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CUTTING PROCESS DIAGNOSTICS UTILISING A SMART CUTTING TOOL
Mechanical Systems and Signal Processing (2002) 16(2–3), 475–486 doi:10.1006/mssp.2001.1453, available online at http://www.idealibrary.com on
CUTTING PROCESS DIAGNOSTICS UTILISING A SMART CUTTING TOOL B.-K. Min, G. O’Neal, Y. Koren and Z. Pasek Department of Mechanical Engineering, The University of Michigan, NSF ERC for Reconfigurable Machining Systems, 2250 Hayward, Ann Arbor, MI 48109-2125, USA. E-mail: [email protected] (Received 26 February 2001, accepted 26 September 2001) A new sensorised boring tool has been developed to enhance the flexibility and precision of line boring processes. A position sensor and tool tip actuator to control and its controller electronics were integrated into a cutting tool. To take further advantage of this highly sophisticated tool, a process diagnostic method based on cutting force measurement method was developed. An observer implemented in the tool controller estimates the disturbance to the actuator using the tool position sensing information. The estimated disturbance is the same as cutting force applied to the tool tip. The algorithm utilising the cutting force to detect cutting process faults, such as tool tip breakage and misalignment of workpiece has been designed and tested experimentally. # 2002 Elsevier Science Ltd. All rights reserved.
1. INTRODUCTION
Process diagnostic capability is an essential function in automated manufacturing plants. Rapid detection and isolation of failures and their root causes increase process reliability and productivity. In machining process, tool breakage and faulty tool condition are critical failures affecting part quality, and therefore, have been intensively investigated during the past three decades. Various direct and indirect monitoring devices, including acoustic sensors, tool dynamometers, force sensors, and machine tool motor current sensors have been used for tool condition monitoring for diagnostics [1, 2]. Among these, motor power and torque are commonly monitored signals in practical applications. Even though the tool dynamometer has demonstrated good performance for tool condition monitoring, adding a tool dynamometer to a machine tool is not always feasible due to the difficulties in the set-up and its effect on the machine tool dynamics. In addition, the high cost of the tool dynamometer makes its use uneconomical in industrial applications. Recently introduced commercial tool condition monitoring systems using load cells are more affordable than the tool dynamometer [3]. A drawback of existing monitoring methods described above is that they require additional equipment. This paper introduces a new cutting force measurement method utilising tool servo that is directly implemented inside the cutting tool itself and its application to the process diagnostics. The proposed method is integrated to a prototype cutting tool with fast tool servo. This new tool, called Smart Tool, has been recently developed by the authors [4, 5] to improve the flexibility and productivity of line boring process. This diagnostic function improves the Smart Tool reliability and reduces the need for external monitoring equipment. 0888–3270/02/+$35.00/0
# 2002 Elsevier Science Ltd. All rights reserved.
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In Section 2, the line boring process is explained and the structure of the Smart Tool is presented as an improvement of the traditional boring tool. Section 3 focuses on the design of an on-tool cutting force estimator as a monitoring method for process diagnostics. Section 4 discusses the diagnostic algorithm with the experimental results.
2. SMART BORING TOOL SYSTEM
Boring tools are recognised as a major bottleneck in increasing the flexibility and precision of line boring processes in automotive engine manufacturing plants. Line boring process refers to a consecutive machining of bores with the same diameter. In the line boring process, typically the boring bar is supported on both ends to avoid droop. This outboard support bearing required in current tooling is one of the major obstacles to automated tool changes. Smart Tool has been developed to achieve agility at the boring station. The goal of development is elimination of support bushings to enable the automated tool change of long boring bars (i.e. the tool is supported only at one end). An on-line compensation mechanism relying on active tool-tip servo using a piezoelectric actuator and tool tip deflection measurement sensor is used to compensate for the increased compliance of the long boring bar without support. An overview of tool tip servo mechanism of the prototype Smart Tool line boring system layout is presented in Fig. 1. The tool body, similar to the one that appeared in reference [6], contains a position sensor and the actuation mechanism. The instrumentation package (including a PC-based embedded controller using PC/104 computer, tool-tip position sensor electronics, a wireless transceiver, and power electronics) is integrated with the boring tool. The cutting tool controller is implemented on the PC/104 computer and has an analog interface. The digital control loop runs at a 150 ms sampling rate. The tool-tip position compensation algorithm is programmed into the controller computer and stored in a flash memory that is included in the computer. A piezoelectric actuator was chosen as
Flexure hinge mechanism Wireless transceiver
Workpiece Smart tool boring bar
Actuator amplifier
Piezoelectric actuator
PC/104 controller computer Sensor interface
Capacitance proximity sensor
Instrumentation package Figure 1. Smart Tool prototype and its controller for the experimental set-up.
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the tool-tip actuator because these devices have high operating frequencies and high output energy per unit volume. The accuracy of the tool tip position sensor (capacitancetype) is about 0.1 mm. A tool tip translation mechanism actuates the cutting insert in the depth of cut direction. The Smart Tool controller communicates with the machine tool controller using a wireless transceiver.
