On-line identiﬁcation of thermally-induced convex ...maps.pme.nthu.edu.tw/web_maps/PDF/PUBLICATION-TEXT/055.pdf · There are numerous works on monitoring used force measurement

On-line identification of thermally-induced convex deformationof the workpiece in surface grinding

H.H. Tsaia,*, H. Hochengb

aU-CONN Technology, Inc., 9 First Innovation Rd., Science Based Industrial Park, Hsinchu, Taiwan, ROCbDepartment of Power Mechanical Engineering, National Tsing Hua University, Hsinchu, Taiwan, ROC

Received 10 February 2000

Abstract

In this paper, the time-series modeling techniques of grinding forces is used to identify over-grinding induced by thermal convex

deformation within one pass. It is shown that the earlier the over-grinding is detected the better are the opportunities to avoid the detrimental

features of this phenomenon, therefore, prediction is an important feature of monitoring strategy. # 2002 Published by Elsevier Science

B.V.

Keywords: Convex deformation; Workpiece; Grinding

1. Introduction

In mould manufacturing, steels of low coefficient of heat

conduction are commonly used. The poor heat-conductivity

of the workpiece induces a high gradient of temperature

within the workpiece in surface grinding, and the high

temperature-gradient gives a large thermal bending moment.

On account of the thermal moment, a transient convex

profile of workpiece is ground during the grinding process,

the workpiece profile, therefore, being concave after having

cooled. When the workpiece is convex-deformed, the grind-

ing depth varies within one pass, the increased grinding

depth during the pass being called over-grinding.

Since over-grinding induces poor accuracy of the work-

piece, a monitoring function is, therefore, applied to identify

and prevent grinding damage. The monitoring function can

be configured in various degrees of complexity. The simplest

and most common configuration is that one sensor produces

one feature. In other cases, several different features might

be evaluated from a single sensor (multi-model monitoring).

Several sensors can be integrated into a system to generate a

number of symptoms of over-grinding, which is referred to

as sensor fusion [1]. Monitoring is thus quite clearly defined

as a function that generates symptoms of the state of the

machining process.

Feature extraction is the most important step in monitor-

ing due to need for accurate indication of catastrophic failure

in the process, for example, tool breakage and grinding burn.

Adequate proper sensor and signal processing are the basic

requirements for successful feature selection in monitoring.

A good monitoring function offers a successful control

function in the machining process based on the selected

feature and signal processing scheme.

Surface grinding with stroke passes differs from the

continuous machining process, the feature extraction in

monitoring being intended to be carried out based on a

three-stage strategy.

Stage 1: within-pass monitoring. The symptoms can

clearly indicate the state of the grinding process, namely,

the feature gives a strong signal once the failure has

occurred. Further, the time for signal processing should

be decreased as much as possible.

Stage 2: between-passes monitoring. Limited to the pro-

cessing of signal and feature selection, the symptoms of

grinding between passes should be a suitable monitoring

strategy because of the sufficient time for signal processing.

Stage 3: between-parts monitoring. Similar to human

monitoring, between-parts monitoring gives poor results

by the monitoring function. The strategy of between-parts

monitoring is the poorest situation in machining process,

since it produces a non-rescuable part once the damage is

monitored, where the machining cost has already been added

onto the part. This strategy is not been considered in the

current investigation, where stage 3 is used as merely a

Journal of Materials Processing Technology 121 (2002) 189–201

* Corresponding author. Present address: 84 Gungjuan Road, 243 Taishan,

Taipei County, Taiwan. Fax: þ886-2-29063269.

E-mail address: [email protected]

0924-0136/02/$ – see front matter # 2002 Published by Elsevier Science B.V.

PII: S 0 9 2 4 - 0 1 3 6 ( 0 1 ) 0 1 0 5 8 - 5

preparatory level due to the delay in rescuing the workpiece.

The best way to monitor the damage during grinding is within

one pass, since the problem can be identified as early as

possible. A proper monitoring index (feature) and processing

scheme should be set for within-pass monitoring, otherwise

the processing time cannot catch the grinding time within one

pass. The transverse time between passes equals the actual

grinding time. Therefore, between-passes monitoring would

be a reliable approach due to the time for signal processing

being double that of within-pass monitoring.

