Measurement 46 (2013) 546–551

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Measurement

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Fiber optic displacement sensor for imaging of tooth surface roughness

H.A. Rahman a,b,d, H.R.A. Rahim e,f, H. Ahmad b, M. Yasin c, R. Apsari c, S.W. Harun a,b,⇑a Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysiab Photonics Research Centre, Department of Physics, Faculty of Science, University of Malaya, 50603 Kuala Lumpur, Malaysiac Department of Physics, Faculty of Science and Technology, Airlangga University, Surabaya 60115, Indonesiad Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Malaysiae Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysiaf Faculty of Electronic & Computer Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

a r t i c l e i n f o

Article history:Received 21 November 2011Received in revised form 17 July 2012Accepted 27 August 2012Available online 8 September 2012

Keywords:Fiber optic displacement sensorTeeth sampleSurface roughness imagingNon-contact

0263-2241/$ - see front matter � 2012 Elsevier Ltdhttp://dx.doi.org/10.1016/j.measurement.2012.08.01

⇑ Corresponding author at: Department of ElFaculty of Engineering, University of Malaya, 50Malaysia.

E-mail address: swharun@um.edu.my (S.W. Haru

a b s t r a c t

A simple and inexpensive method using fiber optic displacement sensor is proposed formeasurements of tooth surface roughness based on the intensity modulation technique.A light beam was launched onto a tooth surface via a bundled fiber. The reflected light fromthe surface was collected and measured as a function of lateral distance to estimate theroughness of the surface. The system’s roughness measurement capability was successfullytested on teeth surfaces of varying surface texture. In the measurement, the average sur-face roughness, Ra for the canine, molar, hybrid composite resin and artificial teeth surfaceswere estimated to be approximately 121, 62.6, 39 and 37.6 lm, respectively. The experi-mental results indicated the capability of implementation of the displacement sensor forthe imaging of the tooth surface profile as well as a micron-size roughness estimator witha measurement error of less than 2.35%.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction

Measurement of teeth surface roughness irregularitiesthat occur as a result of erosion, abrasion and attrition, isan important indicator of the progression of surface tissueloss. A rough surface facilitates plaque retention and adhe-sion of microorganisms by reducing the efficacy of masti-cation and mechanical cleaning processes. As such, weare seeing an increasing number of studies investigatingfactors that modify the surface roughness of teeth in thepast recent years [1–6].

Atomic force microscope (AFM) is one of the interna-tionally recognized technique for roughness characteriza-tion on dental surfaces due to their ability to producehigh spatial resolution measurements [7]. However, sev-eral drawbacks have been identified such as long lead time

. All rights reserved.3

ectrical Engineering,603 Kuala Lumpur,

n).

prior to the measurement and the significant effect of thestylus tip on the surface roughness parameters. Moreover,because of the contact between the stylus and sample,there is the risk of damage to the surface of the tooth. Onthe hand, ultrasound is one of the most widely used tech-niques for non-destructive testing (NDT) [8–13]. A fre-quency range of 1–20 MHz is emitted by a transducerwhich is coupled to a test object. Pulses will travel throughthe object and are either reflected, diffracted or refractedby defects or discontinuities in the material. A receiver isplaced at the other side of the material to detect thereturning echo signal. Any existence of defects is thendetermined by the loss in signal amplitude. This techniqueoffers several advantages such as its superior ability to de-tect deep sub-surface defects compared to other NDT tech-niques. However, it requires complex image analysis andsuitable coupling medium between the transducer andthe object’s surface, which subsequently limits the versa-tility of this technique.

Optical techniques best satisfy the requirements ofin situ, non-contact inspection. However, implementations

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of most optical methods are often hindered by the diffi-culty of adapting them to harsh manufacturing environ-ments, complexity of design as well as the cost ofconstructing such systems. Conversely, fiber optic dis-placement sensors (FODSs) have been suggested for non-contact measurement and inspection of surface roughness[14,15].