3. FORCE MEASUREMENT USING DISTURBANCE ESTIMATION IN CUTTING TOOL
This section describes the designing disturbance observer [7] and use of estimated cutting force for the cutting process diagnostics. Measuring the radial cutting force with a conventional machine tool requires a special equipment, such as a tool dynamometer or spindle torque monitor. However, the Smart Tool system can perform the dual roles of real-time tool-tip position controller and cutting force estimator. Because of its lever mechanism between the cutter and tool actuator in the Smart Tool design, only the radial direction element of the cutting force acts as disturbance to the tool servo. Therefore, in this section only the radial cutting force is referred to as a cutting force. The details of modelling and of the controller design are given in the previous work of the authors [5]. Since the Smart Tool uses only a single sensor to measure tool-tip position, an observer must be used to estimate the remaining states for full state feedback control. The observer model used in the full state feedback control is now expanded to include cutting force dynamics as well as the dynamics of the Smart Tool. The model of the Smart Tool system is given by " # " # " # 0:8464 0:0228 19:02 0:1420 xðk þ 1Þ ¼ xðkÞ þ uðkÞ þ wðkÞ 7:3561 0:8628 75:77 0:5659 yðkÞ ¼ 1
0 xðkÞ
ð1Þ
where yðkÞ is the estimated tool-tip position, uðkÞ is the command voltage input into the Smart Tool, oðkÞ is the cutting force input into the Smart Tool. The values for the system matrices were determined based on the design parameters of the Smart Tool and then refined by system identification experiment which applied static cutting force to the Smart Tool. The Smart Tool system equation represented by equation (1) is expanded with cutting force disturbance model. A discrete state space model of the cutting force disturbance is generated by assuming that the disturbance has the following structure: xd ðk þ 1Þ ¼ M xd ðkÞ ok ðkÞ ¼ Hd xd ðkÞ:
ð2Þ
oðkÞ is the estimate of the cutting force, xd is a generalised state vector of the cutting force as a disturbance to the Smart Tool, and the matrix Hd relates xd to oðkÞ. The form of the matrix M in equation (2) to describe disturbance dynamics depends on what is perceived to be the dominant dynamics of the cutting force. For the Smart Tool controller, the cutting force is assumed to be predominantly composed of a sinusoidal and a constant component, due to the rotation of the spindle during the boring process.
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The sinusoidal component of the cutting force can generally be modelled using the state space model in equation (3), which has an impulse response that is a sinusoid. " # # " xd1 ðk þ 1Þ xd1 ðkÞ 0 1 ¼ : ð3Þ xd2 ðk þ 1Þ xd2 ðkÞ 1 2 cosðTs o0 Þ |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} F
This system in equation (3) has sinusoidal response with a frequency of o0 , which in our case equals the frequency of the spindle rotation. Ts is the sampling time of the controller. Then, the constant component of the cutting force can be represented as xd3 ðk þ 1Þ ¼ xd3 ðkÞ:
ð4Þ
To model the cutting force with sinusoidal and constant components using equations (3) and (4), the M and Hd in equation (2) have to have following form. " # F 0 M ¼ 0 1 Hd ¼ 0
1
1 :
ð5Þ
The dynamics of the Smart Tool can be augmented with the cutting force model to obtain the comprehensive state space model given in equation (6). The state vector, xe, for this model includes the states of both the Smart Tool and the cutting force. 3 3 2 2 0:8664 0:0228 0 0:1420 0:1420 19:02 7 7 6 6 0:5659 0:5659 7 6 75:77 7 6 7:3561 0:8628 0 7 7 xðkÞ 6 xðk þ 1Þ 6 ¼6 0 0 0 1 0 7 þ6 0 7 7 uðkÞ 7 6 6 xd ðk þ 1Þ xd ðkÞ 7 7 |fflfflfflffl 6 |fflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflffl} 6 0 0 1 2 cosðTs o0 Þ 0 5 ffl{zfflfflfflfflffl} 4 0 5 4 xe xe 0 0 0 0 0 1 |fflfflfflfflfflfflffl{zfflfflfflfflfflfflffl} |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} Be
Ae
yðkÞ ¼ 1 0 0 0 0 |fflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} Ce
xðkÞ : xd ðkÞ |fflfflfflffl{zfflfflfflffl}
ð6Þ
xe
The command voltage is the only input into the augmented system while the cutting force input (disturbance as an input to the augmented system in equation (6)) has been internalised. The output of this model, yðkÞ, is the tool tip position. Since x and xd are observable, a Kalman filter [8] can be designed to estimate these states in real time. The observer has the following form: x# e ðk þ 1Þ ¼ ½Ae L Ce Ae x# ðkÞ þ ½Be L Ce Be uðkÞ þ Lye ðk þ 1Þ:
ð7Þ
The Kalman filter gain L is chosen to minimise the expected values for the square of the errors between the actual and the estimated states. These errors by the observer are assumed to come from plant disturbances and measurement noise, which is assumed to be white noise with Gaussian distributions. The controller can be properly tuned to achieve a compromise between fast estimator dynamics and filtering of the sensor signal. Figure 2 shows a block diagram of disturbance estimation of a Smart Tool with its tool tip control loop. The K1 and K2 in the figure are the feedback gains. Force measurement is important to monitor the cutting process. As can be seen in Fig. 2, the cutting force is estimated in real-time, and is fed to the diagnostic algorithm along with the process information (such as spindle speed, expected force and current operation mode) sent from
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CUTTING PROCESS DIAGNOSTICS UTILISING A SMART CUTTING TOOL
Position reference
+
Cutting force
+
Input voltage to servo
Tool tip position
Tool tip servo
+ + K2
K1
Boring bar states
Observer Tool tip servo state Disturbance Diagnostic algorithm
Estimated cutting force
Diagnostic Machine tool output controller
Process information Figure 2. Cutting force estimation.
160 140 120 100
Force (N)
80 60 40 20 0 -20 -40 0
0.02
0. 04
0.06
0.08
0.1
0.12
0.14
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0.2
Time (sec)
Figure 3. Experimental cutting force measurement: }, smart tool; ...., tool dynamometer; –.–, difference.
the stationary main machine tool controller (for machine tool linear axes, power, etc.). The diagnostic algorithm analyses the force signal based on the method described in the next section in order to identify the tool failures. Figure 3 shows a comparison between the radial cutting force measured by a tool dynamometer (dashed line) and one derived by the disturbance estimation method (solid line) in the Smart Tool. The tool dynamometer signal has been filtered using a low pass filter with a 250 Hz cut-off frequency. The cutting speed was 2 m/s and the feedrate was
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Table 1 Failure modes in boring process Step
Operation
Failure modes
1 2 3 4
Part positioning Rough tool positioning Rough boring Finish tool positioning
Part positioning error Tool positioning error Rough tool breakage Tool positioning error
5
Finish boring
Finish tool breakage
Cutting force effects at finish boring None Irregular cutting force Change in cutting force Irregular (and excessive) cutting force Excessive cutting force
fixed at 0.083 mm per revolution. An irregular depth of cut was used to generate a time varying cutting force. As can be seen in the figure, there is a excellent fit between the two curves (the difference is less than 10% of cutting force in RMS) and therefore the force estimation using disturbance measurement can be used to detect both the dynamic and the static cutting forces.
