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Micro-end-milling—I. Wear and breakage
International Journal of Machine Tools & Manufacture 38 (1998) 1419–1436
Micro-end-milling—I. Wear and breakage I. Tansel*, O. Rodriguez, M. Trujillo, E. Paz, W. Li Department of Mechanical Engineering, Florida International University, Miami, FL 33199, USA Received 24 November 1997
Abstract Unpredictable tool life and premature tool failure are major problems in micro-machining. In this study, the failure mechanisms of micro-end-mills were studied during the machining of aluminum, graphite electrodes and mild steel workpieces. Hundreds of machining operations were performed, and the pictures of cutting edges were taken with a scanning electron microscope to identify fatigue and extensive stressrelated failure mechanisms. Also, the cutting force variation was monitored, i.e. the relationship between the utilization-related changes at the tool structure (wear), and the outcomes (increasing cutting force which means raising stress on the tiny shaft). Inspection of the cutting force variation patterns of large numbers of micro-end-mills indicated that tool failure occurs with chip clogging, fatigue and wear-related excessive stress depending on the characteristics of the workpiece. Two tool breakage prediction methods were developed by considering the variation of the static part of the feed direction cutting force. These methods used segmental averages and wavelet transformation coefficients. The accuracy of the proposed approaches were tested with experimental data and the agreement between the predictions and actual observations are reported. 1998 Elsevier Science Ltd. All rights reserved. Keywords: Micro-machining; Micro-tool; Micro-end-mill; Wavelet transformation; Wear; Failure; Monitoring; Metal cutting
1. Introduction Reducing size and weight can substantially increase the convenience and value of many products. Many manufacturers have miniaturized the components of their products in order to increase their market share. The future of many manufacturers will rely on how quickly and successfully they can implement micro-machining in their operations. Wear and tool failure mechanisms are * Corresponding author. 0890-6955/98/$19.00 1998 Elsevier Science Ltd. All rights reserved. PII: S 0 8 9 0 - 6 9 5 5 ( 9 8 ) 0 0 0 1 5 - 7
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known to be complicated in micro-machining. Tool life is acceptable at a very low feed-per-tooth. At higher feed-per-tooth values, tool life becomes unpredictable and short. In this study, the relationship between usage-related changes (wear) of the micro-tool and the stress on the tiny drive shaft of the micro-tool has been investigated. Two approaches are proposed to predict tool breakage slightly in advance of the event. These approaches use either the segmental averages or wavelet transformations. Conventional end milling operations have been studied for more than 20 years. Various studies have been conducted to model cutting mechanism [1–3], to study the characteristics of cutting forces [4,5], and to detect tool failure [6–13]. Cutting force characteristics of micro-end-milling operations are almost the same as those of conventional milling; however, the wear and breakage mechanisms are very different. The cutting edges of conventional end mills wear out when they lose material; as a result, craters are formed and generally the cutting edges break one by one. The purpose of wear monitoring is to estimate when the tool will not be able to remove material at the desired quality standard. Conversely, it is the tiny shafts of the micro-tools that break when either the cutting edges become dull (because of material loss or because they are covered with particles of workpiece material) or a chip clogs. For a micro-tool the most important concern is how the cutting force (and therefore, the stress) changes as it performs the cutting operation, as there is a relationship between the usage (wear) and the stress (cutting force). Manufacturers frequently encounter premature failure or extremely unpredictable tool life when they use conventional machines for micro-machining operations. Miniature tools are expensive. Also, once the tool breaks on a workpiece, one generally discards that piece, because inspection and resetting of the machine is very time consuming. Efficient operation requires high spindle speeds (over 10,000 rpm) so that a very small feed rate per tooth can be used while still maintaining reasonable feed rates. However, this is an expensive option since it usually requires the purchase of a special machine tool and/or a high-speed spindle. In addition, parts often have to be moved from one machine to another for machining operations with different size cutting tools. Konig and coworkers’ studies indicated that the main reason for tool failure in their microdrilling operations was chip clogging [14]. Tansel and coworkers [15–17] observed that the characteristics of cutting force and tool vibration signals changed slightly during micro-drilling operations just before the tool breaks. In this study the cutting force variation of micro-end-milling operations was studied during machining of aluminum and steel workpieces to find a new approach to predict tool breakage. In this study, one of the important indicators of tool condition to predict possible breakage was found to be the change of the static component of the feed direction cutting force in slot milling operations. The static component of the cutting force was monitored through the use of segmental averages and wavelet transformations [18]. The segmental averages are easy to calculate with a microcomputer. The wavelet transformation approach can be executed much faster than the calculation of segmental averages if special parallel processing hardware is used. In the following sections, the theoretical background, tool breakage mechanism, and the proposed tool breakage prediction methods will be outlined. Finally, the accuracy of the proposed methods will be evaluated on the experimental data.
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2. Wavelet transformation Wavelets are obtained by creating a family of functions derived from a single function [19– 24]. An expression for such a system can be written in the following form: h(a,b)(x) = 兩a兩−1/2h
冉 冊
x−b , a
(1)
where a and b are the dilation and translation parameters, respectively. In the above equation, h(a,b) represents the family of wavelets obtained from the single h function by dilations and translations. The given data consist of the f function in the given x coordinate. An original function, f(t), can be represented (also reconstructed) by the following expression:
冘 ⬁
f (x) =
冘冘 ⬁
c(n)⌽n(t) +
n=−⬁
⬁
d(i,j)⌿i,j (t)
(2)
i=0 j=−⬁
where c(n) = 兰f(t)n(t)dt d(i,j) = 兰f(t)⌿i,j (t)dt where c(n) and d(i,j) are the coefficients of the wavelet transform. It can be proven that the basic function (scaling function), ⌽(x), of a wavelet system, can be calculated with the following recursive equation: ⌽(x) =
冘
c(n)⌽(2x − n),
(3)
n
where c(n) is the wavelet coefficient and n is the index. The primary wavelet, (x), can be obtained with the following expression: ⌿(x) =
冘
( − 1)nc(n + 1)⌽(2x − n)
(4)
n
In this study, where c(n + 1) is the coefficient, a well-known wavelet system (Haar) was used to represent the static part of the signal.