2. Sensing techniques

Since over-grinding often occurs in grinding for mould

steels, the over-grinding gives a defective workpiece

because of the poor geometrical and dimensional accuracies.

Hence a reliable sensing technique is needed to advise the

controller or the technician for preventing over-grinding.

Sensing techniques for the grinding process have been one

of the focuses of research on process automation. The

effectiveness of sensing techniques depends on the depen-

dency of the measured signal on the machining conditions.

A strong correlation is a prerequisite to obtaining useful

information from the measured signal. Furthermore, a good

sensor should be easy to use without disturbing the machin-

ing process, and it should also have a high resolution and a

fast dynamic response [2].

Force measurement by a dynamometer is the most widely

used technique and is popular for process monitoring and

control in industry and research due to its sensitivity to

cutting conditions and reliability of measurement [3]. It is

well accepted that the force signal has a strong dependency

on the machining conditions. Most of the adaptive control

and feedback systems for grinding, milling and turning are

thus based on force measurement, although the force-sen-

sing technique suffers from the limitations and inconveni-

ence due to the competing effects on the machine tool

rigidity and the dynamic response [2]. For instance, when

the workpiece is mounted on the top of the dynamometer in

grinding, the rigidity of machine tool structure is reduced as

a result of the limited supporting surface provided by the

dynamometer, the consequent chatter and vibration being

detrimental to the machining quality [4].

There are numerous works on monitoring used force

measurement [5–8] to detect the occurrence of chatter

and estimate tool wear in cutting process. Li and Fu [9],

Matsui et al. [10], Younis et al. [11], Brach et al. [12] and Fu

et al. [13] constructed theoretical grinding force models,

finding that the grinding force is proportional to the grinding

depth, width and workpiece feedrate. The grinding force is

thus used to identify the occurrence of over-grinding arising

from the convex deformation of the workpiece in grinding

process.

Acoustic emission (AE) has been investigated as a sensing

alternative for tool condition monitoring since the late 1970s

[14–16]. The application of AE technique is based on the

strong dependency on the tool condition [17]. Dornfeld and

Kannatey-Asibu [18,19] have shown that the common AE

sources are: plastic deformation in the shear zone and the

tool/chip interface; rubbing of the tool on the tool/chip

interface and the machined workpiece surface; chip break-

age and entanglement; and chipping and breakage of the

cutting tool. The AE sensing technique has been used to

monitor the machining process due to its non-intrusiveness,

ease in operation and fast dynamic response. The AE sensor

is small and easy to be positioned, and the size and weight of

the workpiece or tool do not have a strong effect on the AE

sensor. The dependency of the AE signal on the cutting

conditions, therefore, becomes the key factor governing the

applicability of AE sensing to machining process monitoring.

The potential of AE in metal cutting process monitoring

has been demonstrated by various investigations, for exam-

ple, tool wear monitoring in turning and milling, tool fracture

detection, and grinding burn, where the sensing technique is

reliable and provides a rapid response. The averaged root

mean squared (RMS) energy of the signal shows good

sensitivity to cutting speed, workpiece hardness, depth of

cut and feedrate, hence the mean AE RMS are evaluated and

correlated to the length of flank wear in milling and turning

[20–22]. Asibu and Dornfeld [23] proposed a model for AE

generation in orthogonal metal cutting. The primary assump-

tion in the model is that the energy content of the AE signal

is proportional to the energy consumed in the plastic defor-

mation and friction in cutting zone.

In grinding research, AE has been used for detecting

malfunctions and dressing, wheel sharpness, chatter and

grinding burn. Kakino et al. [24,25] compared the AE

and accelerometer, and concluded that the AE signal is

17.5 ms faster. Inasaki and Omura [26] also found that

the AE is about 1 s earlier than the power of the spindle

motor. Tsai and Hocheng [27] monitored the creep-in depth

beyond the initial contact between the workpiece and the

wheel by means of AE and a dynamometer. They found that

AE gives an effective indication on the initial contact and

well predicts the creep-in depth. AE and a dynamometer are,

therefore, used to monitor the occurrence of wheel-bite in

this study.

