Clamping Fault Detection in a Fixturing System

Journal of Manufacturing Processes Vol. 2/No. 3 2000

Technical Note

Clamping Fault Detection in a Fixturing System J.H. Yeh and F.W. Liou, Dept. of Mechanical and Aerospace Engineering, and Engineering Mechanics/Intelligent Systems Center, University of Missouri-Rolla, USA

Abstract

change. To detect the characteristics of the clamping condition, an industrial modular fixture system was used because it can be used to generate a variety of cases with reasonable repeatability. The finite element method (FEM) was used to model the clamping conditions between the workpiece and the fixture elements. The workpiece and fixture elements were modeled using the FEM modal analysis in the MSC/NASTRAN code.31 Virtual springs represented the contact condition (contact force) between the fixture elements and the workpiece. The stiffnesses of the virtual springs were estimated and implemented in the FEM model. The experimental results were compared with the FEM results. The vibration signals of the clamping conditions were measured and recorded using an accelerometer and associated data acquisition system.

When a workpiece is located and clamped in a fixture, the dynamic response can be used to characterize its corresponding clamping condition. When the contact condition between the workpiece and the fixture is changed, the system response frequency will change accordingly. The identification of insufficient clamping forces is investigated in this study. This paper also presents the modeling and analysis of the contact condition between fixtures and components in a modular fixturing system. Analytical models were obtained using the finite element approach. Virtual spring elements are used to model the dynamic effect of surface contact in the model, and the estimation of the virtual spring constants is also presented. Experimental results are compared with the analytical predictions. Keywords: Fault Detection, Fixturing System, Contact Modeling, FEM, Experiment

Introduction In manufacturing workcells, fixtures enhance efficiency by properly positioning and clamping the workpieces. Production quality can be dramatically improved if the clamping devices are properly designed and implemented. In recent years, fixture modeling and design have been studied and significant results have been achieved.1-8 Although such advances in fixture design have greatly improved fixture accuracy and repeatability, fixture faults (or errors) are still a major cause of quality variation. Most of the literature on fixture analysis has emphasized the positions of fixture elements on the workpiece rather than the contact condition between the two. Significant research has been conducted in the area of machine fault detection/diagnosis,9-29 but relatively little has been done on fixture fault detection and monitoring.30 The need for the detection of fixture faults has motivated the investigation undertaken here. When a workpiece is clamped in a fixture and the contact between the workpiece and the fixture elements changes, its frequency response will also

Experimental Setups An experiment was conducted to investigate the frequency response of the contact condition concept described above. An industrial modular fixturing system is used to precisely locate and clamp the workpiece in place. Due to the ease of their installation and high sensitivity, accelerometers were utilized to measure the vibration signal of the system. The basic features of the experimental setup are shown in Figure 1 and are summarized as follows: •

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Modular fixturing system. Modular fixturing systems, like the one shown in Figure 2, are widely used in industry to build different fixtures from a set of fixturing blocks, wedges, pins, etc., to precisely locate and clamp various parts for assembly and/or manufacture. The modular fixturing system used in this experimental study utilized a base plate (310223, Bluco), serrated edge clamps (310154, Bluco), and locators (310211, Bluco).

Journal of Manufacturing Processes Vol. 2/No. 3 2000

Accelerometer 2 Workpiece

Data Acquisition System

Locator 2 Clamp 1

Accelerometer 1

Computer

Data acquisition software

Clamp 3 Locator 1 Clamp 2

A/D board

Terminals

Base plate

Figure 2 Configuration of Modular Fixture System

•

Spectrum analyzer

Impact hammer. An impact hammer (PCB Piezotronics, Inc.) provided and controlled the excitation force.