The aim of our work was to develop a simple intensitymodulated FODS as a quantitative technique for the imag-ing and estimation of micron-size tooth surface roughness.This non-destructive imaging tool uses multimode plasticfibers as a probe as well as a red He–Ne laser as a transmit-ter. The reflected light from the tooth surface was coupledback into the probe and the intensity of the reflected lightrelative to the transmitted light was used to determine thedistance between the surface and probe. In our approach,variations of surface roughness were achieved throughthe use of different types of teeth surfaces, namely molar,canine, hybrid composite resin and artificial. These typeof sensors have been demonstrated to be efficient in differ-ent applications [16,17]. They are relatively inexpensive,easy to fabricate and suitable for employment in harshenvironments.

2. Material and method

2.1. Experimental setup

Fig. 1 shows the setup for the estimation of tooth sur-face roughness using FODS. The setup mainly consists ofa fiber optic transmitter, mechanical chopper, fiber opticprobe, four teeth samples (consisting each of the molar, ca-nine, hybrid composite resin and artificial sample), a sili-con photodetector, lock-in amplifier and computer(Fig. 1). The fiber optic probe is made of two 2 m longPMMA (polymethyl methacrylate) which consists of onetransmitting fiber core of 1 mm in diameter and 16 receiv-ing fiber cores of 0.25 mm in diameter, numerical apertureof 0.5, core refractive index of 1.492 and cladding refractiveindex of 1.402. A red He–Ne laser (k = 633 nm) was used as

Fig. 1. Setup for the estimation of tooth surface rou

the light source with an average output power of 5.5 mW,beam diameter of 0.80 mm and beam divergence of1.01 mrads. The photodetector is a high speed silicon pho-todiode with an optical response extending from 400 to1100 nm, making it compatible with a wide range of visiblelight including the 633 nm visible red He–Ne laser used inthis setup. The light source was modulated externally by achopper with a frequency of 113 Hz as to avoid the har-monics from the line frequency which is about 50–60 Hz.The modulated light source was used in conjunction witha lock-in amplifier to reduce the dc drift and interferenceof ambient stray light. The displacement of the fiber opticprobe was achieved by mounting it on a micrometer trans-lation stage, which was rigidly attached to a vibration freetable.

2.2. Data acquisition

Firstly, the reflectivity of the teeth surfaces were ob-tained by comparing the powers of the light source beforeand after the reflecting surfaces. Then, by using the setupin Fig. 1, the light source was coupled into the single trans-mitting core and then radiated to the tooth surface. Thelight reflected from the tooth surface was then transmittedthrough the 16 receiving cores to a photodetector. Theamount of light detected by the photodetector dependson the distance between the end of the probe and the tar-get being monitored. In this study, the signal from thereceiving fiber were measured against displacement bymoving the probe axially away from the zero point, wherethe flat reflective surface of the teeth samples and theprobe were in close contact. Each of the four teeth sampleswere used consecutively as the reflecting target whilstmeasuring the output intensity by changing the positionof the fiber optic probe from 0 to 4.5 mm in a step of0.05 mm.

Once the displacement curves have been obtained, theslope of the linear portion of the curves were determined.The slope represents the relationship between the outputvoltage and the displacement for each of the tooth surface.This information is critical in the roughness measurement

ghness using fiber optic displacement sensor.

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since changes in the output voltage per displacement willnot be the same for surfaces of different reflectivities. Theprobe was consequently fixed within the linear range ofthe displacement curve and the intensity of the collectedlight as a function of lateral movement (x and y axis) ofthe tooth surface were recorded while being maintainedin perpendicular and constant in axial position (z axis).This procedure was carried out with minimum successivesteps of 0.5 mm.

Fig. 2. Variation of output voltage against displacement.

2.3. Surface roughness imaging and statistical analysis

The raw data were processed and transformed into two-dimensional (2D) and three-dimensional (3D) imagesusing the Matlab 3D mesh surface plots. The number of Xand Y arrays that were created correspond to the numberof displacement steps taken along the x and y axis, respec-tively. The Z array represents the output voltage capturedby the lock-in amplifier corresponding to each of the[X,Y] position in the array. A fourth effective dimensionwas added to the graph by applying colors to the mesh sur-face which varies in accordance to the height of the mesh.The range of the colors followed a sequence of red–green–blue with red corresponding to the higher values of Z with-in the array. The 2D and 3D images help users to obtain aquick approximation of the tooth surface roughness. Sur-faces with monochrome colors (different shades of a singlecolor) represent a smoother surface compared to thosewith mixtures of different colors.