3. Experimental setup The experimental setup is presented in Fig. 1. The workpiece was attached to a Kistler 9257B three-component dynamometer. The dynamometer was assembled on a linear table. The linear table moved the workpiece in the feed direction by using a servomotor, which was controlled by a microcomputer. The linear table was fixed on the top of a steel table. To eliminate the inertia forces (created by machine vibrations) and the related noise from the dynamometer signal, the steel table which carries the linear table and machine tool were isolated from the spindle. The steel legs gave excellent rigidity to the table and its wood top damped the oscillations transmitted
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Fig. 1. Experimental setup.
to the structure through the floor. The machine tool table was lowered and was not used in this study. The micro-end-mills with 0.015 in. (0.38 mm) diameter were attached to the spindle of the Bridgeport Series I milling machine for tests at the low rpms and a separate spindle which reaches 40,000 rpm. The tools were centered and the spindle speed was calibrated by using a Polytec OFV 2500L laser vibrometer. During the tests, an aluminum, a graphite electrode and a mild steel workpiece were machined. The experimental data were digitized with a Nicolet 310 digital oscilloscope at a 1 ms sampling interval. The digitized data were analyzed on a microcomputer.
4. Breakage mechanism of micro-end mills In previous studies chip clogging was observed as the main cause of tool breakage in microdrilling operations [14]. However, inspection of the cutting forces during micro-drilling operations for some other materials [15,16] suggested that the characteristics of the cutting force changed several revolutions before the failure happens. In this study the tool breakage mechanisms were investigated during the machining of aluminum workpieces by evaluating the cutting force data. In this section wear will be redefined for micro-machining, and three possible breakage mech-
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anisms will be outlined. The existence of the proposed tool breakage mechanisms will be established in the results Section 6. 4.1. Redefinition of wear in micro-machining Tool wear has always been discussed in the context of conventional machining operations. It is necessary to consider the size of micro-tools and redefine the wear considering their dimensions and their consequent drastic influence on various developments, which can be considered negligible in conventional cutting operations. Various changes occur to the cutting tool as a result of its use in machining operations. Some of these changes are loss of tool material, deposition of small workpiece particles on the tool surfaces and change of tool geometry with deflection. In this study, any difference between a new and used tool is referred to as “wear”. For example, microscopic damage to the cutting edges with gradually developing dullness, or filling of the empty spaces around the cutting edges with deposits of small particles with build up on the edge, are all considered wear. In a micro-machining cutting tool, the metal removal rate and cutting forces are very small. A small material loss with 0.00375 in. × 0.005 in. dimensions at one cutting edge of a half inch end-mill may increase the cutting force at the tool tip a few percent and deteriorate the surface finish. Although the stress on the shaft of the cutting tool will slightly increase, it is not a major concern since a small increase will not cause tool shaft breakage. Previous studies on conventional tools have tried to establish a relationship between tool quality (wear, acceptability of machining quality), remaining life of cutting edges and indirect measurements (acoustic emission, cutting force, acceleration, etc.), tool wear and acceptability of machining quality. The same material loss (0.00375 in. × 0.005 in.) would eliminate the half of one cutting edge of a micro-tool with 0.015 in. diameter and may easily double the cutting force on the other edge. The stress on the tiny shaft of the cutting tool will increase in proportion to the force increase. Since the allowable stress on the tiny shaft of a micro-tool is only several times bigger than the stress under normal cutting conditions, the failure probability of the tool will drastically increase. As illustrated by this example, changes at the cutting edges of micro-tools are a major concern since they can create drastic increases in the stress at the shaft and cause tool breakage. When soft and brittle material such as graphite electrodes are cut on machine tools, microtools can achieve prolonged tool life if the cutting conditions are selected conservatively. When the cutting edges lose material, the sharp tips may round, and then become dull. The cutting forces increase when the tool gets dull. The increase of the cutting forces creates bigger tool deflections and geometric accuracy of the part deteriorates. As a result, it might be necessary to run the same part program again with another (new) tool to obtain the desired part dimensions. 4.2. Chip clogging and breakage If chip clogging occurs, the cutting force increases continuously as long as the chip stays at a critical point and obstructs the movement of the cutting edges. A sharp increase is expected in the cutting force. In most of the micro-end-milling operations (with miniature cutting tools with two cutting edges), chips can be removed from the machining area effectively since the selected depth of cut is small and each cutting edge at most (in slot cutting) removes material only for
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half a revolution. If chip clogging occurs, cutting forces and stresses increase beyond the endurance limit of the tool and breakage will occur in a few rotations. High-speed steel (HSS) tools tolerate chip clogging better than carbide tools since they are much more flexible. In this case, the tool wear may have very little influence on the breakage mechanism. Breakage is unpredictable and happens in a very short time after clogging begins. It is almost impossible to predict chip clogging ahead of time. 4.3. Fatigue-related breakage Fatigue-related breakage may happen if the cutting force and the stress (at the tool shaft) increase as a result of tool wear, and then stay at that level for an extended period of time. If the stress is below the endurance limit of the shaft but it is above the normal level, the tool will not be broken immediately. The stress on the shaft will change repeatedly while it is rotating. This means that the strain distribution will also change repeatedly at the tool shaft and create fatigue. The increase of the cutting forces beyond the normal force range will indicate the existence of wear. The tool will eventually break with fatigue. When relatively soft and brittle materials such as graphite electrodes are cut this type of tool failure might take place. The tool life is otherwise very long. 4.4. Excessive stress-related breakage Breakage will occur very quickly if the usage-related problems are extensive and the cutting force increases beyond the strength of the tool. The cutting force might increase for two reasons. First, the cutting edge might lose its sharpness if it gets dull or tiny workpiece particles create a thin coating on the cutting edge. Second, the cutting edge(s) is/are partially damaged or deposition of workpiece particles fills the tiny groves (or a built-up edge is created). In this case, the cutting edges cannot remove enough material to open satisfactory space for the central section (shaft) of the tool. The workpiece starts to push the shaft of the tool and it deflects. The deflection of the tool and the stress will increase with every rotation. The static component of the feed direction force will continuously increase until the tool breaks. The main reason for the breakage in this case is the excessive stress beyond the endurance limit of the tool.