4. CUTTING PROCESS DIAGNOSTICS
Determination of the potential failure modes is the first step in diagnostics system design. Table 1 shows a typical boring process with its operations, failure modes, and the effect of the failure at the finish boring operation. Some failures, such as the rough tool breakage failure, may occur before the finish boring operation, and the process may proceed to finish boring operation without prior detection of such failures. The relationships between the cutting force and the failure modes are detailed in this section. 4.1. TOOL BREAKAGE DETECTION USING CUTTING FORCE Cutting force monitoring is frequently used to detect tool breakage. Therefore, cutting force behaviour during tool breakage is well studied in literature. According to references [9, 10], the tangential cutting force becomes zero during a few revolutions after a breakage because no contact between the workpiece and the tool exists, then increases when contact with the broken tool is made again. However, the force drop in the radial direction due to the loss of the tool tip does not always appear as in the case of the tangential cutting force. In the finish boring process with Smart Tool, a jump of radial cutting force immediately after a tool fracture or chipping is observed. With a small depth of cut in the boring processes, most of the tool breakage cases occur due to excessive cutting force from (i) poor quality of the surface after rough boring or (ii) incorrect finish boring tool position. A sharp rise in the radial cutting force generally indicates finish tool breakage and the machine tool needs to be shut down immediately. 4.2. TOOL POSITION ERROR DETECTION USING CUTTING FORCE Two possible bored-hole geometry errors due to cutting process failures are assumed for Smart Tool diagnostics. Figure 4(a) depicts the case of the rough tool breakage failure during the boring operation resulting in a change in the depth of cut and the surface quality of the finish boring operation. Figure 4(b) shows the geometry of the hole after the finish boring when the centre of the rough boring position and the finish boring position do not match. As a result, the depth of cut of the finish boring process becomes a periodic
CUTTING PROCESS DIAGNOSTICS UTILISING A SMART CUTTING TOOL
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Figure 4. Boring hole shape after failure, (a) cutting surface change after failure, (b) non-uniform depth of cut due to hole-centre position error.
function that repeats every revolution of the spindle. The position errors in the rough and finish boring tools are the primary causes of this phenomenon. Tool wear of either finish or rough boring cutters can also result in the depth of cut change. However, these failure modes are not considered in this paper because the impact is too small to be detected. 4.3. ISOLATION OF THE FAILURE MODES Since cutting force is a monotonic function of the depth of cut, if the cutting force is accurately measured the quality and geometric profile of the cutting surface can be calculated from the force data. For the diagnostics of rotating boring tool, force pattern in a single revolution of the tool can be utilised because the pattern is different for each failure mode. Since the spindle speed is transmitted to the controller of the Smart Tool from the stationary spindle controller, the controller computer inside the boring tool can recognise the force pattern for each revolution.
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The Smart Tool diagnostics detects the failures and isolates the failure mode using force pattern. The diagnostic algorithm continuously checks whether the cutting force is greater than a critical value, in which case the finish tool is broken and the machine must be immediately shut down. If the force is below the critical value but greater than that of normal cutting condition, the algorithm identifies if the failure mode is rough tool breakage or tool positioning error. Two types of force patterns described in Section 4.2 are utilised for the algorithm: (i) step change due to tool breakage and (ii) periodic signal due to incorrect tool position. A band-pass filter may be used to isolate the periodic component from the cutting force signal to detect the tool position failure. However, it is difficult to detect the force difference from step changes due to tool breakage using the simple band-pass filter only. Therefore, quantitative measurement of the signal is still required. To evaluate the force pattern quantitatively, two functions DðiÞ and GðiÞ are introduced. DðiÞ responds to the step changes only and thus can be used to detect the tool breakage failure. In contrast, GðiÞ responds to the periodic signals only to detect tool position errors. The DðiÞ and GðiÞ act similar to filters, and it gives the quantitative measures required for diagnostic algorithm directly as well. The DðiÞ shows the change in cutting force between the previous revolution and the current revolution of the tool such that DðiÞ ¼ f%i f%it ð8Þ P where i is an index of revolution of the tool, f%i ¼ N j¼1 fj =N, fj ¼ force measurement at each sample, N is the number of samples per revolution, and t is the number of revolutions of the tool between the cutting force averages being computed. The function DðiÞ is used to detect a relative cutting force change during the process. To avoid comparison of the cutting forces in the transient of a failure, the t in equation (8) is introduced. Thus, it compares the average cutting force of the ith revolution and the ði tÞth revolution where the failure is fully developed. Minimum value of t is selected based on spindle rotation speed. Since tool breakage is a permanent failure no maximum value of t needs to be determined. DðiÞ value greater than the predetermined level indicates the significant cutting force change that cannot occur in a normal cutting process, such as the case depicted in Fig. 4(a). Function GðiÞ is the measure of cutting force irregularity at one revolution with respect to tool-tip angle when each revolution of the tool is divided by three sections as in equation (9). GðiÞ ¼ max f#j f#k : ð9Þ j:k¼0;1;2
PN=3
i is an index of revolution of tool, f#j ¼ l¼1 flþðjN=3Þ =ðN=3Þ, fi the force measurement at each sample, and N is the number of samples per revolution. The GðiÞ value represents differences between three average cutting force readings obtained from three equally distributed angles at each rotation. This is in contrast to the DðiÞ that compares the cutting force between rotations. A greater GðiÞ indicates a greater irregularity of the cutting force during a single rotation of the cutting tool, which is due to the case shown in Fig. 4(b). When a tool positioning error exists, only the GðiÞ value becomes significantly greater than zero, but not the DðiÞ value because the average cutting force for each revolution remains the same. On the other hand, an undersized hole due to a breakage of the rough tool changes only the DðiÞ because the depth of cut is evenly increased around the angular positions of the hole. Therefore, the behaviours of the GðiÞ and DðiÞ functions are used to
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Set threshold of tool breakage force, Γ(i), ∆ (i)
Estimate cutting force fi
If fi > breakage force
Yes
Tool breakage
No Calculate
Γ(i) and ∆(i)
If Γ(i) > threshold
Yes
Rough tool breakage
Yes
Tool position error
No If ∆(i) > threshold No
Figure 5. Failure mode isolation algorithm.