Further statistical analysis is required to obtain thequantitative roughness measurements. The average sur-face roughness (Ra) is the most widely used surface finishparameter by researchers and people in the industry. It ismost useful in quantifying and qualifying the change of aparticular surface roughness in response to external fac-tors. It is the arithmetic average of the absolute value ofthe height of roughness irregularities from the mean valuemeasured, that is:

Ra ¼Xn

i¼1

jyij !

=n ð1Þ

where yi is the height of roughness irregularities from themean value and n is the number of sampling data.

A higher value of Ra reflects a coarser tooth surface. Inorder to obtain the values of Ra, the standard deviationsof the output voltages within each scanning lines (y axis)were first measured. Since the standard deviation showshow much variation exists from the mean value, the con-version to its respective displacement value will produceyi for each column. This is equivalent to the ratio of thestandard deviation to the slope of the output voltage ver-sus displacement curve. Lastly, the values of Ra were ob-tained by averaging the values of yi.

Stability measurements or the measurement errorswere obtained by capturing a total of 100 output voltagereadings continuously for 200 s while the fiber probe wasfixed at a point on top of the surface. During the experi-ment, the temperature was kept constant at 25 �C andthe error due to the temperature variation was negligible.

3. Results

Fig. 2 shows the reflected light intensity versus distanceof the fiber optic probe from various reflecting teeth sur-faces, namely molar, canine, hybrid composite resin andartificial. All curves exhibit a maximum with a gradualand linear increase in the front slope while the back slopefollows an almost inverse square law relationship. Whenthe displacement was increased, more light were able tobe collected by the receiving cores due to the increasedsize of the reflected cone of light. A further increase inthe displacement led to larger overlapping which resultedin further increase in the output power as seen in the firstpart of the curve until it reached the peak value. At thispeak value, the reflected cone power fell within the surfacearea of the receiving fiber. Further increase of the displace-ment resulted in a reflected cone size bigger than the sizeof the receiving fibers. Therefore, only a fraction of thepower of the reflected light was detected, as seen in thesecond part of the curve. The received light intensity variedconsiderably among the various teeth surfaces due to thedifferent reflectivity of the teeth surfaces. The reflectivityof molar, canine, hybrid composite resin and artificial teethsurfaces were obtained at 4.72%, 4.18%, 2.16%, and 1.82%,respectively.

The features of the FODS for different teeth surfaces aresummarized as shown in Table 1. The sensitivity of thesensor was determined by a slope of straight line portionof the curves. As shown in Fig. 2, the sensor has two slopes;front and back slopes, with a higher sensitivity in the frontslope. Based on the analysis of the front slope, thesensitivities and linear range for the molar, canine, hybridcomposite resin and artificial teeth surfaces were obtainedat 0.9667 mV/mm and 0.45 mm; 0.775 mV/mm and0.4 mm; 0.5109 mV/mm and 0.5 mm; and 0.25 mV/mmand 0.5 mm, respectively, with a good linearity of morethan 99%. The peak voltages also decreased in the same or-der as the decrease in sensitivities which were 1.05, 0.685,0.475 and 0.415 mV for the molar, canine, hybrid compos-ite resin and artificial teeth surfaces, respectively. Thehighest resolution of approximately 0.0025 mm (frontslope) was obtained with the molar tooth surface. Thestability of the displacement sensor was also investigatedand the measurement errors were observed to be less than

Table 1The features of the fiber optic displacement (front slope) for various flat teeth surfaces.