3. Experimental set-up and data processing

Fig. 1 shows the experimental set-up. A series of experi-

ments was performed on a CNC grinder equipped with a

dynamometer (Kistler 9275A). The CNC controller is a

Fanuc 0M model. The force signal from the amplifier is

fed into a data acquisition system. The data acquisition is a

PCL-812PG card that has 30 kHz of the maximum A/D

sampling rate and 16 bits of digital output and 16 channels of

analog input. Once the system has monitored the occurrence

of wheel-bite in the grinding process, the corresponding

control signal from the PC is put through the electronic

190 H.H. Tsai, H. Hocheng / Journal of Materials Processing Technology 121 (2002) 189–201

control interface to the CNC controller to change the

grinding depth or stop the grinding operation. On-line

monitoring and control with feedback function is thus

realized in the grinding process.

The surface grinding experiments were conducted using

an aluminum oxide wheel (WA80K8V) on a CNC grinder.

The total grinding power at the wheel spindle is measured by

a dynamometer, and the tangential velocity of the wheel is

set by a converter. The force transducer has an interface with

a personal computer (PC) for data storage and analysis. The

materials are hardening mold steels of type SKD11, SKD61

and SKS3. Each workpiece has two 12 mm diameter holes

for being bolted onto the dynamometer.

The signal processing procedure is as follows: the

detected tangential grinding force signals are amplified

and sent to a common data acquisition system (PCL-

812PG). The force signals are sampled at 2 kHz and stored

in the PC. Once the sampled values are found to have

gradually increased based on a pre-determined criterion,

the grinding depth is changed or the grinder is stopped by a

control signal from the PC. This approach is reliable to

identify the occurrence of wheel-bite and reduces the discard

rate in grinding.

The data processing scheme used in the current study for

the required rapid response to the controller is shown in

Fig. 2. In between-passes monitoring, the sampled tangential

grinding force is analyzed by the time domain average

method (TDA), which is the most effective scheme to derive

the average grinding force of each pass. Then the derived

average grinding force of this pass is compared with that of

the previous pass. The wheel-bite effect is identified once the

average grinding force is found to have increased, with

regard to the fundamentals of grinding, i.e. the up-grinding

force is always larger than the down-grinding one. All of the

signal processing and the decision-making procedure must

be finished before the start of the next pass. Finally, the PC

control signal instead of a manual pulse generator is fed into

the CNC controller to change the grinding depth or to stop

the grinding process.

In within-pass monitoring, the average tangential grinding

force is sampled every 5 mm in grinding length. In other

words, the number of the data in 5 mm is inversely propor-

tional to the workpiece feedrate: higher feedrate gives a

smaller amount of sampled data. The average grinding force

increases with the grinding distance once that wheel-bite has

occurred. The detection by the time-series method for

within-pass monitoring is to identify the coefficient of the

auto-regressive (AR) model. The value of a1 will increase

once wheel-bite has occurred. The control command is given

from the PC to the CNC controller for changing the grinding

conditions.

Since the series of sampled data (forces and AE) is a

stochastic process, it is impossible to distinguish between

the bite and non-bite states directly. The purpose of signal

processing is to transform the measured signal into char-

acteristics related to the bite or non-bite states by methods

such as regression analysis, time-series analysis and spec-

trum analysis. The processing efficiency of regression for

Fig. 1. Experimental set-up.

Fig. 2. The signal processing procedure for within-pass monitoring.

H.H. Tsai, H. Hocheng / Journal of Materials Processing Technology 121 (2002) 189–201 191

on-line monitoring is higher, and thus it is used in the current

study.

Regression analysis is an approach to transforming the

original signal into a generic mathematical model. The

results of the experiments show that the grinding forces

and AE may be described by an regressive model, because it

can be derived effectively from the solution. Even the simple

linear regressive model can be written when the output is

known

yt ¼ a1xt þ a0 þ et (1)

where t ¼ 1 to N and N is the number of data points, and et

the error item. The vector notation for the parameters of

Eq. (1) is defined as

Y ¼

y1

y2

y3

..

.

yN

266666664

377777775; X ¼

1 x1

1 x2

1 x3

..

. ...

1 xN

266666664

377777775;

b ¼a0

a1

� �; e ¼

e1

e2

e3

..

.

eN

8>>>>>>><>>>>>>>:

9>>>>>>>=>>>>>>>;

(2)

using the least-squares method, b is derived as

b ¼ ðX0XÞ�1X0Y (3)

The parameter a1 of b can be used to identify the trend of the

signal. A positive value of a1 shows an increasing trend of

signal, while negative a1 gives a decreasing trend. The

occurrence of over-grinding is predicted by the continuously

derived a1.