detect and identify the root cause of various failures. The threshold level of the GðiÞ and DðiÞ for failure detection must be determined according to the cutting condition. The flowchart in Fig. 5 summarises the failure mode isolation algorithm used in the control computer. Figures 6–8 show the results of the diagnostic experiments of three different cases of cutting process failures. Each graph displays the cutting forces estimated by the Smart Tool estimation method, and the two diagnostic functions GðiÞ and DðiÞ. For all three cases, the cutting speed was 2 m/s and the feedrate was 0.083 mm per revolution. The number t for the DðiÞ function was set to 4. The threshold level of GðiÞ and DðiÞ was set to 25 N, which is 50% of the normal cutting force in this experiment. The threshold level of finish tool breakage detection was set to 100 N. 4.4. EXPERIMENTAL VERIFICATIONS Figure 6 demonstrates the case in which a failure in the previous cutting pass resulted in a 2.0 mm undersized hole in the middle of cutting. As can be seen in the graph, only the DðiÞ value, which is sensitive to a change of depth of cut during several successive revolutions, became greater at the moment of change in the depth of cut and the failure was identified as the rough tool breakage failure.
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100
Force (N)
Error detected
50
0 0
1
2
3
4
5
6
7
8
9
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Spindle rotation
Figure 6. Cutting force estimation and diagnostic function: rough tool breakage in previous cut (depth of cut changes from 0.25 to 2.25 mm; feedrate: 0.083 mm/rev; cutting speed: 2 m/s): }, smart tool force estimation; – – , G (i); –n–, D (i); – – –, threshold of G (i) and D (i).
250
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Force (N)
150 Tool breakage detected 100
50
0 0
1
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Spindle rotation Figure 7. Cutting force estimation and diagnostic function: finish tool breakage (depth of cut: 0.76 mm; feedrate: 0.083 mm/rev; cutting speed: 2 m/s): }, smart tool force estimation; – –, G (i); –n–, D (i); – –, threshold of G (i) and D (i).
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150
Error detected
Force (N)
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0 0
0.5
1
1.5
2
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Spindle rotation Figure 8. Cutting force estimation and diagnostic function: finish boring tool position error (depth of cut: 0.25 mm; feedrate: 0.083 mm/rev; cutting speed: 2 m/s; tool offset: 1.27 mm): }, smart tool force estimation; – –, G (i); –n–, D (i); – –, threshold of G (i) and D (i).
The event of breakage of the finish cutting tool is captured in Fig. 7. To expedite tool breakage, an insert with a 2.0 mm notch on the tool tip was used. The boring hole was machined with a 0.76 mm depth of cut until a fatigue fracture of the tip occurred. Since the cutting force due to the broken tool was greater than the critical force, the tool breakage failure was immediately detected regardless of the behaviour of DðiÞ and GðiÞ. Figure 8 shows the case when the boring tool had a 1.27 mm position error. In this case, as can be seen in the figure, the cutting force became a periodic function. Therefore, only the GðiÞ value, which indicates the variation of cutting force during one revolution (caught by the variation of the depth of cut), responded. However, the DðiÞ remains small because the average cutting force at each revolution was constant. These experimental results propose that the combination of the GðiÞ and DðiÞ, which is calculated in the Smart Tool from the force estimation, is a good indicator to identify the root causes of the failure.
5. SUMMARY
Diagnostic capability is one of the most important features in automated machine tool systems and it is critical when more sophisticated electronics is used to control the process. This paper has elaborated on the failure detection and a root-cause isolation method, which utilises the process information and monitoring of the cutting force. A new measurement technique of the cutting force based on disturbance estimation was developed and integrated into a sensorised boring tool.
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The experimental results demonstrated that the indirect force measurement by the proposed method matches well with the conventional direct measurement using a tool dynamometer. The developed accurate cutting force estimation method is not only useful for failure monitoring, but it is also useful as a substitute for tool dynamometers, especially in cases where the tool dynamometer is difficult to be placed, such as in a rotating tool tip. The proposed cutting process diagnostic method was effective in detecting several modes of tool failure as well as other process failures which occurred in the boring process. The electronic package and the new algorithm embedded in the Smart Tool made it possible to build a self-contained tool-tip position controller with diagnostics inside a rotating cutting tool, thus to increase the reliability and the productivity of the boring process.
ACKNOWLEDGEMENTS
The research was sponsored in part by the ERC for Reconfigurable Machining System under NSF grant # EEC-9529125. The Smart Tool research presented in this paper was sponsored in part by NIST ATP grant # 70NANB5H1158. The authors appreciate the support of Dr Phil Szuba from Lamb Technicon.