5. Estimation of future feed direction force In this study, the relationship between an increase in the cutting forces during a machining operation and changes in the tool condition was investigated. An increase in the absolute value of the static part of the feed direction cutting force while the cutting conditions are constant indicates that problems are developing. Therefore, tool failure may be predicted by considering the trend. Two different approaches were used to calculate the static component of the feed direction cutting force:
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5.1. Use of segmental averages The averages of 80 data point long data segments (which correspond to approximately four spindle rotations) were calculated for the data collected at low spindle speeds. The averages of 100 data points were calculated for the tests at high rotational speeds. The slope and intercept of a line equation were calculated from three segment averages by using the least square method. The two and four-steps-ahead segmental averages were estimated by using the parameters of the calculated line equation. The forecasts predicted the averages of future static force values 0.16 s (two-steps-ahead forecast) and 0.32 s (four-steps-ahead forecast) in advance. 5.2. Use of wavelet transformation coefficients Calculation of the segmental averages is the best approach with present day computers. However, use of wavelet transformations has two advantages: first, they represent the system more accurately than segmental averages if the waveform is optimized by considering the characteristics of the signal. Second, wavelet parameters can be used for many other purposes such as identification of runout. As multipurpose wavelet transformation hardware becomes affordable, wavelet transformation coefficients could be calculated faster and easier by using generic systems. The c(n) approximation coefficient of the wavelet transformation (Eq. (2)) can be calculated by using the Haar wavelet transformation system. To find the trend, a line equation can be fitted by using linear regression on the parameters of the wavelet transformation. Future static force values can be estimated by means of this line equation. In this study, the c(n) coefficients were obtained after the transformation was repeated four times by using the Haar wavelet transformation system. This process reduced the data to about one-sixteenth of its original size. The parameters of a line equation were obtained by using the linear least squares method from 19 approximation coefficients. The last approximation coefficient of the next wavelet transformation was estimated by using this line equation. The estimated approximation coefficient corresponds to the future static force value about 0.32 s ahead. 6. Results and discussion In this section, the experimental results will be presented and the effectiveness of the proposed remaining tool life estimation procedure will be outlined: 6.1. Experimental study and results More than 50 tools were broken at different cutting conditions while an aluminum (5052) and mild steel workpieces were cut with an HSS micro-end-mill of 0.015 in. diameter. A HSS tool with a 0.03 in. diameter was used to cut a graphite workpiece. A typical raw cutting force signal and its moving average are presented in Fig. 2 for aluminum without any offset and scale-related correction. The cutting force had a repetitive pattern (almost sinusoidal) in each rotation since two cutting edges were engaged in turn with the workpiece. Also, the moving average of the signal is plotted in Fig. 2 to show the trend of the static part or very low-frequency characteristics
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Fig. 2.
Raw cutting force data.
of the signal. To study the static cutting force characteristics at different cutting conditions, the averages of 80 point long segments of the feed direction cutting force were calculated; these are presented in Figs 3 and 4. The cutting conditions for low-speed cutting tests are presented in Table 1. The spindle speed and depth of cut were 30,000 rpm and 0.004 in. respectively for the high-speed cutting tests respectively. The feed rates were 2 in./min for aluminum and 1 in./min for mild steel. 6.2. Tool breakage mechanisms The variation of the cutting force just before the breakage is demonstrated in Figs 3–6. The experiments were repeated at 30,000 rpm with aluminum and mild steel workpieces. All plots Table 1 The experimental cutting conditions for low-speed cutting experiments with aluminum material Data WAVE-01 WAVE-23 WAVE-31 WAVE-33 WAVE-35 WAVE-39 WAVE-43
Cutting speed (rpm) (W01) (W23) (W31) (W33) (W35) (W39) (W43)
3000 3000 3000 3000 3000 3000 3000
Feed rate (in./s) 0.005 0.003 0.003 0.003 0.003 0.001 0.001
Depth of cut (in.) 0.005 0.010 0.015 0.015 0.015 0.015 0.015
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Fig. 3.
Fig. 4.
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The segmental averages of the feed direction cutting force. Fatigue-related breakage occurred.
The segmental averages of the feed direction cutting force. Excessive stress-related breakage occurred.
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Fig. 5. Comparison of two different cases demonstrating the cutting forces at breakage while machining aluminum.
Fig. 6. Characteristic trend of segment averages of cutting forces in mild steel.
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show that the static part of the force increased after the tools were used for a period of time. When the static part of the cutting force increases the stress at the tool shaft increases. Since the tool rotates, the stress distribution at the cutting tool will vary and a repetitive load is encountered. The direction and magnitude of the cutting force is going to have a repetitive pattern while the tool is rotating. The static part of the force had a constant value and breakage occurred without the force reaching very high values in the four cases presented in Fig. 3 and one of the cases (Tool No. 1) presented in Fig. 5. In Fig. 4 and the other case (Tool No. 11) presented in Fig. 5, the absolute value of the static part of the feed direction cutting force increased and breakage occurred more quickly than the cases in Fig. 3 because of excessive stress. In Fig. 6 the existence of similar characteristics is demonstrated for mild steel. The reader should consider the absolute value of the force when we refer to the increase or decrease. In Fig. 4 for example, the absolute value of the cutting force increased until failure occurred. In all the plots, the force was negative because of the selected positive direction. Figs 3–6 verify the following facts: 1. As the tools were used, the static part of the feed direction cutting force signal increased (Figs 3–6). 2. The static part of the feed direction cutting force was constant until fatigue-related breakage occurred in Figs 3 and 5 (Tool No. 1) for five different cases. 3. The static part of the feed direction cutting force (absolute value) increased rapidly until the tool broke with excessive stress in Figs 4 and 6. To investigate the wear and breakage mechanisms, pictures of the tools were taken using an electron scanning microscope. The tip of a relatively new cutting tool (after 7.5 in. of cutting on aluminum material) is presented in Fig. 7(a). Fragments of the workpiece material can be seen adhering to the surface of the cutting edges. The surface is fairly flat indicating the almost perfect condition of the tool. Only very minor damage was observed at the cutting edges. The tip of another cutting tool is presented in Fig. 7(b) after 17.5 in. of cutting. A very large amount of small workpiece particles adhered to the surface. In addition, the not so straight cutting edges indicate the dullness and the possibly high cutting forces. In Fig. 7(c) a new and worn 0.03 in. end-mills are compared in this picture which was taken by optical microscope. The worn tool was used for more than 4 h to machine a graphite electrode. Portions of the cutting edges of the used tool were eroded. Typically, micro-tools that are used to cut metals are broken before they are this badly worn out. These observations verify the explanations in Section 4 about the usage-related changes in the tool, an increase in the cutting forces and the development of higher stress on the tool shaft. The higher stresses eventually cause fatigue or excessive force-related tool breakage. The trend of the cutting force variations in Fig. 4 is studied in Fig. 8. It presents a linear fit of exponential functions to data just before failure. The linear functions [Fig. 8(a)] fit the small sections better, and exponential functions fit the complete curve better. These observations suggest that in the short term damage reduces metal removal and at every single rotation the tool will be bent slowly because of the constant feed rate. This mechanism creates a linearly increasing static force (or stress) before the tool is broken. On the other hand, the better fitting exponential function [Fig. 8(b)] to the whole segment of data indicates that when damage or chip clogging occurs, the
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Fig. 7. Pictures of new and worn tools. (a) The tip of a micro-tool after 7.5 in. of cutting in aluminum material. (b) The tip of a micro-tool after 17.5 in. of cutting in aluminum material. (c) Comparison of a new tool with a worn one which was used to cut graphite electrode for more than 4 h.