Amplifier Impact hammer

Identification of Clamping Errors One purpose of this experiment was to investigate the signal variations between normal and abnormal clamping conditions of the fixture on the workpiece. The signal was taken first from the normal state with correct positioning and sufficient tightening forces. The frequency domain signals were collected based on every nonoverlapped interval (8000 samples per interval were used in most cases). The accelerometer used in the data acquisition system was able to measure the small variations due to different clamping conditions in the modular fixture system. An impact force was imposed on the workpiece using an impact hammer to excite the fixturing structure to obtain distinct vibration data. The excitation force can be measured and controlled in the range of 12 to 15 lb force amplitude. Cases with insufficient clamping forces that cause unstable contact surface are used as examples of abnormal clamping conditions. One common problem with modular fixtures is insufficient clamping forces. This problem can easily occur because, in an effort to provide adequate clamping, excessive clamping forces cause workpiece deformation. Therefore, to compensate, operators may tend to apply insufficient clamping forces on the system, which causes a loss of rigidity in the fixture setup. The cross-correlation function developed from statistical theory was used to help correlate different

Accelerometer

Fixture & workpiece device

Figure 1 Schematic of Experimental Setup

•

• •

Accelerometer. One accelerometer (2224C, 11.6 mV/g, Endevco Instruments Inc.) was attached to measure the fixture’s vibration response using the Fast Fourier Transform (FFT). A charge amplifier was used to magnify the output signal of the accelerometer before sending it to the data acquisition system. Spectrum analyzer. An HP-3566A spectrum analyzer monitored the output from the accelerometer in both the time and frequency domains. Data acquisition system. A PC data acquisition software package (DT Vee, Data Translation Inc.) integrated with a Data Translation A/D board was utilized to analyze the experimental data.

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signals. The unbiased estimate of the cross-correlation function is shown as follows;32 Rxy ( m) =

1 M− m

Table 1 Correlation Values for Clamping Repeatability The transfer function of each case is compared to that of each of the others. The variations are due to refixturing. It can be seen that, although there are variations, the correlation values are all greater than 0.7, and these cases are all considered as “normal” cases.

M − m −1

∑ x ( n ) y( n + m )

n=0

(1)

XY Correlation Value Case

where x(n) and y(n) are two real signal sequences, the index m is the (time) shift (or lag) parameter, and the subscript xy on the cross-correlation sequence Rxy(m) indicates the sequences being correlated. The order of the subscript, with x preceding y, indicates the direction in which one sequence is shifted relative to the other. x(n) and y(n) are assumed to be indexed from 0 to M–1. In other words, x(n) and y(n) are causal sequences of length M, that is, x(n) = y(n) = 0 for n<0 and n ⱖ M. For various time shifts, the maximum value was used. Through this processing technique, two signals can be quantitatively distinguished. After normalizing Eq. (1) by autocorrelation at zero lag, when two signals are identical their normalized cross-correlation will be equal to 1.0, and when two signal patterns are very different their cross-correlation will be close to zero. In a mass production environment, each workpiece with the same geometry is placed on the fixturing station for manufacturing. Theoretically, the dynamic response of these workpiece-fixture configurations should be the same if the clamping forces and the clamping locations are the same. However, due to operator errors as noted earlier, different excitation forces, and slight variations in workpiece geometry and surface finish, the repeatability of these workpiece-fixture configurations can be low. These variations should be relatively small when compared to that of an abnormal clamping condition. To determine the repeatability of the workpiece-fixture configuration, the correlation values for various cases using the same configuration shown in Figure 2 were tested, and a typical result is listed in Table 1. These cases were considered to be the normal setup of the fixturing system and were used to test its repeatability. The normal setup, or the fully tightened condition, occurs when a 100 in.-lb torque is imposed on each clamp of the fixture. In Table 1, cases A1 and A2 represent the reference clamping conditions, and cases B1, B2, C1, C2, D1, and D2 are the repeated workpiece-fixture conditions that were manually generated by entirely loosening the clamp from the setup similar to case A

