an empirical model of the process is built, and this is

16 21 compared with actual experimentally measured quantities, to detect any process deviation. In a classifier based monitoring system, a feature vector is identified which is used for subsequent discrimination. Depending on the actual system being monitored, a suitable strategy needs to be identified and used Monitoring using Vibration Signal Figure 2.4 On-Line Roughness Measuring Technique using Cutting Vibration in Hard Turning (Dong Young et al 1996) Dong Young Jang et al (1996) conducted hard turning experiments to understand the correlation between the cutting tool vibration and the surface roughness of the work material in hard turning. The experimental setup is shown in Figure 2.4. The relative vibration between the tool and the work piece was measured using an inductive pickup. This vibration signal was used to predict the surface roughness. The predicted surface roughness was found to correlate well with the actually measured roughness values.

17 22 Thus, the authors had proposed an online surface roughness monitoring system using an inductive vibration sensor Monitoring using Cutting Forces and Acoustic Emission Scheffer et al (2003) proposed a tool wear monitoring system for hard turning using a three-directional cutting force dynamometer, an AE sensor and a temperature sensor. The authors formulated a monitoring methodology using artificial intelligence (AI) techniques for monitoring tool flank and crater wear of CBN insert. Self-Organizing Map analysis was used to classify and isolate some of the common noises present during the process such as floor vibration, work piece clamping and temperature conditions. Zhou et al (2003) measured the cutting force in the radial direction of the workpiece using a Kistler three component dynamometer and found it to be sensitive to tool wear in hard turning process. Figure 2.5 A Proposed ANN Model for Tool Wear Monitoring (Zhou et al 2003)

18 23 In hard turning the radial force is much higher than feed force and the cutting force due to very small cutting depth, very small feed rate and negative rake angle of the tool. From the experiments, the authors observed both frequency and amplitude to show a tendency to increase as the flank wear progressed The authors had developed an ANN based wear prediction model using radial force, frequency energy and accumulated cutting time as inputs The ANN architecture is shown in Figure 2.5. The predicted wear was corrected using tool holder temperature. The predicted wear was observed to be very close to the measured flank wear values. Mehdi Remadna and Rigal (2006) had conducted turning experiments on tempered steel of hardness 52 HRC using CBN inserts and monitored the three component cutting forces. The authors observed the specific cutting force measured along the radial direction of the workpiece to correlate well with the tool wear and surface roughness. An online process monitoring system for hard turning using threecomponent force sensors was developed by Dongfeng Shi and Gindy (2007). Experiments were carried out for machining Inconel 718 using ceramic tools. The static and dynamic components of the forces were measured using multiple sensors and a power sensor was used to trigger the data acquisition. The acquired signal was subsequently processed using wavelet transforms. This signal processing enabled to distinguish between static and dynamic components, and to obtain features of tool malfunctions such as tool wear, tool chipping and tool breakage. The analysis of force signals showed the gradual wear of the cutting tool to be related to the static force components and the transient occurrences like tool chipping got revealed in the dynamic components. Acoustic emission based online tool failure detection system was presented by Liao et al (1995) in face milling of high chromium material

19 24 using PCBN tool insert. The experiments were carried out to extract the failure features from AE count rate, AE peak and AE RMS. During the cutting experiments the authors observed the pattern of AE RMS to change very sharply beyond the point of tool fracture. Further the authors proposed a moving range control chart for online tool failure monitoring in hard milling operation. Guo and Ammula (2005) had attempted to monitor the hard turning process of AISI steel of about 62 HRC turned with CBN inserts using acoustic emission monitoring. The authors had attempted to monitor primarily the surface integrity which was measured in terms of (i) The thickness of the white layer formed and (ii) The surface roughness. The authors had attributed both the increase in the thickness of the white layer formation and the deterioration in the surface roughness to the increased flank wear and the associated increased rubbing between the flank and the workpiece. The authors had measured the conventional Acoustic Emission parameters like AE RMS, frequency and count rate and observed them to correlate well with the thickness of the white layer and surface roughness. The sharp tool, because of low contact area with the workpiece was observed to produce less damping and increased AE RMS. As the tool wears off the contact area between the tool flank and the workpiece was found to increase resulting in relatively increased damping between the two leading to lowering of AE RMS. But when the tool flank wear land crossed the limit the increased rubbing was found to produce highly brittle white layer. The brittle white layer was observed to contribute low damping leading to increased AE RMS values. 2.3 ACOUSTIC EMISSION BASED MONITORING IN TURNING Tool wear in conventional machining can be monitored using various indirect variables that are sensitive to tool wear. The frequently used indirect parameters are the three components of cutting force, Acoustic