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Fig. 8. The trend of the feed direction cutting force just before excessive stress-related breakage occurs. (a)Linear approximation. (b)Exponential approximation.
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tool will eventually cause problems and the condition of the tool deteriorates. For example, the increasing cutting forces may create more damage at the other sections of the cutting edges. 6.3. Performance of the proposed breakage prediction method Monitoring the static part of the feed direction cutting force was very effective in estimating the tool breakage ahead of time. In all seven cases presented (Figs 3 and 4), the static part of the force just one second before the breakage was higher than typical values with a new tool. A selected threshold of − 2.5 N was sufficient to detect the deteriorating cutting edges in Fig. 3 if the cutting edges are only slightly dull or dysfunctional (with damage or with little chips stuck to them). Excessive stress-related breakage was estimated from the trend of the static part of the feed direction cutting force. In Fig. 9, two and four-steps-ahead predictions from the segmental averages (see Section 5.1 for the procedure) are compared with the actual values. The accuracy of the estimations were satisfactory to take proper action 0.16 and 0.32 s ahead. Depending on the speed of the available hardware (controller and machine tool) two or four segmental averages ahead predictions can be used. In Section 5.2, the use of wavelet transformation coefficients was proposed to forecast 0.32 s ahead. In this section, the original digitized numbers (2-byte integers) are used to demonstrate more realistic signal processing since in an actual application, digitized numbers are used directly without wasting computational time with scaling and offsetting. In Fig. 10(a), the wavelet coefficients were calculated from the experimental feed direction cutting force data and presented. In
Fig. 9.
The two and four-steps-ahead predictions from the segmental averages.
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Fig. 10. Wavelet coefficients. Arbitrary Y-axis without offset and scale. (a)Wavelet coefficients from the experimental feed direction cutting force data. (b)The original data and the reconstructed waveform are presented just before and after breakage takes place.
Fig. 10(b), to show the accuracy and function of the wavelet transformation, the original data and the reconstructed waveform are presented just before and after breakage takes place. The reconstruction was realized by using Eq. (2) and part of the coefficients (just around breakage) in Fig. 10(a). The accuracy of the 0.32 s ahead estimations are presented in Fig. 11. Wavelet coefficients smoothed the data and provided reasonable estimations. The segmental averaging and wavelet transformation-based estimations had comparable accu-
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Fig. 11. The accuracy of the 0.32 s ahead predictions from wavelet transformation. Arbitrary Y-axis without offset and scale.
racy. Although segmental averaging is presently much more straightforward than the wavelet transformation method of present-day commercial microcomputers, if wavelet transformation hardware becomes available at reasonable prices, these calculations can be performed much faster using parallel processors. Also, instead of the Haar transformations, special waveforms may be designed to obtain better accuracy. Time-series models have also been used for forecasting [25]. However, time-series models require continuous updating of the model and the accuracy of the estimations begins to deteriorate after the first two predictions. A linear model is simpler to program and the accuracy of future estimations after many steps ahead are still reasonable as long as the trend of the data has not changed. 7. Conclusion The tool breakage mechanisms in micro-end-milling operations were investigated in this study. A relationship was found between tool condition (wear, cutting edge-damage, little particles stuck to the tool surface), and the static part of the feed direction cutting force. Usage-related stress was found to be an important factor in fatigue and excessive load-related micro-tool breakage. Two tool breakage prediction methods were proposed by using segmental averages and wavelet transformations. The static part of the feed direction force was found to be a simple and reliable indicator of
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tool condition in slot cutting operations. The deteriorating tool condition can be detected by monitoring the absolute value of the static part of the feed direction force. When it increases more than three or four times while the cutting conditions are the same, appropriate actions should be taken to avoid breakage. Two methods were proposed for estimation of the static part of the cutting force in 0.16–0.32 s ahead. The first method calculates the averages of 80 s-long feed direction cutting force segments and predicts the future averages 0.16 and/or 0.32 s ahead. The other prediction method calculates the wavelet transformation and predicts the future values in about 0.32 s ahead. Both approaches estimate the future values with acceptable accuracy. The number of manufactured miniature parts and intricate sections of conventional designs have been increasing dramatically. To produce these parts, micro-machining operations will be used more commonly in the future. Especially by adding high-speed spindles to the conventional machine tools reasonable feed rates with very small feed-per-tooth can be achieved. To increase the feed rates further on these machines, monitoring of the feed direction cutting force will be useful for predicting breakage and taking proper action. Also, many manufacturers will feel pressure to use conventional machine tools for micro-machining applications either because of their limited budget or to eliminate time consumption by setting up the same parts on several machines. Monitoring of the static part of the feed direction cutting force and adjustment of the feed rate during machining will allow use of high feed rates, increase tool life, and minimize breakage during the machining of a part.
