Wear monitoring of single point cutting tool using acoustic emission techniques

Transcription

1 Sādhanā Vol. 38, Part 2, April 2013, pp c Indian Academy of Sciences Wear monitoring of single point cutting tool using acoustic emission techniques P KULANDAIVELU 1,2,, P SENTHIL KUMAR 3 and S SUNDARAM 2,4, 1 Anna University, Chennai , India 2 KS Rangasamy College of Technology, Tiruchengode , India 3 Mechanical Engineering Department, KSR College of Engineering, Tiruchengode , India 4 Vidyaa Vikas College of Engineering and Technology, Mechanical Engineering Department, Tiruchengode , India sundaram1160@gmail.com MS received 13 April 2009; revised 28 August 2011; accepted 3 February 2012 Abstract. This paper examines the flank and crater wear characteristics of coated carbide tool inserts during dry turning of steel workpieces. A brief review of tool wear mechanisms is presented together with new evidence showing that wear of the TiC layer on both flank and rake faces is dominated by discrete plastic deformation, which causes the coating to be worn through to the underlying carbide substrate when machining at high cutting speeds and feed rates. Wear also occurs as a result of abrasion, as well as cracking and attrition, with the latter leading to the wearing through the coating on the rake face under low speed conditions. When moderate speeds and feeds are used, the coating remains intact throughout the duration of testing. Wear mechanism maps linking the observed wear mechanisms to machining conditions are presented for the first time. These maps demonstrate clearly that transitions from one dominant wear mechanism to another may be related to variations in measured tool wear rates. Comparisons of the present wear maps with similar maps for uncoated carbide tools show that TiC coatings dramatically expand the range of machining conditions under which acceptable rates of tool wear might be experienced. However, the extent of improvement brought about by the coatings depends strongly on the cutting conditions, with the greatest benefits being seen at higher cutting speeds and feed rates. Among these methods, tool condition monitoring using Acoustic Techniques (AET) is an emerging one. Hence, the present work was carried out to study the stability, applicability and relative sensitivity of AET in tool condition monitoring in turning. Keywords. Acoustic emission techniques; flank wear; crater wear; stress wave. For correspondence 211

2 212 P Kulandaivelu et al 1. Introduction Kandasami (1998), has shown that the Acoustic Emission Technique (AET) is relatively recent entry in the field of Non-Destructive Evaluation (NDE) which has particularly exhibited a very high potential for material characterization and damage assessment in conventional as well as non-conventional materials. Due to its complementary non-destructive evaluation methods, it is utilized for a wide range of applications. Acoustic Emission (AE) is defined as the class of phenomenon where transient elastic waves are generated by the rapid release of energy from localized sources within a material, or the transient elastic waves so generated. In other words, AE refers to the stress waves generated by dynamic processes in materials. Emission occurs as a release of a series of short impulsive energy packets Lidan & Mathew (1990). The energy thus released travels as a spherical wave front and can be picked from the surface of a material using highly sensitive transducers, (usually electro mechanical type). The picked energy is converted into electrical signal, which on suitable processing and analysis can reveal valuable information about the source causing the energy release (Viktor 2003). The present global industrial scenario is to produce quality products at competitive price. This is possible with the increased productivity aimed at zero error. To achieve this, industries are steering towards unmanned factory where human error is reduced to a great extent. An essential part of a machining system in the unmanned factory is the ability to change out tools automatically due to wear or damage. Hotton & Qinghuan (1999) andjianget al (1996) have shown that the tool failure contributes on an average, up to 7% to the downtime of machining centers. They concluded that most of the tools fail either fracturing or gradual wear. Inasaki (1998) and Iwata &Moriwaki(1976) stated that even through more methods have been developed to monitor tool wear; none of them has achieved significant use in industry. A study by showed that the AE parameters did not exhibit a definite trend with tool wear but rather a general random behaviour with sudden variations related in the process deterioration phenomena. Hence, the present work was carried out to study the wear monitoring in single point cutting tool using acoustic emission techniques. 2. Propagation of stress wave due to crater wear and flank wear Figure 1 show the crater wear occurred on the rake face of the tool. This crater wear emits stress wave, which propagates as spherical wave front and becomes surface wave on the rack face of the tool and travels on the top surface of the tool holder. Hence, the suitable position of the sensor Figure 1. Propagation of a stress wave due to crater wear.