20 25 Emission (AE) signal, motor current and vibration Teti et al (2010). Unlike direct methods these methods can be used for on-line tool wear monitoring AE signals are transient elastic strain waves, generated during plastic deformation, and by disturbances occurring at the level of the atomic structure. Plastic deformation, fracture, wear and rubbing are all important sources of acoustic emission signals Sources of AE in Metal Cutting The AE monitoring is one of the most effective methods for process monitoring the conventional metal cutting operations (Dornfeld 1992). The major advantage of using the AE to monitor the machining operation is that, the frequency range of the AE signal is much higher than environmental noise in a non intrusive process. The friction between the cutting tool and workpiece generates a continuous AE signal, which gives rich information on the cutting process. In process monitoring, using acoustic emission, if a specific threshold and band width can be established for a particular process configuration, then the AE signal and AE RMS may be effectively used for monitoring the event of tool wear (Dornfeld and Asibu 1980). The three major sources of the AE signal in metal cutting process are (Dornfeld 1992): (i) Continuous deformation in the primary shear zone and fracture of work material in the secondary, tertiary shear zone. (ii) Fracture of the cutting tool between chip-tool and toolworkpiece interfaces. (iii) Collision, entangling and breakage of chips. An AE signal can be classified into two types, namely continuous and burst AE signal. Continuous signals are generated with shearing in the primary zone and wear on the tool flank. The burst types are observed during crack growth in the material, tool fracture and chip breakage. The importance of AE signal processing is to eliminate unwanted noise and to extract feature

21 26 signals, which can be used to correlate the process parameters. To effectively detect tool wear and fracture of the cutting tool one has to understand the three basic elements of the monitoring system, they are compatibility of the AE signal, AE sensor and the pre amplifier (Krzysztof Jemielniak 2001) AE Based Tool Condition Monitoring A brief review on AE based tool wear monitoring methodology in turning was presented by Xiaoli Li (2002). In his review, the author has presented the state of research on various AE signal processing techniques and feature extraction. The various AE signal processing techniques widely used include time domain analysis, Fast Fourier Transform and wavelet transform. For feature extraction and tool wear estimation, techniques like pattern classification, group method of data handling (GMDH) methodology, fuzzy classifier, and neural network with data fusion technology are being used. Quantifiable characteristics such as (i) ring down count, (ii) AE event, (iii) rise time, (iv) peak amplitude, and (v) RMS voltage can also be used to characterize the wear of the tool insert. According to the author, careful signal processing and integrations with other sensors will be an effective approach for AE-based tool condition monitoring. Dornfeld and Asibu (1980) conducted the turning experiments using HSS cutting tool. They have presented a brief report about the generation of AE during metal cutting process. The objective of the study was to establish the relationship between AE root mean square (AE RMS ) value with the metal cutting parameters such as strain rate, cutting speed and feed in turning operation and to validate this relationship by experimental investigation. The experimental data was presented as plots between AE RMS signal and cutting parameters for two different tool rake angles. They observed the strain rate to be influenced by the cutting speed and this in turn increased RMS voltage. They have also identified the RMS of the AE signal suitable for process monitoring in turning.