REFERENCES 1. G. Byrne, D. Dornfeld, Z. Znasaki, G. Ketteler, W. Konig and R. Teti 1995 Annals of the CIRP 44, 541–567. Tool conditioning monitoring (TCM)} the status of research and industrial application. 2. Y. Koren, T. R. Ko, G. Ulsoy and K. Danai 1991 Transactions of ASME}Journal Dynamics, Systems, and Control 113, 300–307. Tool wear estimation under varying cutting conditions. 3. K. Jemielniak 1998 International Conference on Monitoring and Automatic Supervision in Manufacturing, AC ’98, pp. 59–69. Commercial tool condition monitoring systems. 4. P. S. Szuba, B.-K. Min, G. O’Neal, Z. J. Pasek and Y. Koren 2000 US Patent 6,062,778. Precision positioner for a cutting tool insert. 5. G. P. O’Neal, B.-K. Min, C.-J. Li, Z. J. Pasek, Y. Koren and P. Szuba 1998 Proceedings of the ASME International Mechanical Engineering Congress and Exposition, Vibration and Nosise Control, DSC, Vol. 65, pp. 99–106. Precision piezoelectric micro-positioner for line boring bar tool insert. 6. R. D. Hanson and T. Tsao 1994 ASME International Mechanical Engineering Congress and Exposition DSC, Vol. 55-2, Dynamic Systems and Control: Vol. 2, pp. 863–871. Development of a fast tool servo for variable-depth-of-cut machining. 7. G. F. Franklin, J. D. Powell and M. Workman 1998 Digital Control of Dynamic Systems, pp. 322–337. Reading, MA: Addison-Wesley. 8. C. Phillips and H. Nagle 1995 Digital Control System Analysis and Design, pp. 413–420. Englewood Cliffs, NJ: Prentice-Hall. 9. M. Shiraishi 1988 Precision Engineering 10(4), 179–189. Scope of in-process measurement, monitoring and control techniques in machining processes}Part 1: in-process techniques for tools. 10. M. S. Lan and D. A. Dornfeld 1984 Journal of Engineering Materials and Technology 106, 111–118. In-process tool fracture detection.
CUTTING PROCESS DIAGNOSTICS UTILISING A SMART CUTTING TOOL B.-K. Min, G. O’Neal, Y. Koren and Z. Pasek Department of Mechanical Engineering, The University of Michigan, NSF ERC for Reconfigurable Machining Systems, 2250 Hayward, Ann Arbor, MI 48109-2125, USA. E-mail: [email protected] (Received 26 February 2001, accepted 26 September 2001) A new sensorised boring tool has been developed to enhance the flexibility and precision of line boring processes. A position sensor and tool tip actuator to control and its controller electronics were integrated into a cutting tool. To take further advantage of this highly sophisticated tool, a process diagnostic method based on cutting force measurement method was developed. An observer implemented in the tool controller estimates the disturbance to the actuator using the tool position sensing information. The estimated disturbance is the same as cutting force applied to the tool tip. The algorithm utilising the cutting force to detect cutting process faults, such as tool tip breakage and misalignment of workpiece has been designed and tested experimentally. # 2002 Elsevier Science Ltd. All rights reserved.
1. INTRODUCTION
Process diagnostic capability is an essential function in automated manufacturing plants. Rapid detection and isolation of failures and their root causes increase process reliability and productivity. In machining process, tool breakage and faulty tool condition are critical failures affecting part quality, and therefore, have been intensively investigated during the past three decades. Various direct and indirect monitoring devices, including acoustic sensors, tool dynamometers, force sensors, and machine tool motor current sensors have been used for tool condition monitoring for diagnostics [1, 2]. Among these, motor power and torque are commonly monitored signals in practical applications. Even though the tool dynamometer has demonstrated good performance for tool condition monitoring, adding a tool dynamometer to a machine tool is not always feasible due to the difficulties in the set-up and its effect on the machine tool dynamics. In addition, the high cost of the tool dynamometer makes its use uneconomical in industrial applications. Recently introduced commercial tool condition monitoring systems using load cells are more affordable than the tool dynamometer [3]. A drawback of existing monitoring methods described above is that they require additional equipment. This paper introduces a new cutting force measurement method utilising tool servo that is directly implemented inside the cutting tool itself and its application to the process diagnostics. The proposed method is integrated to a prototype cutting tool with fast tool servo. This new tool, called Smart Tool, has been recently developed by the authors [4, 5] to improve the flexibility and productivity of line boring process. This diagnostic function improves the Smart Tool reliability and reduces the need for external monitoring equipment. 0888–3270/02/+$35.00/0
# 2002 Elsevier Science Ltd. All rights reserved.
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In Section 2, the line boring process is explained and the structure of the Smart Tool is presented as an improvement of the traditional boring tool. Section 3 focuses on the design of an on-tool cutting force estimator as a monitoring method for process diagnostics. Section 4 discusses the diagnostic algorithm with the experimental results.
2. SMART BORING TOOL SYSTEM
Boring tools are recognised as a major bottleneck in increasing the flexibility and precision of line boring processes in automotive engine manufacturing plants. Line boring process refers to a consecutive machining of bores with the same diameter. In the line boring process, typically the boring bar is supported on both ends to avoid droop. This outboard support bearing required in current tooling is one of the major obstacles to automated tool changes. Smart Tool has been developed to achieve agility at the boring station. The goal of development is elimination of support bushings to enable the automated tool change of long boring bars (i.e. the tool is supported only at one end). An on-line compensation mechanism relying on active tool-tip servo using a piezoelectric actuator and tool tip deflection measurement sensor is used to compensate for the increased compliance of the long boring bar without support. An overview of tool tip servo mechanism of the prototype Smart Tool line boring system layout is presented in Fig. 1. The tool body, similar to the one that appeared in reference [6], contains a position sensor and the actuation mechanism. The instrumentation package (including a PC-based embedded controller using PC/104 computer, tool-tip position sensor electronics, a wireless transceiver, and power electronics) is integrated with the boring tool. The cutting tool controller is implemented on the PC/104 computer and has an analog interface. The digital control loop runs at a 150 ms sampling rate. The tool-tip position compensation algorithm is programmed into the controller computer and stored in a flash memory that is included in the computer. A piezoelectric actuator was chosen as
Flexure hinge mechanism Wireless transceiver
Workpiece Smart tool boring bar
Actuator amplifier
Piezoelectric actuator
PC/104 controller computer Sensor interface
Capacitance proximity sensor
Instrumentation package Figure 1. Smart Tool prototype and its controller for the experimental set-up.