3 Wear monitoring of single point cutting tool 213 Figure 2. Propagation of a stress wave due to flank wear. to capture AE due to crater wear is the top surface of the tool holder as shown in figure 1 (Byrne et al 1995 and Rao 1995). Figure 2 shows the formation of flank wear on the flank face of the tool. This flank wear emits stress wave, which propagates as a spherical wave front and becomes surface wave on the flank face of the tool and travels along the side surface of the tool holder. Hence, the suitable position of sensor to capture AE due to the flank wear is the side surface of the tool holder as shown in figure 2 (Byrne et al 1995 and Rao 1995). Figure 3. Types of tool wear.

4 214 P Kulandaivelu et al Flank wear and crater wear are shown in figure 3. Flank wear is attributed usually due to the following reasons. (i) Abrasion by hard particles and inclusions in the work piece. (ii) Shearing of the micro weld between the tool and the work material. (iii) Abrasion by fragments of built-up edge. Crater wear usually occurs due to, (i) severe abrasion between the chip and the tool face. (ii) High temperature in the tool chip interface reaching the softening or melting temperature of the tool, resulting in the increased rate of wear. (iii) The sharp increase in wear rate after the interface temperature reaches the diffusion temperature of the material. Diffusion is the movement of atoms between the tool and the chip materials resulting in loss of material from the face of the tool. Also, the AE sensors are selected in such way that the signals will not be affected due to noise. It was proved by Byrne et al 1995 and Rao Applications of acoustic emission 2.1a Power plants: To identify the leaks from steam lines, pre-screening of crack inspection in nuclear power plants and the bearing failure studies in hydraulic and steam turbines. 2.1b Chemical/petrochemical/fertilizer industries: Continuous process monitoring of gaseous, liquid and molten fluids in process industries. 2.1c Aerospace industries: The acoustic emission technique is used to monitor the life of aircraft structure. 2.1d Metal fabrication: The acoustic emission technique is used as like other NDT techniques in metal fabrication works. 2.1e Metal cutting: On line tool wear monitoring applications in lathe, drilling, grinding and milling operations. 2.1f Transportation: This technique is widely applied for monitoring the vibration and bearing noise in transport vehicles. In addition to these applications, AE also finds application in the field of medical sciences (the acoustic emission signal from human joints to indicate the grade levels of joint degradation), off-shore structure, metallurgical research and in the study of modern materials such as ceramics and composites. 3. Details of experimental set-up Experimental set-up includes lathe, work piece, cutting tool, AE sensors, couplants, AE signal storing and processing instrument, etc. These were suitably selected to suite the requirements. 3.1 Work material Frequently used material was selected to make the research work as application oriented. Also, harder material was preferred to have the faster rate of tool wear which would reduce the number

5 Wear monitoring of single point cutting tool 215 of observations required. To approach these points in mind C45 steel of 270 BHN was chosen. The C45 steel composition and all its properties are given below. 1. Chemical properties (compositions - % of wt.). Carbon Silicon Manganese Sulphur Phosphorus Nickel Chromium Molybdenum (C) % (Si) % (Mn) % (S) % (P) % (Ni) % (Cr) % (Mo) % < <0.045 <0.045 <0.040 <0.040 < Mechanical properties Tensile strength, ultimate 800 MPa Tensile strength, yield 400 MPa Elongation at break 15% Reduction in area 16% Modulus of elasticity 210 GPa Hardness 270 BHN 3. Physical properties Density 7850 Kg/m 3 Appearance and odour Odourless gray metallic solid. Available in ingots, mill products, castings, sponge, chips, briquettes and other irregular shapes. 4. Thermal properties Specific heat 500 J/kg-K Thermal conductivity 46 W/m-K Melting point 1813 K (1540 C) Maximum service 673 K (400 C) temperature, air 3.2 Cutting tool Coated carbide tool was selected based on its wider application. To have faster flank wear, rough turning grade of TK35 was chosen. Further the tool holder was selected to provide zero clearance angles to accelerate flank wear. 3.3 Selection of cutting conditions The following cutting conditions were selected based on the recommended cutting speed range of m/minute for the selected work and cutting tool material. (a) Cutting velocity (b) Feed (c) Depth of cut (d) Type of coolant 160 m/min mm/rev. 1.5 mm/cut. No coolant [dry condition]