4. Monitoring strategy

4.1. Feature selection

As introduced in Section 3, the key problem in process

monitoring is to correlate features of the measured data

series to the states of the process. There is a lot of literature

on this problem, as surveyed by Dooley and Kapoor [28].

The signal processing above is itself a process of character-

istic analysis. The aim of characteristic analysis is to discard

the less important information related to the grinding states

and to retain the useful data as for as possible.

It is advantageous to select some representative charac-

teristics as features to constitute a pattern space for describ-

ing the grinding states. The result of these experiments

suggests that the mean, variance, one-step autocorrelation,

model parameter a1 and residuals e are all suitable to be

selected as characteristic features. The following uses the

mean for between-passes monitoring and the model para-

meter for within-pass monitoring, respectively.

4.2. State identification

There are many methods for pattern recognition [29] that

may be used to identify the grinding states. The qualitative

criterion for state identification in monitoring is the best

criterion in actual application, because the workpiece, wheel

and machine may affect the response of the sensor during

grinding. Taking into account the requirements of the gen-

eral monitoring strategy, one should analyze the reaction

signals of AE, and of the tangential and normal forces when

over-grinding occurs. Attempts can thus be made to interrupt

the grinding and/or to change the grinding parameters after

over-grinding is detected.

In Fig. 3, the grinding conditions are: pre-set grinding

depth 0.005 mm; grinding velocity 10p m/s, and feedrate

5 m/min. The conventional grinding with transverse strokes

is used in this study. In the figure, the ground profiles of the

workpiece with multi-pass grinding after wheel-bite

occurred is shown. The first pass is from the left side to

the right side. It is easy to find that the grinding depth is

about 0.004–0.005 mm, which indicates that residual depth

is present and that no wheel-bite effect has occurred.

The second pass is from the right to the left. The profile is

clearly concave, and the largest grinding depth is 0.009 mm

at about two-thirds of the total grinding length from the start-

side. Further, the third pass, from the left to the right, is also

concave and the largest grinding depth is 0.010 mm at about

two-thirds of the total grinding length from the start-side.

The heat within the workpiece is conducted from the source

gradually pass by pass. The temperature of the workpiece is

uniform before grinding. At the first pass, the heat is

transferred into the workpiece, the temperature-gradient

inducing a high thermal bending moment (the details are

discussed in Sections 3 and 4). The workpiece is thus

deformed convexly pass by pass due to the high thermal

bending moment, and the largest grinding depth is at about

two-thirds of the total grinding length from the start-side.

However, in the fourth pass, the largest concave-point is at

the middle of the workpiece and the largest grinding depth is

0.015 mm, because the second and the third passes could

leave about equal amounts of heat at both sides.

Fig. 3. The top profiles of cooled workpieces.


The corresponding average grinding forces and AE values

of each pass are depicted in Fig. 4(a) and (b). It is found that

the AE increases gradually, the AE of the second pass being

about 1.2 times that of the first pass. Furthermore, the

grinding forces also display similar trends.

With the trends of AE and grinding forces, it is easy to set

the proper monitoring strategy for the wheel-bite effect. The

grinding forces (tangential and normal) of up-grinding are

larger than the down-grinding, and AE should behave the

same as grinding force, since the AE is proportional to the

grinding energy. However, in Fig. 4 it is found that the AE

and grinding forces are increasing pass by pass, which

differs from the basic reactions in surface grinding. The

qualitative criterion gives an effective indication on the

wheel-bite effect, and the grinding forces provide good

identification of the grinding states.

It is noted that the AE increase is not strong as expected,

since the AE transducer is mounted on the side of dynam-

ometer, and the transmission path of AE from the contact

zone to the transducer is disturbed once wheel-bite occurs.Fig. 4. The averaged grinding forces and AE of continuous passes.

Fig. 5. The original normal grinding force and the correlation between NFt and NFt�1 and NFt�2 during wheel-bite.


4.3. Dependence of NFt on NFt�1 and NFt�2

Fig. 5(a) shows the normal grinding force during wheel-

bite occurring in the process. It appears that the force signal

is independent of the time (sampling point). The dependency

of the force signal can be further examined by the first-order

model as shown in Fig. 5(b). In this figure, if NFt�1 is small,

NFt tends to be small; whilst NFt�1 is large, NFt tends to be

large.