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Micro-end-milling—I. Wear and breakage I. Tansel*, O. Rodriguez, M. Trujillo, E. Paz, W. Li Department of Mechanical Engineering, Florida International University, Miami, FL 33199, USA Received 24 November 1997
Abstract Unpredictable tool life and premature tool failure are major problems in micro-machining. In this study, the failure mechanisms of micro-end-mills were studied during the machining of aluminum, graphite electrodes and mild steel workpieces. Hundreds of machining operations were performed, and the pictures of cutting edges were taken with a scanning electron microscope to identify fatigue and extensive stressrelated failure mechanisms. Also, the cutting force variation was monitored, i.e. the relationship between the utilization-related changes at the tool structure (wear), and the outcomes (increasing cutting force which means raising stress on the tiny shaft). Inspection of the cutting force variation patterns of large numbers of micro-end-mills indicated that tool failure occurs with chip clogging, fatigue and wear-related excessive stress depending on the characteristics of the workpiece. Two tool breakage prediction methods were developed by considering the variation of the static part of the feed direction cutting force. These methods used segmental averages and wavelet transformation coefficients. The accuracy of the proposed approaches were tested with experimental data and the agreement between the predictions and actual observations are reported. 1998 Elsevier Science Ltd. All rights reserved. Keywords: Micro-machining; Micro-tool; Micro-end-mill; Wavelet transformation; Wear; Failure; Monitoring; Metal cutting
1. Introduction Reducing size and weight can substantially increase the convenience and value of many products. Many manufacturers have miniaturized the components of their products in order to increase their market share. The future of many manufacturers will rely on how quickly and successfully they can implement micro-machining in their operations. Wear and tool failure mechanisms are * Corresponding author. 0890-6955/98/$19.00 1998 Elsevier Science Ltd. All rights reserved. PII: S 0 8 9 0 - 6 9 5 5 ( 9 8 ) 0 0 0 1 5 - 7
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known to be complicated in micro-machining. Tool life is acceptable at a very low feed-per-tooth. At higher feed-per-tooth values, tool life becomes unpredictable and short. In this study, the relationship between usage-related changes (wear) of the micro-tool and the stress on the tiny drive shaft of the micro-tool has been investigated. Two approaches are proposed to predict tool breakage slightly in advance of the event. These approaches use either the segmental averages or wavelet transformations. Conventional end milling operations have been studied for more than 20 years. Various studies have been conducted to model cutting mechanism [1–3], to study the characteristics of cutting forces [4,5], and to detect tool failure [6–13]. Cutting force characteristics of micro-end-milling operations are almost the same as those of conventional milling; however, the wear and breakage mechanisms are very different. The cutting edges of conventional end mills wear out when they lose material; as a result, craters are formed and generally the cutting edges break one by one. The purpose of wear monitoring is to estimate when the tool will not be able to remove material at the desired quality standard. Conversely, it is the tiny shafts of the micro-tools that break when either the cutting edges become dull (because of material loss or because they are covered with particles of workpiece material) or a chip clogs. For a micro-tool the most important concern is how the cutting force (and therefore, the stress) changes as it performs the cutting operation, as there is a relationship between the usage (wear) and the stress (cutting force). Manufacturers frequently encounter premature failure or extremely unpredictable tool life when they use conventional machines for micro-machining operations. Miniature tools are expensive. Also, once the tool breaks on a workpiece, one generally discards that piece, because inspection and resetting of the machine is very time consuming. Efficient operation requires high spindle speeds (over 10,000 rpm) so that a very small feed rate per tooth can be used while still maintaining reasonable feed rates. However, this is an expensive option since it usually requires the purchase of a special machine tool and/or a high-speed spindle. In addition, parts often have to be moved from one machine to another for machining operations with different size cutting tools. Konig and coworkers’ studies indicated that the main reason for tool failure in their microdrilling operations was chip clogging [14]. Tansel and coworkers [15–17] observed that the characteristics of cutting force and tool vibration signals changed slightly during micro-drilling operations just before the tool breaks. In this study the cutting force variation of micro-end-milling operations was studied during machining of aluminum and steel workpieces to find a new approach to predict tool breakage. In this study, one of the important indicators of tool condition to predict possible breakage was found to be the change of the static component of the feed direction cutting force in slot milling operations. The static component of the cutting force was monitored through the use of segmental averages and wavelet transformations [18]. The segmental averages are easy to calculate with a microcomputer. The wavelet transformation approach can be executed much faster than the calculation of segmental averages if special parallel processing hardware is used. In the following sections, the theoretical background, tool breakage mechanism, and the proposed tool breakage prediction methods will be outlined. Finally, the accuracy of the proposed methods will be evaluated on the experimental data.
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2. Wavelet transformation Wavelets are obtained by creating a family of functions derived from a single function [19– 24]. An expression for such a system can be written in the following form: h(a,b)(x) = 兩a兩−1/2h
冉 冊
x−b , a
(1)
where a and b are the dilation and translation parameters, respectively. In the above equation, h(a,b) represents the family of wavelets obtained from the single h function by dilations and translations. The given data consist of the f function in the given x coordinate. An original function, f(t), can be represented (also reconstructed) by the following expression:
冘 ⬁
f (x) =
冘冘 ⬁
c(n)⌽n(t) +
n=−⬁
⬁
d(i,j)⌿i,j (t)
(2)
i=0 j=−⬁
where c(n) = 兰f(t)n(t)dt d(i,j) = 兰f(t)⌿i,j (t)dt where c(n) and d(i,j) are the coefficients of the wavelet transform. It can be proven that the basic function (scaling function), ⌽(x), of a wavelet system, can be calculated with the following recursive equation: ⌽(x) =
冘
c(n)⌽(2x − n),
(3)
n
where c(n) is the wavelet coefficient and n is the index. The primary wavelet, (x), can be obtained with the following expression: ⌿(x) =
冘
( − 1)nc(n + 1)⌽(2x − n)
(4)
n
In this study, where c(n + 1) is the coefficient, a well-known wavelet system (Haar) was used to represent the static part of the signal.