A1

A2

B1

B2

A1 1.000 0.8012 0.7131 0.7675 A2 1.000 0.8305 0.9411 B1 1.000 0.8618 B2 1.000 C1 C2 D1 D2

C1

C2

D1

D2

0.8692 0.8649 0.8874 0.8614 1.000

0.8108 0.8843 0.7749 0.8495 0.8542 1.000

0.8218 0.8876 0.8688 0.8881 0.8578 0.8700 1.000

0.7771 0.7375 0.8073 0.7563 0.7898 0.7520 0.8394 1.000

and reclamping the workpiece to the original setup, that is, the case A setup. A torque meter was used to apply the same force level in each case. Because different exciting force may alter the dynamic response of the system, each workpiece-fixture configuration was tested twice with slightly different exciting forces. These are denoted by A1 and A2 for case A, B1 and B2 for case B, and so on, in Table 1. The results show that the correlation values for overall cases were above 0.7. Therefore, the correlation values for the repeatability of the clamping conditions of the fixturing system were quite high. In the next step of the experiment, clamp 1 and clamp 3 in Figure 2 were loosened independently so that their applied forces were abnormal. The acceleration signals for different abnormal clamping conditions were taken and correlated to the reference signals A1, B1, C1, and D1 in Table 1, and the corresponding correlation values are listed in Table 2. These results show that the fixture clamping force condition can be monitored using the dynamic response of the fixture-workpiece. The test cases demonstrate that if the correlation value is below about 0.5, a clamping error (loose clamp) may have occurred.

Modeling Concept The above results show that it is possible to identify clamping faults (errors) using the dynamic response of the fixture-workpiece. Research was also conducted to understand the behavior of the clamping conditions by modeling the system. In this

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Table 2 Correlation Values for Various Clamping Conditions The transfer function of each abnormal case is compared to those of cases in Table 1. It can be seen that the correlation values are all smaller than 0.5, and these cases are considered as “abnormal” cases.

XY Correlation Value Test Case Description 1

2

Correlated Case Case A1 Case B1 Case C1 Case D1

Tighten Clamp1 to 60 in.-lb Loosen Clamp1 completely

0.3889

0.3682 0.4372

0.4919

0.3938

0.2576 0.3626

0.2479

Tighten Clamp3 to 40 in.-lb Loosen Clamp3 completely Loosen Clamp3, Clamp2 completely

0.4467

0.3857 0.4568

0.4143

0.3752

0.3492 0.4313

0.3851

0.4238

0.2364 0.3281

0.2665

(a)

(b)

Figure 3 Three-Object Assembly Connected with Virtual Spring Elements

a system, the total mass of the assembled system would not vary significantly from one workpiece to the other, when similar workpieces are to be mass produced. Therefore, the contact stiffness between system components is the main factor changing the system’s dynamic response. The contact stiffness defines the force-deformation relationship and is highly dependent on the applied clamping forces and material deformation related parameters.

part of the research, the characteristics of the rigidity, or force-deformation relation, between the workpiece and fixture elements are represented as a set of virtual springs that connect the workpiece and the fixture elements. The tightness between the two objects is represented by the value of the assumed spring stiffness. For instance, an extreme case for three objects in contact is shown in Figure 3a. This case can be treated as a structure shown in Figure 3b where the spring rigidity is very large when the objects are compressed with excessive forces. The stiffness and mass of the assembled workpiece and fixture system varies with the geometry and contact condition. Thus, the dynamic response of this workpiece-fixture assembly structure will be different, and therefore the system’s response frequency can be used as an index to represent the clamping condition of the fixture system. Based on this concept, analytical and experimental investigations were conducted to analyze the fixturing conditions by detecting the corresponding response frequency signal. The dynamic response frequency of a multiobject system should be a function of the mass and the stiffness. This frequency response is proposed as a parameter to monitor the fixturing contact conditions. The stiffness of the workpiece itself or the individual fixture elements will not vary much from workpiece to workpiece because stiffness is a function of geometry and material property. Similarly, when the workpiece and the fixture elements are assembled to form

Finite Element Model For the FEM model, the workpiece and the fixture elements were divided into eight-node solid elements in the meshing process. The FEM spring elements were used to model the clamping conditions between the workpiece and the fixture elements. As discussed earlier, it is assumed that the rigidity, or force-deformation relation, between the workpiece and the fixture elements can be represented as a set of virtual springs that adjoin the workpiece and the fixture elements. These virtual springs represent the surface contact rigidity between two objects. The tighter the two objects are forced together, the closer they approach the rigid state as shown in Figure 3b. Also, the tighter the two objects are forced together, the more difficult it is to produce additional deformation between the two objects. This means that the contact rigidity, or spring constant, has increased. In this research, the spring elements connect the nodal points from the workpiece to the corresponding nodal points of the fixture elements. The modal analysis in the MSC/NASTRAN code31 was used for to perform the analysis.

