Knowledge-based diagnosis of drill conditions

Journal of Intelligent Manufacturing (1993) 4, 233-241

Knowledge-based diagnosis of drill conditions

SHANE Y. HONG

Department of Mechanical and Material Engineering, Wright State University, Dayton, OH 45435, USA

Received May 1992 and accepted August 1992

One major bottleneck in the automation of the drilling process by robots in the aerospace industry is drill condition monitoring. This paper describes a system approach to solve this problem through the advancement of new machine design, sensor instrumentation, metal- cutting research, and intelligent software development. All drill failures can be detected and distinguished: chisel edge wear, flank wear, crater wear, margin wear, corner wear, breakage, asymmetry, lip height difference, and chipping at lips. However, in the real manufacturing environment, different workpiece materials, drill size, drill geometry, drill material, cutting speed, feed rate, etc. will change the criteria for judging the drill condition. The knowledge base used for diagnosing the drill failures requires a huge data bank and prior exhaustive testing. A self-learning scheme is therefore introduced to the machine in order to acquire the threshold history needed for automatic diagnosis by using the same new tool under the same drilling conditions.

Keywords: Tool wear, sensing, machine intelligence, drill failure

1. Introduction

The automation of the drilling process by robots or CNC machines calls for on-line monitoring of drill conditions. The different kinds of drill failure shown in Fig. 1 are chisel edge wear, flank wear, outer corner wear, margin wear, crater wear, breakage, chipping at lip, lip height difference, and drill asymmetry (Kanai et al., 1978). An on-line monitoring system is required to sense all of the defects which affect the performance of the drill. In the past, researchers have tried different approaches to monitor the drill conditions (e.g. Subramanian and Cook, 1977; Thangarai and Wright, 1988; Rangwala and Dornfield, 1990; and a more detailed review in Hong et al., 1992). Even though they were moderately successful, they missed one important point: there are many types of drill failure. Each failure generates distinct sensory outputs. This article covers all drill failures, and not only deals with the sensor system and the algorithms used for detecting drill failure, but also distinguishes among different kinds of drill defects. To achieve this task, a knowledge base must be built, and diagnostic algorithms based on this knowledge have to be developed. More

#956-5515 @) 1993 Chapman & Hall

importantly, a ‘self-learning’ system which can automatically determine the criteria for diagnosing drill failures must be developed to cover a variety of workpiece materials, drill sizes, drill geometries, drill materials, and cutting conditions. This paper reports on a successful system implemented in the factory environment which includes advanced machine design, sensor instrumentation, metal cutting research and intelligent software development .

In Section 2 of this paper, the machine design and the sensor system are described. A ‘knowledge’ base related to the nature of drill failures and sensor outputs is explained in Section 3. Diagnostic algorithms using set representations are introduced in Section 4. The ‘self- learning’ approach used in determining the thresholds of the algorithms is discussed in Section 5. The implementation of the knowledge-based drill diagnosis system is explained in Section 6.

2. Development of a robotic drilling unit

This research is directed toward aircraft manufacturing in which drilling is a major operation. For a small jet

234 Hong

OUTER CORNER WEAR FLANK WEAR ASYMMETRY DIFFERENCE IN LIP HEIGHT

MARGIN WEAR CRATER WEAR BREAKAGE CHISEL EDGE WEAR CHIPPING AT LIP

Fig. 1. Types of drill failure.

fighter, for example, more than 245 000 holes are drilled. Workpieces, such as aircraft wings, frames, etc., are so large that air-powered, portable drilling machines, which are operated manually or handled by industrial robots, are generally used. In order to establish a fully automatic drilling operation, a special computer-controlled drilling machine that serves as an end effector for the robot was therefore developed (Horng, 1982).

2.1. The machine - robotic drilling unit

Due to the weight limitation of the end-effector, the machine, called the robotic drilling unit (RDU, shown in Fig. 2), uses two air motors, each dehvering 1 hp at 9Opsi, 4Ocfm, to drive the spindle. The power is transmitted to the spindle through a pinion-gear mechanism. The speed can be adjusted by the gear ratio as well as the air pressure. The feed motion is provided by a precision ball screw. A stepping motor with a resolution of 200 steps per revolution is used to drive the ball screw which moves the drill head through a thrust bridge. The stepping of the motor is directly controlled by the pulses generated by a single-board computer. Therefore, it needs only a short response time to change the feed rate and to control the position with high accuracy.