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the tool-tip actuator because these devices have high operating frequencies and high output energy per unit volume. The accuracy of the tool tip position sensor (capacitancetype) is about 0.1 mm. A tool tip translation mechanism actuates the cutting insert in the depth of cut direction. The Smart Tool controller communicates with the machine tool controller using a wireless transceiver.
3. FORCE MEASUREMENT USING DISTURBANCE ESTIMATION IN CUTTING TOOL
This section describes the designing disturbance observer [7] and use of estimated cutting force for the cutting process diagnostics. Measuring the radial cutting force with a conventional machine tool requires a special equipment, such as a tool dynamometer or spindle torque monitor. However, the Smart Tool system can perform the dual roles of real-time tool-tip position controller and cutting force estimator. Because of its lever mechanism between the cutter and tool actuator in the Smart Tool design, only the radial direction element of the cutting force acts as disturbance to the tool servo. Therefore, in this section only the radial cutting force is referred to as a cutting force. The details of modelling and of the controller design are given in the previous work of the authors [5]. Since the Smart Tool uses only a single sensor to measure tool-tip position, an observer must be used to estimate the remaining states for full state feedback control. The observer model used in the full state feedback control is now expanded to include cutting force dynamics as well as the dynamics of the Smart Tool. The model of the Smart Tool system is given by " # " # " # 0:8464 0:0228 19:02 0:1420 xðk þ 1Þ ¼ xðkÞ þ uðkÞ þ wðkÞ 7:3561 0:8628 75:77 0:5659 yðkÞ ¼ 1
0 xðkÞ
ð1Þ
where yðkÞ is the estimated tool-tip position, uðkÞ is the command voltage input into the Smart Tool, oðkÞ is the cutting force input into the Smart Tool. The values for the system matrices were determined based on the design parameters of the Smart Tool and then refined by system identification experiment which applied static cutting force to the Smart Tool. The Smart Tool system equation represented by equation (1) is expanded with cutting force disturbance model. A discrete state space model of the cutting force disturbance is generated by assuming that the disturbance has the following structure: xd ðk þ 1Þ ¼ M xd ðkÞ ok ðkÞ ¼ Hd xd ðkÞ:
ð2Þ
oðkÞ is the estimate of the cutting force, xd is a generalised state vector of the cutting force as a disturbance to the Smart Tool, and the matrix Hd relates xd to oðkÞ. The form of the matrix M in equation (2) to describe disturbance dynamics depends on what is perceived to be the dominant dynamics of the cutting force. For the Smart Tool controller, the cutting force is assumed to be predominantly composed of a sinusoidal and a constant component, due to the rotation of the spindle during the boring process.
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The sinusoidal component of the cutting force can generally be modelled using the state space model in equation (3), which has an impulse response that is a sinusoid. " # # " xd1 ðk þ 1Þ xd1 ðkÞ 0 1 ¼ : ð3Þ xd2 ðk þ 1Þ xd2 ðkÞ 1 2 cosðTs o0 Þ |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} F
This system in equation (3) has sinusoidal response with a frequency of o0 , which in our case equals the frequency of the spindle rotation. Ts is the sampling time of the controller. Then, the constant component of the cutting force can be represented as xd3 ðk þ 1Þ ¼ xd3 ðkÞ:
ð4Þ
To model the cutting force with sinusoidal and constant components using equations (3) and (4), the M and Hd in equation (2) have to have following form. " # F 0 M ¼ 0 1 Hd ¼ 0
1
1 :
ð5Þ
The dynamics of the Smart Tool can be augmented with the cutting force model to obtain the comprehensive state space model given in equation (6). The state vector, xe, for this model includes the states of both the Smart Tool and the cutting force. 3 3 2 2 0:8664 0:0228 0 0:1420 0:1420 19:02 7 7 6 6 0:5659 0:5659 7 6 75:77 7 6 7:3561 0:8628 0 7 7 xðkÞ 6 xðk þ 1Þ 6 ¼6 0 0 0 1 0 7 þ6 0 7 7 uðkÞ 7 6 6 xd ðk þ 1Þ xd ðkÞ 7 7 |fflfflfflffl 6 |fflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflffl} 6 0 0 1 2 cosðTs o0 Þ 0 5 ffl{zfflfflfflfflffl} 4 0 5 4 xe xe 0 0 0 0 0 1 |fflfflfflfflfflfflffl{zfflfflfflfflfflfflffl} |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} Be
Ae
yðkÞ ¼ 1 0 0 0 0 |fflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} Ce
xðkÞ : xd ðkÞ |fflfflfflffl{zfflfflfflffl}
ð6Þ
xe
The command voltage is the only input into the augmented system while the cutting force input (disturbance as an input to the augmented system in equation (6)) has been internalised. The output of this model, yðkÞ, is the tool tip position. Since x and xd are observable, a Kalman filter [8] can be designed to estimate these states in real time. The observer has the following form: x# e ðk þ 1Þ ¼ ½Ae L Ce Ae x# ðkÞ þ ½Be L Ce Be uðkÞ þ Lye ðk þ 1Þ:
ð7Þ
The Kalman filter gain L is chosen to minimise the expected values for the square of the errors between the actual and the estimated states. These errors by the observer are assumed to come from plant disturbances and measurement noise, which is assumed to be white noise with Gaussian distributions. The controller can be properly tuned to achieve a compromise between fast estimator dynamics and filtering of the sensor signal. Figure 2 shows a block diagram of disturbance estimation of a Smart Tool with its tool tip control loop. The K1 and K2 in the figure are the feedback gains. Force measurement is important to monitor the cutting process. As can be seen in Fig. 2, the cutting force is estimated in real-time, and is fed to the diagnostic algorithm along with the process information (such as spindle speed, expected force and current operation mode) sent from
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Figure 3. Experimental cutting force measurement: }, smart tool; ...., tool dynamometer; –.–, difference.