6 216 P Kulandaivelu et al 3.4 Selection of lathe The power required for the given cutting condition, the work material and cutting tool combination were calculated using the following formula and accordingly a 5 HP all geared lathe was chosen. P = Vc f d P s , η where P = Power (kw), Vc = Cutting speed (m/min.), f = Feed (mm/rev.), d = Depth of cut (mm), P s = Specific cutting force (200 kg/mm 2 for C45 Steel), η = Efficiency of the machine (0.75) (Taken from hmt Production Technology Book) P = = 3.33 kw = HP. 3.5 AE sensors and pre-amplifiers with filters As the sensors and pre-amplifiers with filters were chosen as given below. a) AE sensor Make FAC 500 S.No Sensor element Piezo-electric crystal Operating frequency range 125 khz 2 MHz b) Pre-amplifier with filters Make Physical acoustic corporation Model 140 B; gain 40 db Operating voltage +15 V Filter 125 khz - high pass The schematic experimental arrangement is shown in figure 4. The preferred work material was cleaned and set on the lathe. Also the selected cutting tool along with the tool holder was fixed. The AE sensors for flank wear and crater wear were placed in the respective positions, as explained above, after cleaning the surface and applying the couplants. The sensors were fixed by using epoxy resin couplant. These sensors output signals were fed to the digitals to storage oscilloscope via pre-amplifier, filter and power supply unit. The stored signals were processed off-line through a computer using AUTODASP software. 4. Experimental procedure The details of the procedures of the experimental work carried out are presented below. (i) The machine was set to the selected cutting conditions.

7 Wear monitoring of single point cutting tool 217 Figure 4. Schematic AET diagram for crater and flank wear. (ii) The oscilloscopes were set in Auto Arm mode to receive and store 15 frames of signals automatically. As the machining interval was decided as 30 seconds duration, each signal frame was of 2 seconds duration. (iii) The machine was started and simultaneously the oscilloscopes were armed to capture the AE signals generated due to crater and flank wear. (iv) The Machine was stopped at the end of 30 seconds and the oscilloscopes were capturing and storing AE signals generated due to both wears in 15 frames of 2 seconds during this 30 seconds interval of metal cutting. (v) Tool was taken out from the tool holder and cleaned with carbon tetrachloride. (vi) AE signals stored in the oscilloscopes due to both wears were transferred to a computer through RS 423 serial interface for analysis at a later stage. (vii) The tool was again fixed to the tool holder with the same cutting edge in cutting position. (viii) The step from 1 to 7 was repeated for next observation. Like this, 40 observations were noted, which means the experiment was carried out for 20 min and 800 AE waveforms of each 2 sec duration were captured separately for the crater wear and the flank wear. The captured signals were processed using AUTO DASP software. The AE signals in the oscilloscope screen and the same in the monitor of computer when transferred respectively. Blum & Inasaki (1990) have stated in their research investigations that a wide variety of measurement s can be carried out according to the type of AE signal, the experimental instrumentation used and the personal preferences of the individual research workers. One of the most significant methods to analyse AE is the measurement of the energy content of the AE signals. The rate with which the energy is transmitted by the signal can be directly correlated with the rate of energy generation by the original AE source. A simple way to measure such energy is the evaluation of the RMS voltage of the signal. Hence, in this work, AE parameters like the average value of the signal (mv), RMS value of the signal (mv) and area or energy of the signal (mv.s) were found using AUTO DASP software. In this experimental work, AE released due to wear, observed at each intervals of duration of 30 seconds was stored in 15 frames and so, 15 values of AE parameters were found. Therefore, the means of these values were calculated and given as mean AE.

8 218 P Kulandaivelu et al 5. Verification of AE experimental work on TCM The AE parameters against crater wear were experimentally studied. The AE parameters studied are Cumulative mean area (mv.s), Cumulative mean RMS value (mv), and Cumulative mean average value (mv). The mean area is area under the acoustic emission wave form. The RMS value is the root mean square value of the AE signal is a measure of the signal intensity. For characterizing continuous emission usually RMS value of the signal is used. The average value of the acoustic emission data captured during the experimentation was also studied in this research work. The observations have been analysed for correlations between the tool wear and the AE parameters and the results are presented in the following sections. 5.1 Crater wear Vs AE signal Figure 5 shows the variation in crater wear with time. It clearly indicates that the three stages of wear viz., Stage I, where the rate of wear is low, Stage II, where the rate of wear is moderate and Stage III, where the rate of wear is faster and leading to the termination of tool life. In stage I, 0 to 8.5 min of the machining operation the tool wear is in the range of μm only. But in the stage II and Stage III the crater wear increases with increase in machining time and the tool fails at about 20 min from the starting of the machining operation. 5.2 Crater wear Vs cumulative mean AE parameters The variation of crater wear with mean AE Parameters (mean average value, mean RMS value and mean area) is shown in figure 6. This reflects the random variations of mean AE parameters such as, mean average value, the mean RMS value and the mean area of the AE signal. The behaviour of random variation is due to cumulative crater wear, whereas AE parameters are due to release of AE signals at that instant. The variation of cumulative mean AE parameters with crater wear is shown in figure 7. Itis observed that the three stages of crater wear occurred when cumulative AE parameters plotted against the crater wear. Thus, by noting these limiting values of cumulative mean AE parameters at these transition stages, the state of crater wear can be monitored. This is presented in table 1. Figure 5. Variation of crater wear with time.