The averaged normal force of 50 pieces is also analyzed

to check the dependency (Fig. 6). The derived figures show

approximately linear proportional relationships between

NFt and NFt�1, and between NFt and NFt�2. Although a

higher order should describe the signal trend accurately,

the computing time of deriving the model parameters is

longer, which might be unsuitable for on-line within-pass

monitoring. The first-order regressive model is used to

process the collecting signal once the lower-order regres-

sive model can give a clear indication that wheel-bite has

occurred.

4.4. State identification

Fig. 7(a) shows the grinding forces of a non-bite grinding

process. The workpiece is 100Cr6 steel of width 10 mm,

thickness 20 mm and length 150 mm. The average value of

each batch of data is derived, where a batch includes 50

pieces of data. The series of data are shown in Fig. 7(b). The

grinding forces depict a quasi-stable state. The value of a1 is

shown in Fig. 7(c), the value of a1 being based on the zero

line. In regression analysis, a positive value of a1 means that

the trend of the signal is to increase gradually. Therefore,

when the trend of the signal rises, the value of a1 is indicated

as positive. The value of a1 fluctuates in Fig. 7(c) when

wheel-bite does not occur.

Fig. 8(a) shows the top profile of the workpiece, where the

preset grinding depth is 0.01 mm. It can be seen that the

wheel-bite occurs where the grinding starts at the left side.

The corresponding original normal grinding force is shown

in Fig. 8(b). The trend of the normal force is to gradually

increase with respect to the depth of over-grinding. The

Fig. 6. The averaged normal grinding force and the correlation between NFt and NFt�1 and NFt�2 during wheel-bite.


original normal grinding force is averaged per batch of data,

as shown in Fig. 8(c). Using the regression model, the

averaged normal grinding force is processed and the value

of a1 is derived (Fig. 8(d)). The value of a1 in the regression

model is positive until the 14th sampling point, which means

that the depth of over-grinding increases gradually.

The original tangential grinding force is shown in

Fig. 9(a), whilst the corresponding averaged tangential force

Fig. 7. The normal grinding forces and the value of a1 without wheel-bite.

Fig. 8. The top profile, the original and average normal grinding force and the value of a1 in wheel-bite.

Fig. 9. The original and average tangential grinding force and the value of

a1 in wheel-bite.


and the value of a1 are shown in Fig. 9(b) and (c). The value

of a1 is positive until the 14th sampling point, although the

10th data has a slight negative value. The corresponding AE

signal processing is shown in Fig. 10. It can be seen that an

AE signal is not evident at the occurrence of the wheel-bite,

the reason was described in the previous section. A strong

AE signal is produced when the grinding depth increases,

but the bottom of the workpiece is separated due to the

convex deformation of the workpiece during wheel-bite.

Therefore, an AE transducer mounted on the side of

Fig. 10. The original and average AE and the value of a1 in wheel-bite.

Fig. 11. The top profile, original and average normal grinding force and the value of a1 in wheel-bite.

Fig. 12. The original and average tangential grinding force and the value

of a1 in wheel-bite.


magnetic chuck is unsuitable for receiving the AE, unless

wheel-bite does not occur.

Fig. 11(a) shows the lowest point of the workpiece top

profile exceeding the preset grinding depth. From Fig. 11(b)

and (c), it is found that the normal grinding force has a

significant trend to increase, so that the corresponding values

of a1 are positive until the 16th sampling point. Using this

signal processing, the wheel-bite effect can be detected

easily. Similar results can be found in Fig. 12, where the


Fig. 14. The top profile, original and average normal grinding force and the value of a1 in wheel-bite.

Fig. 15. The original and average tangential grinding force and the value

of a1 in wheel-bite.



Fig. 17. (a) The concave profile of the ground surface; (b) the average tangential forces per 5 mm of workpiece of the whole pass; (c) the value of a1.


tangential grinding force also gives a good indication of

wheel-bite. The AE values cannot provide a good trend with

respect to the actual grinding depth, as shown in Fig. 13.