To increase the efficiency, as well as to fully automate the operation, a special quick tool-changing mechanism was developed. The quick tool-changing system enables the machine to change drills automatically; this feature is very useful for the automatic self-learning procedure described in Section 5. A remote center compliance is also adopted in the RDU to assist with the insertion of the nose of the machine into template holes. It provides

both lateral and angular yielding capability to com- pensate for the robot’s positioning inaccuracy.

2.2. Sensor system

The built-in sensory system enables the monitoring and diagnostic functions to be implemented on-line (Horng, 1985). Five sensors, as shown in Fig. 3, are used for measuring thrust force, displacement, vibration, speed and air pressure.

Thrust force is one of the most important and useful dynamic parameters in the drilling process. The thrust sensor is a strain-gaged bridge between the feed mechanism and the carriage which houses the spindle drive system. The deflection of the bridge beam when the thrust force is applied is a measure of the thrust force. Four strain gages forming a Wheatstone bridge are used to measure the beam deflection. A specially designed electronic circuit branches the thrust output into thrust average and thrust variation components. This circuitry has the capability to avoid transient false spike signals. The two branched components, thrust and thrust variation (fluctuation), have different meanings in terms of drill defect types which are detailed in Section 3.

The air-powered drilling machine provides a unique feature used in measuring torque. Torque is measured indirectly through the torque-speed-air pressure rela- tionship of the RDU. The reduction of the spindle speed indicates the torque involved in the drilling process and is measured by a speed sensor (magnetic pick-up) pointed toward the gear teeth.

A monolithic pressure transducer is used to measure the air pressure at the inlet of the air motors. The

Knowledge-based diagnosis of drill conditions

AIR PRESSURE SENSOR

COMPLIANCE MECHANISM BALL BUSHING

ACCELEROMETER QUICK CI-IANGE AIR MOTOR

LVDT

DRILL HOLDER \

NOSE PIECE

QUICK CHANGE MECHANISM

--- m-v-

THRUST SE

BALL NUT BALL SCREW

\ A;R CYLINDER STEPPING MOTOR

Fig. 2. Schema of the robotic drilling unit.

MEMORY 64K RAM

8” DISK x 2 <

es-- _ l/O $2

I

gs -

s8

235

/ SPINDLE

To be held by robot

Fig. 3. Sensor and control network.

236 Hong

pressure transducer is formed from a piezoresistive integrated circuit which provides a temperature-com- pensated voltage output proportional to the applied pressure. For closed-loop control of the countersink depth, an LVDT displacement transducer is adopted to measure the position of the moving carriage, through which the feed and the feed rate can be obtained.

The lateral vibration of the drill greatly affects the hole quality and accuracy. It is measured by the accelerometer mounted near the front ball-bearing. Usually, the vibration signal from the sensor is very noisy in the time domain. This kind of signal is difficult to use for on-line monitoring purposes. The ideal vibration signal output should be a voltage proportional to the amplitude of the vibration displacement, which is directly related to the hole quality. The conversion of the acceleration signal to displacement amplitude is accomplished through a special circuitry which includes a rectifier and an enveloping circuit. The output voltage indicating the displacement amplitude can be used directly by the computer for on-line diagnosis.

As described above, the hardware approach in the sensor instrumentation reduces the transient sensor output to avoid the signal error, and reduces the need for excessive data analysis or the use of a statistical process. On-line sensing and control by a single-board computer is greatly simplified by using this approach.

3. Knowledge base about drill condition

Since the drill is engaged in the workpiece during the drilling process, the condition of the drill cannot be inspected visually or by an intelligent optical vision system. The diagnosis of the drill condition by using the dynamic parameters involved in the material-cutting process, such as thrust, torque and vibration, is con- sidered the most reasonable approach. These dynamic quantities can be readily obtained from the integrated sensor system in the robotic drilling unit.