9 Wear monitoring of single point cutting tool 219 Figure 6. Mean AE parameters with crater wear. Table 1 shows that the crater wear in stage I occurs if the cumulative mean average value, mean RMS value and mean area of AE signal are in the range of mv, mv and mv.s, respectively. The same in stage II, varies in the range of 413 to 708 mv, 623 to 1030 mv and 995 to 1661 mvs and in Stage III, the respective cumulative mean AE parameters are above 708 mv, above 1030 mv and above 1661 mvs, respectively. 5.3 Time Vs cumulative mean AE parameters for crater wear Figure 8 shows the variation of cumulative mean AE parameters with cutting time. Like crater wear Vs cumulative mean AE parameters, this plot does not clearly identify the three stages of wear, but there is still minor identification of the stages as marked by arrows in figure 8, atthe corresponding transitions in the curve. However, unlike the transitions in the crater wear Vs cumulative mean AE parameters curve (figure 7), these transition points slightly differ from that of the wear curve (figure 6). First Figure 7. Crater wear with cumulative mean AE parameters.

10 220 P Kulandaivelu et al Table 1. Trend of crater wear with cumulative mean AE parameters. Stages of crater wear Stage I Stage II Stage III Crater wear (μm) Above 19.6 Time (min) Above 16 Cumulative mean average value (mv) Above 708 Cumulative mean RMS value (mv) Above 1030 Cumulative mean area (mvs) Above 1661 transition point coincided with the wear curve, but the second one occurred at the 14 th min instead of the 16 th min. Even then, these transitions in the Time Vs cumulative mean AE parameters curve are also suggested as a measure to monitor the stage of crater wear due to the following reasons. In crater wear Vs cumulative mean AE parameters, it is necessary to measure the wear initially to study the trend, whereas in time Vs cumulative mean AE parameters, without any addiction on the wear, this curve can be plotted during the process and the transitions in the curve can be monitored to identify the stages of wear. 5.4 Crater wear Vs AE signal types AE signals due to crater wear were studied for their types. AE signals are of two types. They are burst type and continuous emission signals. If the signals can be identified as individual bursts with a definite time gap between two successive events, then they are called burst type signals. If the burst rate is very high, then the events occur very close to one another and overlapping occurs. In this case the signals are called continuous emission signals. In addition, it was seen that the burst type signals contained much variation in the amplitude level when compared to continuous emission. Hence, in order to characterize the changes in AE signal at various stages of crater wear, the coefficient of variations of RMS values of the signals were found. The coefficient of variation is defined as the ratio of the standard deviation to the Figure 8. Variation of cumulative mean AE parameters with time due to crater wear.

11 Wear monitoring of single point cutting tool 221 Figure 9. Crater wear with coefficient of variation of RMS value. mean value. This coefficient of variation would be of larger value for burst type AE signals when compared to continuous emission signals. Figure 9 shows plot between crater wear and coefficient of variation of RMS value. It has been observed that the coefficient of variations of RMS values is high when the crater wear is in Stage I, they are of low value when the wear is in Stage II and the lowest when the wear is in Stage III. This reveals that the AE signals are burst type in Stage I, continuous emission type in Stage II and Stage III. The research findings are further confirmed by noting the patterns of the signals in these three stages of wear, which are presented in figures 10, 11 and 12, respectively. These figures clearly show that the signals contain many variations in amplitude level in the first two stages of wear, whereas the variation in amplitude level is less in the third stage of wear. Figure 10. AE wave form captured crater wear at stage I (5 min and 50 s after start of machining).