Fig. 14(a) shows a very poor top profile of the work-

piece. The corresponding normal grinding force has an

overt trend. After processing the average normal grinding

force, a continuous positive value of a1 is derived (shown

in Fig. 14(c) and (d)). The tangential grinding force also

gives a clear indication of the wheel-bite effect in Fig. 15,

whilst the AE cannot give a good prediction of wheel-bite

in Fig. 16. By within-pass monitoring, the force signal can

detect the thermally-induced problem (wheel-bite), but the

AE sensing technique is insensitive for monitoring the

over-grinding effect due to the separation between the

workpiece bottom and the top surface of the chuck that

disturbs and weakens the transmission of AE to the AE

transducer.

5. Monitoring the experiments

Identification problems may be classified in qualitative

and quantitative terms. Quantitative identification is amen-

able to wheel-bite and deals with the estimation of the real

grinding depth once wheel-bite occurs. Qualitative identifi-

cation aims at discriminating between the occurrence and

non-occurrence of wheel-bite by its characteristics.

Qualitative identification is well used in this study for

detecting the wheel-bite between passes regardless of

the variation of the cutting parameters. The time-series

approach is crucial for within-pass, on-line identification

and in the previous section, it has been shown how it can be

employed for within-pass wheel-bite during the process.

However, the feasibility of a stochastic criterion must be

identified. In this section, the derived schemes for on-line

identification of the between-passes and within-pass

Fig. 18. (a) The concave profile of the ground surface; (b) the average tangential forces per 5 mm of workpiece of the whole pass; (c) the value of a1.


wheel-bite effect during the grinding process are, therefore,

required to be verified by a series of experiments.

The verification of within-pass monitoring is conducted

under the following conditions: workpiece HRC 58, SKD11;

wheel WA80K8V 187 mm � 10 mm � 25 mm; preset grind-

ing depth 0.010 mm; tangential velocity of the wheel

35.25 m/s; feedrate 3.28 m/min (up-grinding).

The tangential grinding force of the whole one-pass is

shown in Fig. 17(b). The variation of the value of a1 from

the time-series regressive model is depicted in Fig. 17(c).

The results indicate that the value of a1 is positive until

X ¼ 65 mm, then it changes transits to negative. It is thus

known that the grinding depth is gradually increased with

respect to the dimension X before X ¼ 65 mm (Fig. 17(a)).

The monitoring scheme thus can identify that over-grinding

has occurred.

The second verification of within-pass monitoring is

conducted under the following conditions: workpiece

HRC 58, SKS3; wheel: WA80K8V 187 mm � 10 mm�25 mm; preset grinding depth 0.020 mm; tangential velocity

of the wheel 35.25 m/s; feedrate 3.04 m/min (down-grind-

ing).

The concave profile of the ground surface is shown in

Fig. 18(a). The actual grinding depth is larger than the preset

grinding depth, and the lowest point of the profile is at about

X ¼ 95 mm. The tangential grinding force of the whole one-

pass is shown in Fig. 18(b). The variations of the value of a1

are depicted in Fig. 18(c). The results indicate that the value

of a1 is positive until X ¼ 85 mm, then changes to negative.

It is thus known that the grinding depth is gradually

increased with respect to the dimension X before

X ¼ 85 mm (Fig. 18(a)). In fact, the monitoring scheme

can identify that over-grinding has occurred before

X ¼ 45 mm, since the value of a1 is consistently positive

and the scheme judges the over-grinding by five values of

positive a1 coefficient.

6. Conclusions

Using the time-series modeling technique of grinding

forces, by the monitoring scheme can be identified over-

grinding induced by thermal convex deformation within one

pass effectively. While the AE signal fails to reflect the

corresponding grinding force due to the separation between

the workpiece bottom and the top surface of the chuck

resulting from the convex deformation of the workpiece,

a regressive model of order 1 gives a good indication on the

occurrence of within-pass wheel-bite. In monitoring verifi-

cation, it is shown that the earlier the over-grinding is

detected the better are the opportunities to avoid over-

grinding, therefore, prediction is an important requirement

of the monitoring strategy. In practice, it is found that the

identification algorithms of the within-pass work well. One

of the major benefits of using stochastic identification

resides in the improved efficiency to process the sampled

data. Experiments are well verified with the monitoring

strategy.

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On-line identiﬁcation of thermally-induced convex ...maps.pme.nthu.edu.tw/web_maps/PDF/PUBLICATION-TEXT/055.pdf · There are numerous works on monitoring used force measurement