3. Experimental setup The experimental setup is presented in Fig. 1. The workpiece was attached to a Kistler 9257B three-component dynamometer. The dynamometer was assembled on a linear table. The linear table moved the workpiece in the feed direction by using a servomotor, which was controlled by a microcomputer. The linear table was fixed on the top of a steel table. To eliminate the inertia forces (created by machine vibrations) and the related noise from the dynamometer signal, the steel table which carries the linear table and machine tool were isolated from the spindle. The steel legs gave excellent rigidity to the table and its wood top damped the oscillations transmitted
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Fig. 1. Experimental setup.
to the structure through the floor. The machine tool table was lowered and was not used in this study. The micro-end-mills with 0.015 in. (0.38 mm) diameter were attached to the spindle of the Bridgeport Series I milling machine for tests at the low rpms and a separate spindle which reaches 40,000 rpm. The tools were centered and the spindle speed was calibrated by using a Polytec OFV 2500L laser vibrometer. During the tests, an aluminum, a graphite electrode and a mild steel workpiece were machined. The experimental data were digitized with a Nicolet 310 digital oscilloscope at a 1 ms sampling interval. The digitized data were analyzed on a microcomputer.
4. Breakage mechanism of micro-end mills In previous studies chip clogging was observed as the main cause of tool breakage in microdrilling operations [14]. However, inspection of the cutting forces during micro-drilling operations for some other materials [15,16] suggested that the characteristics of the cutting force changed several revolutions before the failure happens. In this study the tool breakage mechanisms were investigated during the machining of aluminum workpieces by evaluating the cutting force data. In this section wear will be redefined for micro-machining, and three possible breakage mech-
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anisms will be outlined. The existence of the proposed tool breakage mechanisms will be established in the results Section 6. 4.1. Redefinition of wear in micro-machining Tool wear has always been discussed in the context of conventional machining operations. It is necessary to consider the size of micro-tools and redefine the wear considering their dimensions and their consequent drastic influence on various developments, which can be considered negligible in conventional cutting operations. Various changes occur to the cutting tool as a result of its use in machining operations. Some of these changes are loss of tool material, deposition of small workpiece particles on the tool surfaces and change of tool geometry with deflection. In this study, any difference between a new and used tool is referred to as “wear”. For example, microscopic damage to the cutting edges with gradually developing dullness, or filling of the empty spaces around the cutting edges with deposits of small particles with build up on the edge, are all considered wear. In a micro-machining cutting tool, the metal removal rate and cutting forces are very small. A small material loss with 0.00375 in. × 0.005 in. dimensions at one cutting edge of a half inch end-mill may increase the cutting force at the tool tip a few percent and deteriorate the surface finish. Although the stress on the shaft of the cutting tool will slightly increase, it is not a major concern since a small increase will not cause tool shaft breakage. Previous studies on conventional tools have tried to establish a relationship between tool quality (wear, acceptability of machining quality), remaining life of cutting edges and indirect measurements (acoustic emission, cutting force, acceleration, etc.), tool wear and acceptability of machining quality. The same material loss (0.00375 in. × 0.005 in.) would eliminate the half of one cutting edge of a micro-tool with 0.015 in. diameter and may easily double the cutting force on the other edge. The stress on the tiny shaft of the cutting tool will increase in proportion to the force increase. Since the allowable stress on the tiny shaft of a micro-tool is only several times bigger than the stress under normal cutting conditions, the failure probability of the tool will drastically increase. As illustrated by this example, changes at the cutting edges of micro-tools are a major concern since they can create drastic increases in the stress at the shaft and cause tool breakage. When soft and brittle material such as graphite electrodes are cut on machine tools, microtools can achieve prolonged tool life if the cutting conditions are selected conservatively. When the cutting edges lose material, the sharp tips may round, and then become dull. The cutting forces increase when the tool gets dull. The increase of the cutting forces creates bigger tool deflections and geometric accuracy of the part deteriorates. As a result, it might be necessary to run the same part program again with another (new) tool to obtain the desired part dimensions. 4.2. Chip clogging and breakage If chip clogging occurs, the cutting force increases continuously as long as the chip stays at a critical point and obstructs the movement of the cutting edges. A sharp increase is expected in the cutting force. In most of the micro-end-milling operations (with miniature cutting tools with two cutting edges), chips can be removed from the machining area effectively since the selected depth of cut is small and each cutting edge at most (in slot cutting) removes material only for
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half a revolution. If chip clogging occurs, cutting forces and stresses increase beyond the endurance limit of the tool and breakage will occur in a few rotations. High-speed steel (HSS) tools tolerate chip clogging better than carbide tools since they are much more flexible. In this case, the tool wear may have very little influence on the breakage mechanism. Breakage is unpredictable and happens in a very short time after clogging begins. It is almost impossible to predict chip clogging ahead of time. 4.3. Fatigue-related breakage Fatigue-related breakage may happen if the cutting force and the stress (at the tool shaft) increase as a result of tool wear, and then stay at that level for an extended period of time. If the stress is below the endurance limit of the shaft but it is above the normal level, the tool will not be broken immediately. The stress on the shaft will change repeatedly while it is rotating. This means that the strain distribution will also change repeatedly at the tool shaft and create fatigue. The increase of the cutting forces beyond the normal force range will indicate the existence of wear. The tool will eventually break with fatigue. When relatively soft and brittle materials such as graphite electrodes are cut this type of tool failure might take place. The tool life is otherwise very long. 4.4. Excessive stress-related breakage Breakage will occur very quickly if the usage-related problems are extensive and the cutting force increases beyond the strength of the tool. The cutting force might increase for two reasons. First, the cutting edge might lose its sharpness if it gets dull or tiny workpiece particles create a thin coating on the cutting edge. Second, the cutting edge(s) is/are partially damaged or deposition of workpiece particles fills the tiny groves (or a built-up edge is created). In this case, the cutting edges cannot remove enough material to open satisfactory space for the central section (shaft) of the tool. The workpiece starts to push the shaft of the tool and it deflects. The deflection of the tool and the stress will increase with every rotation. The static component of the feed direction force will continuously increase until the tool breaks. The main reason for the breakage in this case is the excessive stress beyond the endurance limit of the tool.