12 222 P Kulandaivelu et al Figure 11. AE wave form captured crater wear at stage II (11 min and 20 s after start of machining). The reason for this change in the type of signals can be attributed to the rate of wear. The rate of crater wear is slow in the first stage and moderate in the second stage. Hence the acoustic emission due to wear occurs with definite time gap between each emission and so, the signals are of burst type in these stages of wear. In the final or terminal stage of wear, the wear rate is faster loading to successive emissions without any time gap between the events and overlapping one over the other. Consequently, the final (third) stage of wear emits continuous emission. Online tool condition monitoring is suggested to monitor the coefficient of variation of RMS value of the signal. In this case, the crater wear would be in Stage I, Stage II and Stage III, when the coefficient of variation of RMS value is above and up to 0.061, below and up to and below 0.042, respectively. Figure 12. AE wave form captured crater wear at stage III (16 min and 25 s after start of machining).

13 Wear monitoring of single point cutting tool 223 Figure 13. Variation of flank wear with time. 5.5 Relative sensitivities of AE parameters in monitoring crater wear A close study of the slopes of the curves in figure 7, which shows the variation of cumulative mean AE parameter with respect to crater wear, reveals that the relative sensitivities of the AE parameters in monitoring the crater wear. The exposure of this study is that the sensitivities of the AE parameters decrease in the order given below in monitoring crater wear. (i) Cumulative mean area (mvs). (ii) Cumulative mean RMS value (mv). (iii) Cumulative mean average value (mv). 5.6 Flank wear Vs AE signals The AE parameters against flank wear were experimentally studied. The observations have been analysed for correlations between the tool wear and the AE parameters and the results are presented in the following sections. Figure 13 shows the variation of flank wear with respect to time. As explained in crater wear, this also has three stages of wear rate by means of stage I indicating low wear, stage II indicating moderate wear and stage III indicating faster wear. Figure 14. Flank wear with mean AE parameters.

14 224 P Kulandaivelu et al Figure 15. Flank wear with cumulative mean AE parameters. 5.7 Flank wear Vs cumulative mean AE parameters Figure14 shows the plot between flank wear and mean AE parameters using mean average value; mean RMS value and mean area. This reveals, the random variations of mean AE parameters with respect to the flank wear. This study can be used for the measurement of flank wear. The flank wear measured was cumulative one and not the instantaneous one, whereas the AE parameters measured were due to the release of AE at the particular instant. So consequently, the cumulative mean AE parameters were found for the corresponding flank wear. The correlation between flank wear and the cumulative mean AE parameters is shown in figure 15. Comparison of figures 13 and 15 reveals the cumulative mean AE parameters, when plotted against flank wear, shows transitions similar to the curve in figure 14. Hence, here also, as suggested for crater wear, the values of cumulative mean AE parameters at the transitions are suggested as limiting values to monitor the stages of flank wear. These are presented in table 2. Table 2 indicates that the cumulative mean average value, mean RMS value and mean area of AE signal are between 0 and 450 mv, 0 to 690 mv and 0 to 1100 mvs in stage I, where the tool wear is low. In stage II, where the tool wear is moderate, the respective cumulative mean AE parameters lie in the range of 450 to 750 mv, 690 to1150 mv and 1100 to 1880 mvs, respectively. In sage III, due to faster tool wear the respective cumulative mean AE parameters are above 750 mv, 1150 mv and 1880 mvs, respectively. 5.8 Time Vs cumulative mean AE parameters for flank wear Figure 16 shows the variation of cumulative mean AE parameters with cutting time. Here, the change occurs at the sixth minute instead of the eighth minute, whereas the second change point coincides with the crater wear curve. Therefore, the flank wear of the tool during the Table 2. Trend of flank wear Vs cumulative mean AE parameters. Stages of flank wear Stage I Stage II Stage III Flank wear (mm) Above 0.8 Time (min) Above 14 Cumulative mean average value (mv) Above 750 Cumulative mean RMS value (mv) Above 1150 Cumulative mean area (mvs) Above 1880