the stationary main machine tool controller (for machine tool linear axes, power, etc.). The diagnostic algorithm analyses the force signal based on the method described in the next section in order to identify the tool failures. Figure 3 shows a comparison between the radial cutting force measured by a tool dynamometer (dashed line) and one derived by the disturbance estimation method (solid line) in the Smart Tool. The tool dynamometer signal has been filtered using a low pass filter with a 250 Hz cut-off frequency. The cutting speed was 2 m/s and the feedrate was
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Table 1 Failure modes in boring process Step
Operation
Failure modes
1 2 3 4
Part positioning Rough tool positioning Rough boring Finish tool positioning
Part positioning error Tool positioning error Rough tool breakage Tool positioning error
5
Finish boring
Finish tool breakage
Cutting force effects at finish boring None Irregular cutting force Change in cutting force Irregular (and excessive) cutting force Excessive cutting force
fixed at 0.083 mm per revolution. An irregular depth of cut was used to generate a time varying cutting force. As can be seen in the figure, there is a excellent fit between the two curves (the difference is less than 10% of cutting force in RMS) and therefore the force estimation using disturbance measurement can be used to detect both the dynamic and the static cutting forces.
4. CUTTING PROCESS DIAGNOSTICS
Determination of the potential failure modes is the first step in diagnostics system design. Table 1 shows a typical boring process with its operations, failure modes, and the effect of the failure at the finish boring operation. Some failures, such as the rough tool breakage failure, may occur before the finish boring operation, and the process may proceed to finish boring operation without prior detection of such failures. The relationships between the cutting force and the failure modes are detailed in this section. 4.1. TOOL BREAKAGE DETECTION USING CUTTING FORCE Cutting force monitoring is frequently used to detect tool breakage. Therefore, cutting force behaviour during tool breakage is well studied in literature. According to references [9, 10], the tangential cutting force becomes zero during a few revolutions after a breakage because no contact between the workpiece and the tool exists, then increases when contact with the broken tool is made again. However, the force drop in the radial direction due to the loss of the tool tip does not always appear as in the case of the tangential cutting force. In the finish boring process with Smart Tool, a jump of radial cutting force immediately after a tool fracture or chipping is observed. With a small depth of cut in the boring processes, most of the tool breakage cases occur due to excessive cutting force from (i) poor quality of the surface after rough boring or (ii) incorrect finish boring tool position. A sharp rise in the radial cutting force generally indicates finish tool breakage and the machine tool needs to be shut down immediately. 4.2. TOOL POSITION ERROR DETECTION USING CUTTING FORCE Two possible bored-hole geometry errors due to cutting process failures are assumed for Smart Tool diagnostics. Figure 4(a) depicts the case of the rough tool breakage failure during the boring operation resulting in a change in the depth of cut and the surface quality of the finish boring operation. Figure 4(b) shows the geometry of the hole after the finish boring when the centre of the rough boring position and the finish boring position do not match. As a result, the depth of cut of the finish boring process becomes a periodic
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Figure 4. Boring hole shape after failure, (a) cutting surface change after failure, (b) non-uniform depth of cut due to hole-centre position error.
function that repeats every revolution of the spindle. The position errors in the rough and finish boring tools are the primary causes of this phenomenon. Tool wear of either finish or rough boring cutters can also result in the depth of cut change. However, these failure modes are not considered in this paper because the impact is too small to be detected. 4.3. ISOLATION OF THE FAILURE MODES Since cutting force is a monotonic function of the depth of cut, if the cutting force is accurately measured the quality and geometric profile of the cutting surface can be calculated from the force data. For the diagnostics of rotating boring tool, force pattern in a single revolution of the tool can be utilised because the pattern is different for each failure mode. Since the spindle speed is transmitted to the controller of the Smart Tool from the stationary spindle controller, the controller computer inside the boring tool can recognise the force pattern for each revolution.
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The Smart Tool diagnostics detects the failures and isolates the failure mode using force pattern. The diagnostic algorithm continuously checks whether the cutting force is greater than a critical value, in which case the finish tool is broken and the machine must be immediately shut down. If the force is below the critical value but greater than that of normal cutting condition, the algorithm identifies if the failure mode is rough tool breakage or tool positioning error. Two types of force patterns described in Section 4.2 are utilised for the algorithm: (i) step change due to tool breakage and (ii) periodic signal due to incorrect tool position. A band-pass filter may be used to isolate the periodic component from the cutting force signal to detect the tool position failure. However, it is difficult to detect the force difference from step changes due to tool breakage using the simple band-pass filter only. Therefore, quantitative measurement of the signal is still required. To evaluate the force pattern quantitatively, two functions DðiÞ and GðiÞ are introduced. DðiÞ responds to the step changes only and thus can be used to detect the tool breakage failure. In contrast, GðiÞ responds to the periodic signals only to detect tool position errors. The DðiÞ and GðiÞ act similar to filters, and it gives the quantitative measures required for diagnostic algorithm directly as well. The DðiÞ shows the change in cutting force between the previous revolution and the current revolution of the tool such that DðiÞ ¼ f%i f%it ð8Þ P where i is an index of revolution of the tool, f%i ¼ N j¼1 fj =N, fj ¼ force measurement at each sample, N is the number of samples per revolution, and t is the number of revolutions of the tool between the cutting force averages being computed. The function DðiÞ is used to detect a relative cutting force change during the process. To avoid comparison of the cutting forces in the transient of a failure, the t in equation (8) is introduced. Thus, it compares the average cutting force of the ith revolution and the ði tÞth revolution where the failure is fully developed. Minimum value of t is selected based on spindle rotation speed. Since tool breakage is a permanent failure no maximum value of t needs to be determined. DðiÞ value greater than the predetermined level indicates the significant cutting force change that cannot occur in a normal cutting process, such as the case depicted in Fig. 4(a). Function GðiÞ is the measure of cutting force irregularity at one revolution with respect to tool-tip angle when each revolution of the tool is divided by three sections as in equation (9). GðiÞ ¼ max f#j f#k : ð9Þ j:k¼0;1;2
PN=3
i is an index of revolution of tool, f#j ¼ l¼1 flþðjN=3Þ =ðN=3Þ, fi the force measurement at each sample, and N is the number of samples per revolution. The GðiÞ value represents differences between three average cutting force readings obtained from three equally distributed angles at each rotation. This is in contrast to the DðiÞ that compares the cutting force between rotations. A greater GðiÞ indicates a greater irregularity of the cutting force during a single rotation of the cutting tool, which is due to the case shown in Fig. 4(b). When a tool positioning error exists, only the GðiÞ value becomes significantly greater than zero, but not the DðiÞ value because the average cutting force for each revolution remains the same. On the other hand, an undersized hole due to a breakage of the rough tool changes only the DðiÞ because the depth of cut is evenly increased around the angular positions of the hole. Therefore, the behaviours of the GðiÞ and DðiÞ functions are used to
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Set threshold of tool breakage force, Γ(i), ∆ (i)
Estimate cutting force fi
If fi > breakage force
Yes
Tool breakage
No Calculate
Γ(i) and ∆(i)
If Γ(i) > threshold
Yes
Rough tool breakage
Yes
Tool position error
No If ∆(i) > threshold No
Figure 5. Failure mode isolation algorithm.