5. Estimation of future feed direction force In this study, the relationship between an increase in the cutting forces during a machining operation and changes in the tool condition was investigated. An increase in the absolute value of the static part of the feed direction cutting force while the cutting conditions are constant indicates that problems are developing. Therefore, tool failure may be predicted by considering the trend. Two different approaches were used to calculate the static component of the feed direction cutting force:
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5.1. Use of segmental averages The averages of 80 data point long data segments (which correspond to approximately four spindle rotations) were calculated for the data collected at low spindle speeds. The averages of 100 data points were calculated for the tests at high rotational speeds. The slope and intercept of a line equation were calculated from three segment averages by using the least square method. The two and four-steps-ahead segmental averages were estimated by using the parameters of the calculated line equation. The forecasts predicted the averages of future static force values 0.16 s (two-steps-ahead forecast) and 0.32 s (four-steps-ahead forecast) in advance. 5.2. Use of wavelet transformation coefficients Calculation of the segmental averages is the best approach with present day computers. However, use of wavelet transformations has two advantages: first, they represent the system more accurately than segmental averages if the waveform is optimized by considering the characteristics of the signal. Second, wavelet parameters can be used for many other purposes such as identification of runout. As multipurpose wavelet transformation hardware becomes affordable, wavelet transformation coefficients could be calculated faster and easier by using generic systems. The c(n) approximation coefficient of the wavelet transformation (Eq. (2)) can be calculated by using the Haar wavelet transformation system. To find the trend, a line equation can be fitted by using linear regression on the parameters of the wavelet transformation. Future static force values can be estimated by means of this line equation. In this study, the c(n) coefficients were obtained after the transformation was repeated four times by using the Haar wavelet transformation system. This process reduced the data to about one-sixteenth of its original size. The parameters of a line equation were obtained by using the linear least squares method from 19 approximation coefficients. The last approximation coefficient of the next wavelet transformation was estimated by using this line equation. The estimated approximation coefficient corresponds to the future static force value about 0.32 s ahead. 6. Results and discussion In this section, the experimental results will be presented and the effectiveness of the proposed remaining tool life estimation procedure will be outlined: 6.1. Experimental study and results More than 50 tools were broken at different cutting conditions while an aluminum (5052) and mild steel workpieces were cut with an HSS micro-end-mill of 0.015 in. diameter. A HSS tool with a 0.03 in. diameter was used to cut a graphite workpiece. A typical raw cutting force signal and its moving average are presented in Fig. 2 for aluminum without any offset and scale-related correction. The cutting force had a repetitive pattern (almost sinusoidal) in each rotation since two cutting edges were engaged in turn with the workpiece. Also, the moving average of the signal is plotted in Fig. 2 to show the trend of the static part or very low-frequency characteristics
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Fig. 2.
Raw cutting force data.
of the signal. To study the static cutting force characteristics at different cutting conditions, the averages of 80 point long segments of the feed direction cutting force were calculated; these are presented in Figs 3 and 4. The cutting conditions for low-speed cutting tests are presented in Table 1. The spindle speed and depth of cut were 30,000 rpm and 0.004 in. respectively for the high-speed cutting tests respectively. The feed rates were 2 in./min for aluminum and 1 in./min for mild steel. 6.2. Tool breakage mechanisms The variation of the cutting force just before the breakage is demonstrated in Figs 3–6. The experiments were repeated at 30,000 rpm with aluminum and mild steel workpieces. All plots Table 1 The experimental cutting conditions for low-speed cutting experiments with aluminum material Data WAVE-01 WAVE-23 WAVE-31 WAVE-33 WAVE-35 WAVE-39 WAVE-43
Cutting speed (rpm) (W01) (W23) (W31) (W33) (W35) (W39) (W43)
3000 3000 3000 3000 3000 3000 3000
Feed rate (in./s) 0.005 0.003 0.003 0.003 0.003 0.001 0.001
Depth of cut (in.) 0.005 0.010 0.015 0.015 0.015 0.015 0.015
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Fig. 3.
Fig. 4.
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The segmental averages of the feed direction cutting force. Fatigue-related breakage occurred.
The segmental averages of the feed direction cutting force. Excessive stress-related breakage occurred.
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Fig. 5. Comparison of two different cases demonstrating the cutting forces at breakage while machining aluminum.
Fig. 6. Characteristic trend of segment averages of cutting forces in mild steel.
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show that the static part of the force increased after the tools were used for a period of time. When the static part of the cutting force increases the stress at the tool shaft increases. Since the tool rotates, the stress distribution at the cutting tool will vary and a repetitive load is encountered. The direction and magnitude of the cutting force is going to have a repetitive pattern while the tool is rotating. The static part of the force had a constant value and breakage occurred without the force reaching very high values in the four cases presented in Fig. 3 and one of the cases (Tool No. 1) presented in Fig. 5. In Fig. 4 and the other case (Tool No. 11) presented in Fig. 5, the absolute value of the static part of the feed direction cutting force increased and breakage occurred more quickly than the cases in Fig. 3 because of excessive stress. In Fig. 6 the existence of similar characteristics is demonstrated for mild steel. The reader should consider the absolute value of the force when we refer to the increase or decrease. In Fig. 4 for example, the absolute value of the cutting force increased until failure occurred. In all the plots, the force was negative because of the selected positive direction. Figs 3–6 verify the following facts: 1. As the tools were used, the static part of the feed direction cutting force signal increased (Figs 3–6). 2. The static part of the feed direction cutting force was constant until fatigue-related breakage occurred in Figs 3 and 5 (Tool No. 1) for five different cases. 3. The static part of the feed direction cutting force (absolute value) increased rapidly until the tool broke with excessive stress in Figs 4 and 6. To investigate the wear and breakage mechanisms, pictures of the tools were taken using an electron scanning microscope. The tip of a relatively new cutting tool (after 7.5 in. of cutting on aluminum material) is presented in Fig. 7(a). Fragments of the workpiece material can be seen adhering to the surface of the cutting edges. The surface is fairly flat indicating the almost perfect condition of the tool. Only very minor damage was observed at the cutting edges. The tip of another cutting tool is presented in Fig. 7(b) after 17.5 in. of cutting. A very large amount of small workpiece particles adhered to the surface. In addition, the not so straight cutting edges indicate the dullness and the possibly high cutting forces. In Fig. 7(c) a new and worn 0.03 in. end-mills are compared in this picture which was taken by optical microscope. The worn tool was used for more than 4 h to machine a graphite electrode. Portions of the cutting edges of the used tool were eroded. Typically, micro-tools that are used to cut metals are broken before they are this badly worn out. These observations verify the explanations in Section 4 about the usage-related changes in the tool, an increase in the cutting forces and the development of higher stress on the tool shaft. The higher stresses eventually cause fatigue or excessive force-related tool breakage. The trend of the cutting force variations in Fig. 4 is studied in Fig. 8. It presents a linear fit of exponential functions to data just before failure. The linear functions [Fig. 8(a)] fit the small sections better, and exponential functions fit the complete curve better. These observations suggest that in the short term damage reduces metal removal and at every single rotation the tool will be bent slowly because of the constant feed rate. This mechanism creates a linearly increasing static force (or stress) before the tool is broken. On the other hand, the better fitting exponential function [Fig. 8(b)] to the whole segment of data indicates that when damage or chip clogging occurs, the
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Fig. 7. Pictures of new and worn tools. (a) The tip of a micro-tool after 7.5 in. of cutting in aluminum material. (b) The tip of a micro-tool after 17.5 in. of cutting in aluminum material. (c) Comparison of a new tool with a worn one which was used to cut graphite electrode for more than 4 h.