22 Traditional quantitative measurement in AE signal The most widely used signal measurement parameters are count, peak amplitude, duration and rise time. The figure2.5 illustrates these parameter under filtered signal envelop. These parameters detected and captured through AE sensor and digital storage oscilloscope in terms of applied voltage with respect to time. The terminology of these terms as explained as follows. Peak amplitude - The peak voltage of AE signal Duration - The time from the first threshold crossing to the end of the last threshold crossing Count - The number of AE signal exceeds threshold Count rate - Number of count per unit time Rise time - The time from the first threshold crossing to the maximum amplitude. Figure 2.6 Characteristics of Acoustic emission signal (Robert and Talebzadeh, 2003)

23 Acoustic Emission Distribution Parameters One of the methods of processing AE signals, is by using parameters like the ring down count and total count (Teti and Micheletti, 1989), as metrics for monitoring tool wear. There are several techniques to extract the features in the time domain AE signal. The commonly applied techniques are (i) using measures of central tendency like the mean, median, mode and root mean square value (ii) using measures of dispersion like standard deviation or variance and (iii) using higher order moments, like the Skew and Kurtosis. In their study on tool wear monitoring in conventional turning, Kannatey-Asibu and Dornfeld (1982) had assumed -distribution to characterize the AE RMS signals and evaluated the central moments and distribution function parameters. The skew is the normalized third order central moment and kurtosis is the fourth order central moment of the assumed -distribution function. in equation (2.1) The probability density function of the AE RMS can be expressed as f (x) r 1 s 1 x (1 x) (r,s) (2.1) Where (r, s) can be expressed as 1 r 1 s 1 (r,s) x (1 x) dx (2.2) 0 The distribution parameters of the given by the equations 2.3 and 2.4. distribution of r and s are

24 r ( ) 2 (2.3) (1 ) 2 2 s ( ) 2 (2.4) The skew indicates the level of symmetry about its mean value and the kurtosis indicates the sharpness of the peak of assumed -distribution. The values of the skew (S B ) and kurtosis (K B ) can be obtained from the equations 2.5 and 2.6 given below. S B 2(s r) r s 1) r s 2 rs 1 2 (2.5) K B 2 6{(r s) (r s 1) rs(r s 2)} rs(r s 2)(r s 3) (2.6) In their study Kannatey-Asibu and Dornfeld (1982) had demonstrated the possibility of using the distribution parameters of AE RMS signal, like skew and kurtosis, for effectively monitoring the tool wear status. As can be seen in Figure 2.6, the skew was observed to decrease as the tool flank wear increased. Also, the kurtosis was observed to increase as the tool flank wear increased (Figure 2.7).

25 30 Figure 2.7 Skew of AE RMS Vs Flank Wear in Conventional Turning (Kannatey-Asibu and Dornfeld 1982) Figure 2.8 Kurtosis of AE RMS Vs Flank Wear in Conventional Turning (Kannatey-Asibu and Dornfeld 1982)

26 31 Though various works had been carried out to monitor the process of hard turning, the quest for a novel sensor monitoring system that is robust, reconfigurable, reliable, intelligent and inexpensive, meeting the demands of advanced manufacturing technology, continues. 2.4 OBJECTIVE AND SCOPE The overall objective of this study is to develop a reliable process monitoring technique for the hard turning process using acoustic emission signals. The hard turning of AISI D3 steel using cubic boron nitride inserts, is taken as the application domain. 2.5 CHAPTER SUMMARY This chapter dealt with the literature review of hard turning process and the various techniques adopted in monitoring it. Specific emphasis was given in studying the influence of the various process parameters on hard turning, mechanism of chip formation and tool wear. Section 2.2 of this chapter dealt with the various monitoring strategies adopted in hard turning. The application of acoustic emission monitoring in conventional turning was also reviewed, as it is very relevant to the present work on monitoring hard turning using acoustic emission signal. Section 2.4 indentified the objective and scope of the study.