detect and identify the root cause of various failures. The threshold level of the GðiÞ and DðiÞ for failure detection must be determined according to the cutting condition. The flowchart in Fig. 5 summarises the failure mode isolation algorithm used in the control computer. Figures 6–8 show the results of the diagnostic experiments of three different cases of cutting process failures. Each graph displays the cutting forces estimated by the Smart Tool estimation method, and the two diagnostic functions GðiÞ and DðiÞ. For all three cases, the cutting speed was 2 m/s and the feedrate was 0.083 mm per revolution. The number t for the DðiÞ function was set to 4. The threshold level of GðiÞ and DðiÞ was set to 25 N, which is 50% of the normal cutting force in this experiment. The threshold level of finish tool breakage detection was set to 100 N. 4.4. EXPERIMENTAL VERIFICATIONS Figure 6 demonstrates the case in which a failure in the previous cutting pass resulted in a 2.0 mm undersized hole in the middle of cutting. As can be seen in the graph, only the DðiÞ value, which is sensitive to a change of depth of cut during several successive revolutions, became greater at the moment of change in the depth of cut and the failure was identified as the rough tool breakage failure.
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Figure 6. Cutting force estimation and diagnostic function: rough tool breakage in previous cut (depth of cut changes from 0.25 to 2.25 mm; feedrate: 0.083 mm/rev; cutting speed: 2 m/s): }, smart tool force estimation; – – , G (i); –n–, D (i); – – –, threshold of G (i) and D (i).
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Spindle rotation Figure 7. Cutting force estimation and diagnostic function: finish tool breakage (depth of cut: 0.76 mm; feedrate: 0.083 mm/rev; cutting speed: 2 m/s): }, smart tool force estimation; – –, G (i); –n–, D (i); – –, threshold of G (i) and D (i).
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Spindle rotation Figure 8. Cutting force estimation and diagnostic function: finish boring tool position error (depth of cut: 0.25 mm; feedrate: 0.083 mm/rev; cutting speed: 2 m/s; tool offset: 1.27 mm): }, smart tool force estimation; – –, G (i); –n–, D (i); – –, threshold of G (i) and D (i).
The event of breakage of the finish cutting tool is captured in Fig. 7. To expedite tool breakage, an insert with a 2.0 mm notch on the tool tip was used. The boring hole was machined with a 0.76 mm depth of cut until a fatigue fracture of the tip occurred. Since the cutting force due to the broken tool was greater than the critical force, the tool breakage failure was immediately detected regardless of the behaviour of DðiÞ and GðiÞ. Figure 8 shows the case when the boring tool had a 1.27 mm position error. In this case, as can be seen in the figure, the cutting force became a periodic function. Therefore, only the GðiÞ value, which indicates the variation of cutting force during one revolution (caught by the variation of the depth of cut), responded. However, the DðiÞ remains small because the average cutting force at each revolution was constant. These experimental results propose that the combination of the GðiÞ and DðiÞ, which is calculated in the Smart Tool from the force estimation, is a good indicator to identify the root causes of the failure.
5. SUMMARY
Diagnostic capability is one of the most important features in automated machine tool systems and it is critical when more sophisticated electronics is used to control the process. This paper has elaborated on the failure detection and a root-cause isolation method, which utilises the process information and monitoring of the cutting force. A new measurement technique of the cutting force based on disturbance estimation was developed and integrated into a sensorised boring tool.
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The experimental results demonstrated that the indirect force measurement by the proposed method matches well with the conventional direct measurement using a tool dynamometer. The developed accurate cutting force estimation method is not only useful for failure monitoring, but it is also useful as a substitute for tool dynamometers, especially in cases where the tool dynamometer is difficult to be placed, such as in a rotating tool tip. The proposed cutting process diagnostic method was effective in detecting several modes of tool failure as well as other process failures which occurred in the boring process. The electronic package and the new algorithm embedded in the Smart Tool made it possible to build a self-contained tool-tip position controller with diagnostics inside a rotating cutting tool, thus to increase the reliability and the productivity of the boring process.
ACKNOWLEDGEMENTS
The research was sponsored in part by the ERC for Reconfigurable Machining System under NSF grant # EEC-9529125. The Smart Tool research presented in this paper was sponsored in part by NIST ATP grant # 70NANB5H1158. The authors appreciate the support of Dr Phil Szuba from Lamb Technicon.
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