15 Wear monitoring of single point cutting tool 225 Figure 16. Time with cumulative mean AE parameters due to flank wear. machining operation can also be determined using the same procedure followed for crater wear identification. 5.9 Flank wear Vs AE signal types In order to characterize the changes in AE signal at various stages of flank wear, the coefficient of variations of RMS value of signals were found. Figure 17 shows the plot between flank wear and coefficient of variations of RMS value. On observation of this plot, it has been noted that the variation of coefficient of RMS values are similar to the crater wear. Here also, the coefficient of variations of RMS value is high when the flank wear is in Stage I, they are of low value when the wear is in Stage II and the lowest when the wear is in Stage III. This reveals that the AE signals are burst type in Stage I, and continuous emission type in Stage II and Stage III. These findings are further confirmed by noting the patterns of the signals in these three stages of flank wear which are presented in figures 18, 19 and 20, respectively. These figures clearly show that the signals contain much variation in the amplitude level in the first two stages of wear, whereas the variation in amplitude level is less in the third stage of Figure 17. Flank wear with coefficient of variation of RMS value.

16 226 P Kulandaivelu et al Figure 18. AE wave form captured flank wear at stage I (3 min and 50 s after start of cutting). wear. The reasons for this change in the type of signals can be recognized to the rate of wear as explained for crater wear. Like crater wear, the online tool condition monitoring is suggested for flank wear also, to monitor the variation of RMS values. The coefficient of variation of RMS value during flank wear in stage I raises up to The same in stage II varies from 0.02 to Also, in stage III the coefficient of variation of RMS value varies from to This shows that the coefficient of RMS value is proportional to the AE signal captured during the machining operation Relative sensitivities of AE parameters in monitoring flank wear Here also the close observation of the figure 15, which is the plot between cumulative mean AE parameters and flank wear, reveals that the sensitivities of the AE parameters decrease in the Figure 19. AE wave form captured flank wear at stage II (7 min and 35 s after start of cutting).

17 Wear monitoring of single point cutting tool 227 Figure 20. AE wave form captured flank wear at stage III (13 min and 25 s after start of cutting). order given in monitoring flank wear (i) Cumulative mean area (mvs). (ii) Cumulative RMS value (mv). (iii) Cumulative average value (mv). 6. Verification of repeatability of tool condition monitoring through AET The experimental observations were analysed in the same way as explained in pervious section for both crater wear and flank wear to verify the repeatability of the research findings. 6.1 Crater wear Vs AE signal Figure 21 shows the variation of crater wear for initial and repeated experiments. The stage I of crater wear ends at 8.5 min and 9.5 min for initial and repeated experiments, respectively. Figure 21. Time with crater wear of initial and repeated experiments.

18 228 P Kulandaivelu et al Figure 22. Crater wear with mean AE parameters of repeated experiments. Figure 23. Crater wear with cumulative mean average value of initial and repeated experiments. Figure 24. Crater wear with cumulative mean RMS value of initial and repeated experiments.

19 Wear monitoring of single point cutting tool 229 Figure 25. Crater wear with cumulative mean area of initial and repeated experiments. Table 3. Comparison of limiting values of mean AE parameters with stage I crater wear for initial and repeated experiment. Crater wear at the end of Limiting values of cumulative mean AE parameters Experiment Time (min) stage I (μm) Area (mvs) RMS value (mv) Average value (mv) Initial experiment Repeated experiment Variation 1.74% 2.86% 2% Figure 26. AE wave form captured due to stage I crater wear of repeated experiment (2 min and 30 s after start of cutting).

20 230 P Kulandaivelu et al Figure 27. Time with flank wear of initial and repeated experiment. Figure 28. Flank wear with AE parameters of repeated experiment. Figure 29. Flank wear with mean average value of initial and repeated experiment.

21 Wear monitoring of single point cutting tool 231 Figure 30. Flank wear with cumulative mean RMS value of initial and repeated experiment. The instantaneous values of AE parameters show the randomness with respect to crater wear in the repeated experiments and shown in figure 22. The cumulative mean AE parameters of both repeated and initial experiments with respect to the crater wear are plotted in figures 23, 24,and25. In figures 23, 24 and 25, the cumulative mean AE parameters show the repeatability in indicating stages of crater wear when the cutting conditions are kept constant. From these curves, the limiting values of mean AE parameters for stage I crater wear for both initial and repeated experiments, were compared and the values are presented in table 3. This comparison reveals that the cumulative mean AE parameters show the repeatability in indicating the stages of crater wear within ± 3%. Among these AE parameters, cumulative mean area is good in sensing and is also having the repeatability with a variation of 1.8%. Also, the AE signal type observed in stage I crater wears of repeated experiment was of burst type. This AE signal is shown in figure 26. Figure 31. Flank wear with cumulative mean area value of initial and repeated experiment.