Extensive research and experimentation have been done to correlate the sensor outputs with the drill condition using the robotic drilling unit (Hong et al., 1992). Figures 4, 5 and 6 exemplify the thrust, torque and vibration of various defective drills which are detailed in Hong et al. (1992) and Horng (1982). Dynamic properties associated with each kind of drill failure as a result of the study are summarized as follows:

(1) Chisel edge wear - compared to a new, flawless drill, chisel edge wear increases average thrust and lateral vibration. The ratio of thrust to torque of a drill with chisel edge wear is larger than that of a new drill. Unless mixed with other drill defects, chisel edge wear alone does not cause thrust fluctuation. Chisel edge wear will not increase torque significantly;

240 I

i

0 io li 20 25 30 40

TIME (Seconds)

Fig. 4. Some thrust force curves for drills with different defects.

DRlLL BREAKAGE--- --. *=‘;----: ; . -

1480.- -. 10

I!i:l;li,;,i,ii:I:i;;i;::;iI:;j

0 5 10 15 20 25

TIME (Seconds)

Fig. 5. Some torque (speed reduction) for drills with different defects.

1.0

E i DRILL BREAKAGE

.

..I 0 .!iil!!:[:l:lli:I:I!:::I!I::I:

0 5 10 15 20 25 30 35 40

TIME (Seconds)

Fig. 6. Some vibration amplitude history during drilling with defected drills.

Knowledge-based diagnosis of drill conditions 237

(2) Flank wear - flank wear tends to greatly increase thrust and moderately increase torque. Flank wear has a larger thrust-to-torque ratio than a new drill. Unless there is uneven wear at the two flank edges, flank wear generally does not cause thrust variation and lateral vibration;

(3) Crater wear - crater wear increases torque but not thrust. The thrust-torque ratio is smaller than that of a new drill. Vibration in a thrust or lateral direction is negligible;

(4) Corner wear - outer corner wear increases both torque and thrust. Since it occurs at the outer radius, torque increases more than thrust. Therefore, the thrust- torque ratio is smaller than that of a new drill. Because slight asymmetry at the two corners usually occurs, outer corner wear will cause some thrust fluctuation. Lateral vibration also appears when the drill is retracting;

(5) Margin wear - margin wear has little effect on torque and thrust. The margin edge of a drill touches the drilled hole surface; therefore, its wear has a large impact on the hole quality. However, the sensing of margin wear is rather difficult. The only clear sign of margin wear is revealed by lateral vibration when the drill is retracting;

(6) Chipping at lips - chipping at the cutting edge will increase torque and thrust to some degree, depending on the chipping shape, size and location. The thrust fluctuation results from an unbalanced cut. Due to the geometry change at the cutting edge, torque increases more than thrust; therefore, the thrust/torque ratio decreases;

(7) Drill asymmetry - drill asymmetry may be caused by unbalanced wear, misaligned grinding, or a bent drill axis. Thrust and torque do not change significantly, but excessive vibration in the axial direction (thrust fluctuation), and in the cross-direction (lateral vibration), occurs during all stages of hole drilling;

(8) Breakage - drill breakage changes the geometry of the cutting edge and significantly increases the cutting torque, thrust average, thrust fluctuations and lateral vibration. Since the torque increases more than the thrust, the thrust-torque ratio for a broken drill is less than that of a new drill;

(9) Lip height difference - a drill with height difference between lips may be a poorly ground new drill. Thrust fluctuation is the strongest indicator of height difference between lips. This drill defect may not affect the thrust average, torque or lateral vibration if it is not associated with other drill defects.

4. Set theory representation of diagnosis algorithms

In order to facilitate the implementation of diagnostic functions, the discussion from Section 3 should be mathematically represented. The set theory representa-

tion is therefore used to establish the criteria in diagnosing drill failures. There are six quantities from the output of three sensors (thrust, torque and lateral vibration): thrust average (j), thrust fluctuation (Sfl, torque (m), ratio of thrust average to torque (Y), lateral vibration at beginning (V,) and lateral vibration at drill retraction (VJ. The set theory representation relates these six quantities with the drill conditions (H) and drilling conditions (x), where:

= (various drill defects, such as chisel wear, flank wear, crater wear, margin wear, asymmetry)

-I- = (Xl, x2, . . .> x,) = (drill bit material, drill geometry, drill size,

workpiece material, drilling feed rate, drilling speed, . . .).