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Fig. 8. The trend of the feed direction cutting force just before excessive stress-related breakage occurs. (a)Linear approximation. (b)Exponential approximation.
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tool will eventually cause problems and the condition of the tool deteriorates. For example, the increasing cutting forces may create more damage at the other sections of the cutting edges. 6.3. Performance of the proposed breakage prediction method Monitoring the static part of the feed direction cutting force was very effective in estimating the tool breakage ahead of time. In all seven cases presented (Figs 3 and 4), the static part of the force just one second before the breakage was higher than typical values with a new tool. A selected threshold of − 2.5 N was sufficient to detect the deteriorating cutting edges in Fig. 3 if the cutting edges are only slightly dull or dysfunctional (with damage or with little chips stuck to them). Excessive stress-related breakage was estimated from the trend of the static part of the feed direction cutting force. In Fig. 9, two and four-steps-ahead predictions from the segmental averages (see Section 5.1 for the procedure) are compared with the actual values. The accuracy of the estimations were satisfactory to take proper action 0.16 and 0.32 s ahead. Depending on the speed of the available hardware (controller and machine tool) two or four segmental averages ahead predictions can be used. In Section 5.2, the use of wavelet transformation coefficients was proposed to forecast 0.32 s ahead. In this section, the original digitized numbers (2-byte integers) are used to demonstrate more realistic signal processing since in an actual application, digitized numbers are used directly without wasting computational time with scaling and offsetting. In Fig. 10(a), the wavelet coefficients were calculated from the experimental feed direction cutting force data and presented. In
Fig. 9.
The two and four-steps-ahead predictions from the segmental averages.
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Fig. 10. Wavelet coefficients. Arbitrary Y-axis without offset and scale. (a)Wavelet coefficients from the experimental feed direction cutting force data. (b)The original data and the reconstructed waveform are presented just before and after breakage takes place.
Fig. 10(b), to show the accuracy and function of the wavelet transformation, the original data and the reconstructed waveform are presented just before and after breakage takes place. The reconstruction was realized by using Eq. (2) and part of the coefficients (just around breakage) in Fig. 10(a). The accuracy of the 0.32 s ahead estimations are presented in Fig. 11. Wavelet coefficients smoothed the data and provided reasonable estimations. The segmental averaging and wavelet transformation-based estimations had comparable accu-
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Fig. 11. The accuracy of the 0.32 s ahead predictions from wavelet transformation. Arbitrary Y-axis without offset and scale.
racy. Although segmental averaging is presently much more straightforward than the wavelet transformation method of present-day commercial microcomputers, if wavelet transformation hardware becomes available at reasonable prices, these calculations can be performed much faster using parallel processors. Also, instead of the Haar transformations, special waveforms may be designed to obtain better accuracy. Time-series models have also been used for forecasting [25]. However, time-series models require continuous updating of the model and the accuracy of the estimations begins to deteriorate after the first two predictions. A linear model is simpler to program and the accuracy of future estimations after many steps ahead are still reasonable as long as the trend of the data has not changed. 7. Conclusion The tool breakage mechanisms in micro-end-milling operations were investigated in this study. A relationship was found between tool condition (wear, cutting edge-damage, little particles stuck to the tool surface), and the static part of the feed direction cutting force. Usage-related stress was found to be an important factor in fatigue and excessive load-related micro-tool breakage. Two tool breakage prediction methods were proposed by using segmental averages and wavelet transformations. The static part of the feed direction force was found to be a simple and reliable indicator of
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tool condition in slot cutting operations. The deteriorating tool condition can be detected by monitoring the absolute value of the static part of the feed direction force. When it increases more than three or four times while the cutting conditions are the same, appropriate actions should be taken to avoid breakage. Two methods were proposed for estimation of the static part of the cutting force in 0.16–0.32 s ahead. The first method calculates the averages of 80 s-long feed direction cutting force segments and predicts the future averages 0.16 and/or 0.32 s ahead. The other prediction method calculates the wavelet transformation and predicts the future values in about 0.32 s ahead. Both approaches estimate the future values with acceptable accuracy. The number of manufactured miniature parts and intricate sections of conventional designs have been increasing dramatically. To produce these parts, micro-machining operations will be used more commonly in the future. Especially by adding high-speed spindles to the conventional machine tools reasonable feed rates with very small feed-per-tooth can be achieved. To increase the feed rates further on these machines, monitoring of the feed direction cutting force will be useful for predicting breakage and taking proper action. Also, many manufacturers will feel pressure to use conventional machine tools for micro-machining applications either because of their limited budget or to eliminate time consumption by setting up the same parts on several machines. Monitoring of the static part of the feed direction cutting force and adjustment of the feed rate during machining will allow use of high feed rates, increase tool life, and minimize breakage during the machining of a part.
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