No. Type of teeth and dimension Linear range (mm) Peak voltage (mV) Sensitivity (mV/mm) Resolution (mm)

1 Molar, 7 mm � 7 mm 0.05–0.5 1.05 0.9667 0.00252 Canine, 6 mm � 7 mm 0.05–0.45 0.685 0.775 0.00673 Hybrid composite resin, 9 mm � 9 mm 0–0.5 0.475 0.5109 0.00534 Artificial, 8 mm � 10 mm 0–0.5 0.415 0.25 0.0084

Fig. 3. 2D and 3D views of the molar tooth surface profile.

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0.3%, 0.88%, 2.35% and 0.67% for the molar, canine, hybridcomposite resin and artificial teeth surfaces, respectively.

Fig. 3 shows the 2D and 3D views of the molar toothsurface, which was obtained by 27 � 27 lines of scanningalong the row axis and column axis. The intensity of the re-flected light from the tooth surface depends upon the sur-face texture of the tooth and the standoff distance betweenthe surface and fiber tip. The level of roughness which wasobtained from the deviations of the reflected light were ob-tained at 11.2% with a respective average surface rough-ness of 62.6 lm. The experiment was then repeated forcanine, hybrid composite resin and artificial teeth surfaceswith results as shown in Figs. 4–6. Based on the figures, thelevel of roughness and average surface roughness for thecanine, hybrid composite resin and artificial teeth surfaceswere obtained at around 19.4% and 121 lm; 13.1% and39 lm; and 2.9% and 37.6 lm, respectively. Table 2 sum-marizes the experimental results for all the teeth surfaceroughness parameters.

4. Discussions

The experimental results prove that the sensor can dis-tinguish between different teeth surface roughness, whichis particularly useful for characterization purposes. On theother hand, confinement of analysis on a single type oftooth facilitates studies on prolonged effects of chemicalsused in dental restoration procedures. Furthermore, theproduced images can aid users in obtaining a quick

approximation of the tooth surface roughness and also inselecting specific areas for quantitative roughness mea-surement. Users can visualize the exact location of irregu-larity or defects occurring on the tooth surface. The sensorproves to be simple and easy to be implemented. The mainweakness of this technique in comparison with commer-cially available roughness measurement technique is thecomparatively longer data scanning time. The displace-ments of the fiber probe were manually done hence result-ing in approximately 20 min per complete scan. Thishowever can be solved by the use of picomotor actuatorswhich can significantly reduce the scanning time depend-ing on the type used. Post treatment of data using digitalimage processing can also enhance the accuracy of thesystem.

5. Conclusions

A fiber optic sensor is introduced as a novel non-destructive method for quantitative imaging and measure-ment of tooth surface roughness in a non-contact mode.The sensor is based on intensity modulation techniqueand uses a multimode plastic bundled fiber as a probeand a He–Ne laser as a light source. The feasibility ofthe sensor to estimate surface roughness of different typesof teeth have been demonstrated. For instance, the averagesurface roughness, Ra for the canine, molar, hybrid compos-ite resin and artificial teeth surfaces were estimated to beapproximately 121, 62.6, 39 and 37.6 lm, respectively with

Fig. 4. 2D and 3D views of the canine tooth surface profile.

Fig. 5. 2D and 3D views of the hybrid composite resin tooth surface profile.

Fig. 6. 2D and 3D views of the artificial tooth surface profile.

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Table 2Level of surface roughness and surface finish parameter (Ra) for various teeth surfaces.

No. Type of teeth Level of roughness (%) Level of roughness, yi (lm) Average surface roughness, Ra (lm)

1 Molar 1.7–11.2 13.8–114.3 62.62 Canine 1.6–19.4 23.9–257.7 1213 Hybrid composite resin 3.4–13.1 17.4–63.9 394 Artificial 1.3–2.9 20.6–45.7 37.6

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an average error of less than 2.35%. This will greatly assistdental researchers in the characterization and evaluationof different dental surfaces under the exposure of differentconditions.

Acknowledgements

The authors are grateful to Dr. Suhaila Abdullah and Dr.Mohd Noor Fareezul Noor Shahidan for providing the hu-man teeth samples used in this experiment. This work isfinancially supported by University of Malaya under PPPGrant Scheme (No. PV033/2011A) and UMRG (RG108/11AET).

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