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␦1,␦2 = contact deformations subjected to the different pressures p1 and p2

Stiffness Estimation From the Hertz theory,33 the relation between deformation ␦ resulting from the applied compression P assumes the form of a power function, namely: ␦ = c · Pn

Equation (5) represents the deformation taking place at the contact surfaces between the workpiece and the fixture locating elements under the action of steady loads. The critical coefficient C1 has been experimentally investigated by Shawki and Abdel-Aal34 as defined in Eq. (3). C1 is a function of material, surface finish and hardness, and the contact area.

(2)

in which the coefficient c depends on workpiece surface finish and hardness as well as the area of contact between the workpiece and fixture. Consideration of surface hardness and contact area is more practical when dealing with the contact condition of the fixture elements. The relationship between the force exerted on the fixture and the resulting deformation has been experimentally studied by Shawki and Abdel-Aal.34 The equation to express two-plane contact surface deformation, ␦, and applied pressure, p, is as follows: ␦ = (a1 – a2HB + b1Ac + b2Rh)pn

Case Demonstration To model the contact conditions produced by various clamping forces, analytical and experimental studies were conducted. The experimental setup for this case is shown in Figure 4 in which a workpiece was clamped between a clamp element and locator. The FEM configuration of this workpiece-fixture system is shown in Figure 5. As previously men-

(3) Accelerometer

where

Clamp

␦ = deformation per contact (microns) HB = Brinell hardness value of workpiece material (kgf/mm2) Ac = contact area (cm2) Rh = surface finish of workpiece (microns) p = pressure (kgf/cm2) a1,a2,b1,b2 = coefficients that depend on the material properties, such as Young’s modulus n = index that depends on the object material

Locator

Figure 4 Experimental Setup for Case Demonstration

Thus Eq. (3) can also be represented as follows: ␦ = C 1 pn

Base plate

Workpiece

(4)

Thus, the contact stiffness between the workpiece and the fixture can be obtained from the following: K=

∆P P1 − P2 = ∆δ C1 ( p1n − p2n )

(5)

1,123456

1,123456 Z Y X

where P1,P2 = total loadings subjected to the different pressures p1 and p2; and

1,123456

Figure 5 FEM Modeling of Fixture Setup in Figure 4

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shown in Figure 6 on the locator along the x-direction. The frequency-domain signals were taken from a specific grid point on the same face of the locator. The dominant frequency of the fully tightened case was 1420 Hz, as shown in Table 4. The dominant frequencies for these cases were 1345 Hz, 1155 Hz, and 1140 Hz, respectively, as shown in Table 4. Experiments were also carried out for comparison to the FEM results. A typical experimental and FEM result comparison of the transfer functions is shown in Figure 7. A comparison of the dominant frequencies for the corresponding cases is listed in Table 4. The dominant frequency value shifts from 1460 Hz to 1390 Hz for the fully tightened case and from

tioned, eight-node solid elements were used to model the system. In this case, 66 virtual springs represented the contact condition between the workpiece and the locating element, and nine virtual springs were used between the workpiece and the clamp element. Based on the concept of using virtual spring elements to connect two objects, the contact condition between the locator and the workpiece can then be modeled by connecting all of the contact nodes with virtual spring elements. Specifically, each two contacting nodes were connected by one virtual spring element. Thus, the number of virtual spring elements is closely related to the number of finite element nodes. The number of nodes used was selected by testing the numerical convergence of the solution. In the analytical model, the fixture base plate is constrained by four three-dimensional virtual springs attached to the ground. According to Eq. (5) and test results for steel workpieces,12 the pressure-deformation relation is as follows:

725 1,123456

1,123456 Z

␦ = (0.4 – 0.0016HB + 0.012Ac + 0.004Rh)p0.7 (6)