Let us define F as a set which includes all drill defects causing the thrust force average (f> to be higher than the threshold ( TmaX). In the same manner we can define M as a set which includes all drill defects causing torque (m) to be higher than torque threshold (rpm,,,iJ, and AF can be defined as a set of drill defects causing thrust fluctuation (Sj) to be larger than the threshold value (T,). Ri is defined as a set of drill defects causing larger thrust- torque ratio (F/M) than that of a new drill, and R, is a set including all defects which cause a greater increase in torque than thrust. V, is defined as a set including all defects which create a larger vibrational amplitude than the vibration threshold (V,,,) at the beginning of drilling, and V, is a set including drill defects causing excessive vibration when the drill is retracting.

The characteristics of the various driil defects in Section 3 can be expressed in the following mathematical forms:

dl: chisel edge dlEFnR,nVb wear

d,: flank wear d,EFnR,

d3: crater wear d3 E M n R,

d4: corner wear d4 E M f’ F fl R,

d5: margin wear d, E V,

d6: chipping at d,EAFfl R,

(1)

(2)

(3)

(4)

(5)

(6) cutting edge

d,: drill d,fV,,nAFnV, (7) asymmetry

d8: drill breakage d, E R, fl V, fl V,, cl AF n F n M (8)

d,: height d,EAF (9 difference between lips

238 Hong

One can also rearrange the above equations into the following forms:

F = (4, 4, 4, 4, 4s) (10) AF = (d6, 4, 4, 4) (11) M = (4, 4, 4, 4, d6, 4) (12)

V, = (4, 4, 44 (13)

Vr = (4, 4, 4) (14)

RI = (4, 4) (15)

Rs= (4, 4, hi, 4) (16)

Each equation above has its own meaning and can be classified as one symptom of drill failure. For example, we can read Equation 11 as ‘the variation of thrust force is possibly caused by drill chipping, asymmetry, breakage or lip height difference’, and Equation 16 as ‘if the thrust-torque ratio is smaller than that of a new, good drill, the possible defects are crater wear, corner wear, chipping and breakage’.

If a drill has only one kind of defect, we can rearrange Equations 10 through 16 and isolate a specific drill failure. The algorithms for determining the specific drill defects are listed as follows:

Chisel edge wear dl = VI, - V,

Flank wear d2 = FnR1-Vb

Crater wear d3 = R, - F

Corner wear d4= FflR,-AF

Margin wear d5 = V, - Vb

Chipping at edge d6 = AF fl R, - V,

Drill asymmetry d7 = AF fl V, - R,

Drill breakage dg= AFnMflR,

Lip height dg = AF-V,-R, difference

(17)

(18)

(19)

(20)

(21)

(22)

(23)

(24)

(25)

As will be discussed in Section 6, the implementation of the drill condition monitoring system uses the same small computer that controls the machine. The diagnosis program is nested in the program to generate computer pulses driving the stepping motor, and the drill condition is monitored continually during drilling. One drill defect probably occurs earlier than the other in terms of exceeding the set threshold. Even though Equations 17-25 apply to cases where only one kind of defect is allowed, they are still useful most of the time since two defects rarely occur (exceeding the threshold value) simultaneously within the sampling cycle time, 0.0075 s.

5. Self-learning system used to find the thresholds for diagnosis

To use the set theory algorithms presented in Section 4, one will make a decision on the drill conditions based on the measured quantities of thrust force, torque and vibration. These quantities will change with different drill sizes, drill geometries, workpiece materials, as well as different feed rates and speeds. Even for the same workpiece material, the microstructures and hardnesses within the workpiece may vary greatly from the surface to the center of the workpiece due to the casting process, heat treatment and surface oxidation. Selecting the thresholds for the six parameters, T,,,, rpmmin, T,, F/M, V t, max, V, min is rather difficult.