22 232 P Kulandaivelu et al Table 4. Comparison of limiting values of mean AE parameters with Stage I flank wear for initial and repeated experiment. Flank wear at the end of Limiting values of cumulative mean AE parameters Experiment Time (min) stage I (mm) Area (mvs) RMS value (mv) Average value (mv) Initial experiment Repeated experiment Variation 1.82% 4.35% 1.01 % 6.2 Flank wear Vs AE signal Figure 27 shows the variation of flank wear for initial and repeated experiments. The stage I of flank wear ends at 8.5 minutes and 9.5 minutes for initial and repeated experiments, respectively. Like the initial experiments, the instantaneous values of AE parameters vary randomly with respect to flank wear for the repeated experiment also and it is shown in figure 28. The cumulative mean AE parameters of repeated and initial experiments with respect to the flank wear are presented in figures 29, 30 and 31. These AE curves clearly prove that the cumulative mean AE parameters show the repeatability indicating stages of flank wear when the cutting conditions are kept constant. From these curves, the limiting values of mean AE parameters for stage I flank wear for initial and repeated experiments are compared and the findings are presented in table 4. This comparison reveals that the cumulative mean AE parameters show the repeatability in indicating the stages of flank wear within ±5%. Among these AE parameters, the cumulative mean area is having the repeatability with a variation of 1.9%. Also, the AE signal types observed in stage I flank wear of repeated experiments were of burst type, conforming to the findings. This AE signal is shown in figure 32. Figure 32. AE wave form captured due to stage I flank wear of repeated experiment (1 min and 30 s after start of cutting).

23 Wear monitoring of single point cutting tool 233 Figures 21, 23, 24, 25, 27, 29, 30 and 31 indicate the difference between the initial and the repeated experimental data and found that the results are reproducible. Therefore, the system developed in this research work is a reliable one. 7. Conclusion The summary of the extensive experimental research work on tool condition monitoring through AET is presented below. (i) The discrimination of flank and crater wear on AE and also enhancing the individual effects can be achieved by positioning the sensor for crater wear on the top surface of the tool holder and the sensor for flank wear on the side surface of the tool holder, adjacent to flank face. This has been proved experimentally. (ii) Tool condition monitoring through AET has significant effect above 200 khz (iii) Crater wear and flank wear stages can be monitored by (a) Observing cumulative mean values of AE parameters like area, RMS value and average value. (b) Noting the transitions in the time vs. cumulative AE parameters plot and (c) Monitoring the signal type (or) co efficient of variation of RMS value. (d) The sensitivities of AE parameters in monitoring tool wear decrease in the order: cumulative mean area, cumulative mean RMS value and cumulative mean average value. (e) The limiting values of AE parameters obtained to monitor tool condition for a given cutting conditions is found to be applicable to monitor tool condition, even when the cutting speed is varied within ± 12% by keeping all other cutting conditions constant. (f) Peak to peak amplitude is found to be the suitable AE parameter in sensing the tool breakage. References Blum T and Inasaki I 1990 A study in acoustic emission from the orthogonal cutting process, Journal of Engineering for Industry 112: Byrne G, Dornfeld D, Inasaki I, Ketteler G, Konig W and Teti R 1995 Tool conditioning monitoring (TCM) The status of research and industrial application, Ann. CIRP 44(2): Hotton D V and Qinghuan Yr 1999 On the effects of built up edge on acoustic emission in metal cutting, J. Eng. Industry, Inasaki Ichiro 1998 Application of Acoustic Emission Sensor for Monitoring Machining Processes, Ultrasonics 36: Iwata K and Moriwaki T 1976 An application of acoustic emission measurement to in-process sensing of tool wear, Annals of the CIRP 25: Jiang C Y, Zhang Y Z and Xu H J 1996 In-Process monitoring of tool wear stage by the frequency band energy method, Annals of the CIRP 36: 45 48

24 234 P Kulandaivelu et al Kandasami G S 1998 Application of acoustic emission technique to studies of machining processes, Ph.D. thesis, Madras Institute of Technology, Chennai Lidan and Mathew J 1990 Tool wear and failure monitoring techniques for turning - a review, Intern. J. Machine Tools Manufact. 30(4): Rao S S 1995 Mechanical Vibrations. New York: Addison Wesley, 572 Viktor P A 2003 The assessments of cutting tool wear, Intern. J. Machine Tools Manufact. 44(6):