Y

The hardness, HB, contact area, Ac, and surface finish, Rh, were obtained from the actual tests and measurements. The results were as follows: HB = 156 kgf/mm2, Rh = 2 microns, Ac = 0.94 cm2 for each contact element area, the contact area between the workpiece and locator = 45 cm2, and the contact area between the workpiece and clamp = 7 cm2. Based on the input data, the virtual spring constants between the workpiece and the locator and between the workpiece and the clamp are 1.3 x 105 N/mm and 3.5 x 105 N/mm, respectively, as determined by Eq. (5). A tangential spring constant is estimated to be about one-third of its corresponding normal stiffness value. The material properties for this example are shown in Table 3. An impact force of 10 lb magnitude was excited at a grid point as

X

782 1,123456

Impact grid point Data acquisition point

Figure 6 Impact Point and Data Acquisition Point in FEM Modeling

Table 4 Comparison of Experimental and FEM Results

Experiment Dominant Frequency Fully tightened case 1460 Hz (800 lb)

Fully tightened case 1420 Hz (K1=1.3 x 105 N/mm) (K2=3.5 x 105 N/mm)

Loosening case I (500 lb)

1390 Hz

Loosening case I (K1=1.2 x 105 N/mm) (K2=2.9 x 105 N/mm) 1345 Hz

Loosening case II (200 lb)

1200 Hz

Loosening case II (K1=0.8 x 105 N/mm) (K2=2.3 x 105 N/mm) 1155 Hz

Table 3 Material Properties for Case Study

Young’s Modulus Poisson’s (N/mm2) Ratio Base plate Locator Clamp Workpiece

1.0E+5 0.7E+5 2.0E+5 2.0E+5

0.3 0.3 0.3 0.3

Density (kg/m3)

Material

7200 2700 7700 7700

Cast iron Aluminum alloy Cast steel Cast steel

FEM Dominant Frequency

Fully loosened case 1145 Hz (0 lb)

Fully loosened case (K1=K2=0 N/mm)

K1=virtual spring stiffness for locator side K2=virtual spring stiffness for clamp side

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1140 Hz

Frequency (Hz)

Journal of Manufacturing Processes Vol. 2/No. 3 2000

1500 1450 1400 1350 1300 1250 1200 1150 1100 0

1

2

3

4

5

6

7

8

9

3000 Force (100 lb)

Frequency (Hz)

Force vs. frequency in experiment (a) Experimental result for fully loosened case (0 lb) Frequency peak = 1145 Hz

Frequency (Hz)

Acceleration 4 3.5 3 2.5 2

1500 1450 1400 1350 1300 1250 1200 1150 1100 0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4

1.5 1

Stiffness (10^5 N/mm)

0.5

Stiffness vs. frequency in FEM

0 0

0.5

1

1.5

2

2.5

3.0E+03

Frequency (Hz)

Figure 8 General Trend of Results from FEM and Experiment

(b) FEM result for lower bound case (K = 0 N/mm) Frequency peak = 1140 Hz

the dominant frequencies for the cases with varying contact areas are listed in Table 5. The peak frequencies of the patterns shift consistently with the changes of the contact area and are consistent with those from experiment.

Figure 7 Transfer Functions from FEM and Experiment

1200 Hz to 1145 Hz for partially loosened cases for the fully loosened case. It was found that the dominant frequency shifts from the FEM analyses and the experimental results show similar trends, as shown in Figure 8, demonstrating that the variation of force and stiffness with frequency from the FEM analysis and experiment are in good agreement. Another case was studied using the same set of workpiece and fixture elements as those used in the previous case, except that the contact areas between the workpiece and the fixture elements were varied. In addition to the configuration shown in Figure 5, the other fixture setup configurations are shown in Figure 9. The dynamic response of these fixturing systems depends solely on the configuration and contact area on which the clamping force is exerted. The virtual spring stiffnesses were calculated using Eq. (3) and Eq. (5) and implemented in the respective models to simulate the estimated contact stiffness. With the same impact condition and taking data from the same grid point as shown in Figure 6,