We can use the advantage that the RDU not only has the sensing systems, but also a computer available for ‘learning’ to set up the thresholds of the six criteria. In the learning mode, the machine uses a new, good drill bit to drill a hole in the same material with the same feed rate and air line pressure as that of the drilling operation. All the sensor outputs are recorded in the computer’s memory. These data are multiplied by allowance factors and then stored in the memory of the microprocessor as a reference for T,,,, rpmmi,,, T,, F/M, V, max, V, min. These multiplying factors, which are expressed as percen- tages, may be adjusted according to the specific require- ments of workpiece quality or tool life preference. In most cases, adding 20% is recommended. The data stored in the memory are then used as thresholds for each parameter.

Implementation of the ‘learning’ procedure is shown in the flow chart in Fig. 7. This procedure is carried out by the RDU and robots before using any drill with a new size, material or geometry, or before changing the speed and feed in drilling. Due to the virtue of the advanced design of the quick tool-changing system, the robotic drilling system can do it automatically without an oper- ator’s interruption. We call this a ‘self-learning’ process by the machine itself. An alternative method for setting those factors is through another test using some marginal drills.

To determine the drill bit’s malfunction, the thrust average output, f(d, x), is compared with the threshold value defined by pf(&, x), in which p is a threshold multiplier and da represents the drill condition parameters for a new drill with no defects. F(d, x) and I.Lf(d,, X) are obtained under the same drilling conditions (x) but with different drill conditions (4.

The set defined in Section 4 (F, M, AF, RI, R,, Vb, V,) can then be expressed as follows:

F = [di lf(& 4 > /@o> 111 (26)

M = [di I4-4, 4 > PWO, 41 (27)

AF = [di 1 W(4, 4 > ~W(do, 41 (28)


STORE IN P,,,

1 TAKE DATA FROM SPEED

I MULTIPLY BY K2

I

STORE IN RPM,,N

1 TAKE DATA FROM THRUST AVERAGE

I MULTIPLY BY K3

I

6 A

Fig. 7. Flow chart of self-learning procedure.

R, = di 1 r(di, x) = f@i7 x, > fcdo, x) e&,x) wo, 4 I R, = di 1 r(di, x) = ;$ t) < ;$; 2)

I> ’ 1 vb = [dil.Vb& X) > pVddo> x)1

Vr = [di 1 V&4,4 > ~Vr(do, 41

(29)

(30)

(31)

(32)

6. The implementation of on-line diagnosis of major machine failures and drill failures

The on-line diagnosis of machine and drill failures can be implemented without additional cost. In the robotic drilling unit, the computer generates pulses to drive the motor and control the feed rate. In order to get 2-4 in min-’ (50.&101.6 mm min-‘) regular feed rate, the computer should deliver 1 pulse per 0.0075-0.0150 s, or 66-133 steps s-l. However, the computer speed is much faster than this requirement. The stepping interval

of the stepping motor is roughly 7500-1.5 000 computer cycles which is enough to execute more than 2000 instructions for the diagnosis of major machine and drill failures. A software program was written to check all sensor outputs and diagnose the problem. Figure 8 shows the flow chart for the on-line diagnosis of the machine or tool failures. Two stages of diagnosis are implemented. The first diagnostic level uses Equations 10-14 which classify the drill failure type into symptoms 3, 4, 5, 7 and 8. It does not matter if the drill has one or multiple kinds of defects. Once there is any trouble, the machine stops, and the computer terminal displays the symptom num- ber. It also specifies the nature of the failure and suggests the way to fix it. Since industries are more concerned about whether the drill is in good condition than the specific defects, the first level of diagnosis is sufficient for use on the shop floor.

For academic interest, in the second level of diagnosis, an advanced drill condition diagnosis which utilizes the algorithm sets, Equations 17-25 in Section 4, was further developed to specify the detailed type of drill failure. This calls for the use of a designated computer register (or memory space) in the diagnosis software. The register stores and sums up the index of the symptoms. If the content of the register is not 00000000, then the drill must be defective. Table 1 is used to specify the types of drill failure.