Conclusions The determination of insufficient clamping forces in a modular fixturing system has been investigated. This clamping condition identification was also performed analytically with FEM and verified by experiment. The use of virtual spring elements for simulating surface contact conditions for fixturing systems is described. The implementation of virtual spring elements to model workpiece-fixture systems using FEM was also demonstrated in case studies. The FEM results are in good agreement with those from the experiment. In general, the model predictions and experimental results are within 10% error. These results show the feasibility of identifying clamping conditions from system dynamic response signals, as well as the feasibility of using virtual spring elements to model surface contact conditions. One can make use of the result of this study by

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Table 5 Comparison of Results for Variable Contact Area Cases

Dominant Frequency

1,123456 1,123456

Z Y X 1,123456 (a) Contact area change I — locator/clamp: 3 x 1.7" / 1 x 1"

FEM

Full contact case Locator / clamp contact areas: 3” ⫻ 2” / 1” ⫻ 1”

1460 Hz

1420 Hz

Contact area change I Locator / clamp contact areas: 3” ⫻ 1.7” / 1” ⫻ 1”

1290 Hz

1210 Hz

Contact area change II Locator / clamp contact areas: 3” ⫻ 0.8” / 1” ⫻ 0.5”

1200 Hz

1105 Hz

• Locate the workpiece and accelerometers to a fixture system precisely • Acquire the signal of standard workpiece-fixture system for spectrum analysis with data acquisition system • Store the data as the standard database

1,123456 1,123456

Experiment

Z Y

2. During actual production, acquire the spectrum for the ongoing workpiece-fixture system, and compare the spectrum pattern with standard base line using signal correlation technique 3. Detect the natural frequency shift based on the position variety, and examine the shift difference if it is within the allowance

X 1,123456 (b) Contact area change II — locator/clamp: 3 x 0.8" / 1 x 0.5"

Figure 9 Configuration of Fixture Setup for Changing Contact Area

experimentally measuring and comparing the dominant frequencies of a fixture-workpiece system under both normal fixturing conditions and undesirable fixturing conditions. The simulation approach for a specific fixture-workpiece system includes the following steps:

Acknowledgments This work is partially supported by National Science Foundation grants No. DMI-9871185 and No. BCS-9302201, the Missouri Department of Economic Development through the MRTC grant, and a grant from the University of Missouri Research Board. This financial support is greatly appreciated.

1. Estimate contact stiffness K based on Eq. (5) and experimental data. K should be a function of material, surface finish and hardness, and contact area; and 2. Incorporate virtual spring into FEM model to simulate the fixture-workpiece system.

References 1. Y-C. Chou, V. Chandru, and M.M. Barash, “A Mathematical Approach to Automatic Configuration of Machining Fixtures: Analysis and Synthesis,” Journal of Engg. for Industry (v111, 1989), pp229-306. 2. H. Asada and A. By, “Kinematic Analysis of Work Part Fixturing for Flexible Assembly with Automatically Reconfigurable Fixtures,” IEEE Journal of Robotics and Automation (vRA-1, n2, 1985), pp86-94. 3. J.K. Salisbury and B. Roth, “Kinematic and Force Analysis of Articulated Mechanical Hands,” ASME Journal of Mechanisms, Transmissions and Automation in Design (v105, 1983), pp34-41. 4. R.J. Menassa and W.R. DeVries, “Locating Point Synthesis in Fixture Design,” Annals of the CIRP (v38, 1989), pp165-169. 5. R.T. Meyer and F.W. Liou, “Fixture Analysis Under Dynamic Machining,” Int’l Journal of Production Research (v35, n5, 1997), pp1471-1489.

During production, the dominant frequencies can be monitored and used as a tool to help indicate a fixture problem, as shown in Tables 1 and 2. The practical implementation for the fixture fault detection using shift in natural frequency is summarized below, as follows: 1. Establish the baseline data of standard workpiece-fixture system:

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6. P.M. Rapacz and M.C. Lou, “Application of FEM Analysis to a Structural Vibration Test,” ASME Pressure Vessels and Piping Div. (v101, 1986), pp101-107. 7. R.J. Menassa and W.R. DeVries, “Optimization Methods Applied to Selecting Support Positions in Fixture Design,” Proc. of USA Japan Symp. on Flexible Automation, Crossing Bridges in Advanced Flexible Automation Robotics (1988), pp475-482. 8. P.C. Pong, R.R. Barton, and P.H. Cohen, “Optimum Fixture Design,” Proc. of Int’l Engg. Research Conf. (1993), pp6-10. 9. Ian Liddle and Steven Reilly, “Diagnosing Vibration Problems with an Expert System,” Mechanical Engg. (Apr. 1993). 10. D. Dyer and R.M. Steward, “Detection of Rolling Element Bearing Damage by Statistical Vibration Analysis,” Journal of Mechanical Design (v100, 1978), pp229-235. 11. L.F. Paul, Failure Diagnosis and Performance Monitoring (New York: Marcel Decker Inc., 1981). 12. E. Subrahmanian, M.D. Rychener, J.W. Wiss, and S.J. Lasky, “Diagnosis for a Complex Machine: Integration of Evidential Reasoning with Causal Reasoning and Quantitative Simulation,” ASME Computers in Engg. (1988), pp337-342. 13. M. Basseville, “Detecting Changes in Signals and Systems - A Survey,” Automatica (v24, n3, 1988), pp309-326. 14. C.D. DeBenito, “On-Board Real Time Failure Detection and Diagnosis of Automotive Systems,” Trans. of ASME, Journal of Dynamic Systems, Measurement and Control (v112, n4, 1990), pp769-773. 15. J. Gertler, “Failure Detection and Isolation in Complex Process Plants - A Survey,” IFAC Symp. on Microcomputer Applications in Process Control, Istanbul, Turkey, 1986. 16. D.M. Himmelblau, Fault Detection and Diagnostics in Chemical and Petrochemical Processes (New York: Elsevier Scientific Publishing Co., 1978). 17. I.E. Alguindigue, A.L. Buczak, and R.E., Uhrig, “Monitoring and Diagnosis of Rolling Element Bearings Using Artificial Neural Networks,” IEEE Trans. on Industrial Electronics (v40, n2, 1993). 18. L. Winn, “Reliability and Failure Detection of Rolling Element Bearings in Power Plant Machinery: A Utility Survey,” Proc. of 4th Incipient Failure Detection Conf., Oct. 1990. 19. R.A. Collacott, Machine Fault Diagnosis and Condition Monitoring (Chapman & Hall, 1977). 20. S. Li and M. A. Elbestawi, “Fuzzy Clustering for Automated Tool Condition Monitoring in Machinery,” Mechanical Systems and Signal Processing (v10, n5, 1996), pp533-550. 21. S.N. Melkote, S.M. Athavale, R.E. DeVor, S.G. Kapoor, and J.J. Burkey, “Prediction of the Reaction Force System for Machining Fixtures Based on Machining Process Simulation,” Trans. of NAMRI/SME (Vol. XXIII, 1995), pp325-330. 22. J.F. Hurtado and S.N. Melkote, “A Model for the Estimation of Reaction Forces in a 3-2-1 Machining Fixture,” Trans. of NAMRI/SME (Vol. XXVI, 1997), pp335-340. 23. J.F. Hurtado and S.N. Melkote, “An Experimental Study of the Effect of Fixture and Workpiece Varibles on Workpiece-Fixture Contact Forces in

Authors’ Biographies Dr. Jung-Hua Yeh received both his MS and PhD degrees from the University of Missouri-Rolla in 1993 and 1997, respectively. Since then, he has been working at the Inteplast Group Ltd. in Texas. His major research interests include failure detection and prevention, and condition monitoring and diagnosis of multibody systems. Dr. Frank Liou is a professor of mechanical engineering at the University of Missouri-Rolla (UMR). He serves as the chair of the executive committee of the Interdisciplinary Manufacturing Engineering Education Program (MEEP) at UMR. He is also an associate journal editor of Mechanism and Machine Theory. He received his PhD degree from the University of Minnesota in July 1987, his MS degree in 1984 from North Carolina State University, and his BS degree in 1980 from National ChengKung University. His teaching and research interests include CAD/CAM, manufacturing automation, rapid prototyping, and augmented reality applications in design and manufacturing.

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