In the implemented diagnostic program, the criteria T max, rpmmin, TV, F/M, vb max, and V, min, are obtained from the ‘self-learning’ process described in Section 5.

7. Conclusions

Drill condition monitoring, which is the bottleneck in the automation of the drilling process by robots in the aerospace industry, has been achieved through the development of an intelligent drilling machine to work

Table 1. Diagnosis code for detailed drill failure

Register [R] content Drill defect

001010x1 001000x1 000XXX10 000XX0x1 00x10xXX 0000x1XX 0001x1XX 000XX11x 0010x1XX

Chisel edge wear Flank wear Crater wear Corner wear Margin wear Chipping at lip Drill asymmetry Drill breakage Height difference

between two lips

Note: X means either 0 or 1.

240

-7-l SYMPTOM 1 INSUFFICIENT AIR SUPPLY

n 1

MISS ONE STEP

SYMPTOM 2 AIR MOTOR

TROUBLE I

SYMPTOM 3 DRILL WEAR OR

j BREAKAGE

, b 1 SYMPTOM4 1 - r SYMPTOM 5 1

DRILL MARGIN WEAR

SYMPTOM 6 CARRIAGE JAM ”

1 SYMPTOM 8

DRILL WEAR DIFFERENT (CHISELOR FLANK] 1 y I DEPTH OF CUT 1

Lw& LVDT = 0 7

DRILL FAILURE

GO 1 TO

Fig. 8. Fiow chart of diagnosis procedure.

BALL SCREW/BRIDGE LOOSENESS


with industrial robots. This computer-controlled drilling machine has five built-in sensors. Based on recognizing different patterns of sensor outputs and applying sets of algorithms, an intelligent diagnostic system has been developed for on-line detecting and pin-pointing of any one of the nine drill failures: chisel edge wear, margin wear, asymmetry breakage, flank wear, crater wear, lip height difference, corner wear and chipping at lips. The manufacturing process has many inherent factors which affect the criteria in judging drill wear/breakage, such as drill size, drill material, drill geometry, workpiece material, cutting speed, feed rate, material microstructure, hardness distribution, etc. The complexity of the drilling process is simplified by implementing the self-learning system. This system enables the machine to acquire the knowledge needed to make judgements of the drill conditions regardless of complex situations.

8. Acknowledgements

The author thanks Dr S. M. Wu of the University of Michigan, Ann Arbor, for all his help in completing this development work. This work was based on the con- tracted machine development supported by General Dynamics under the US Air Force Manufacturing Modernization program.

References

Hong, S., Ni, J. and Wu, S. (1992) Diagnosis of drill failure made by multi-sensors on a robotic end effector, in ASME Proceedings of 1992 Japan-USA Symposium on Flexible Automation, San Francisco, CA, July ls1.5.

Horng, Shi-Yuan (1982) Development of an end effector for robotic drilling with on-line sensing and diagnosis, Ph.D. Thesis, University of Wisconsin-Madison.

Horng, Shi-Yuan (1985) Advanced sensor system for robotic drilling, in Proceedings of Sensors ‘8.5 Conference, Detroit, MI, November 5-7.

Kanai, M., Inata, K., Fujii, S. and Kanda, Y. (1988) Statistical characteristics of drill wear and drill life for the standar- dized performance tests, in CIRP Annual.

Li, P. G. and Wu, S. M. (1988) Monitoring drill wear states by a fuzzy pattern recognition technique. Journal of Engineer- ing for Industry, 110, 297-300.

Rangwala, S. and Dornfield, D. (1990) Sensor integration using neural networks for intelligent tool condition monitoring, Journal of Engineering for Industry, 112, 219-228.

Subramanian, K. and Cook, N. H. (1977) Sensing of drill wear and prediction of drill life. J. of Engineering for Industry, 99, 295-301.

Thangarai, A. and Wright, P. K. (1988) Computer-assisted prediction of drill failure using in-process measurements of thrust force. ASME Journal of Engineering for Industry, 110, 192-209.

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Knowledge-based diagnosis of drill conditions