Contamination on bonding surfaces has been determined to be a primary cause for degraded bond strength in certain solid rocket motor bondlines. Hydrocarbon and silicone based organic contaminants that are airborne or directly introduced to a surface are a significant source of contamination. Diffuse infrared (IR) reflectance has historically been used as an effective technique for detection of organic contaminants, however, common laboratory methods involving the use of a Fourier transform IR spectrometer (FTIR) are impractical for inspecting the large bonding surface areas found on solid rocket motors. Optical methods involving the use of acousto-optic tunable filters and fixed bandpass optical filters are recommended for increased data acquisition speed. Testing and signal analysis methods are presented which provide for simultaneous measurement of contamination concentration and roughness level on rough metal surfaces contaminated with hydrocarbons

Pearson, L. H.

English

NASA Technical Reports Server

/¢ j
N95- 31804
Cleanliness Evaluation of Rough Surfaces
with Diffuse IR Reflectance
L. H. Pearson
Thiokoi Corporation
P.O. Box 707, M/S 244
Brigham City, Utah 84302
Abstract
Contamination on bonding surfaces has been determined to be a primary cause for
degraded bond strength in certain solid rocket motor bondlines. Hydrocarbon and silicone
based organic contaminants that are airborne or directly introduced to a surface are a
significant source of contamination. Diffuse infrared OR) reflectance has historically been
used as an effective technique for detection of organic contaminants, however, common
laboratory methods involving the use of a Fourier transform IR spectrometer (FTIR) are
impractical for inspecting the large bonding surface areas found on solid rocket motors.
Optical methods involving the use of acousto-optic tunable filters and fixed bandpass optical
filters are recommended for increased data acquisition speed. Testing and signal analysis
methods are presented which provide for simultaneous measurement of contamination
concentration and roughness level on rough metal surfaces contaminated with hydrocarbons.
Introduction
IR reflection spectroscopy is an analytical methodology that has been applied
previously to the problem of surface and thin film characterization (1-3). More specifically,
diffuse reflectance IR spectroscopy has been evaluated for detection of organic contaminants
on bonding surfaces (4-6) and has been found to be effective for detection of contaminants
with sufficiently strong IR absorption bands. The use of IR spectroscopy allows both the
detection and identification of contaminant types since each has a characteristic absorption
spectrum (fingerprint). However, the use of laboratory analytical FTIR instrumentation is not
practical for on-line inspection or mapping of large bonding surface areas such as those found
on solid rocket motor components the size of the Shuttle and large strategic defense motors.
In addition to the rapid data acquisition requirements imposed by their large size, surfaces on
solid rocket motors tend to be rough, such as grit blasted metal surfaces, or machined
surfaces of fiber filled polymeric materials. Roughness has a tendency to lower signal to
noise ratios and thus may impose additional constraints on optical design.
Contaminants most commonly found on solid rocket motor bonding surfaces are either
hydrocarbon or silicone based and are introduced by direct contact with tooling or personnel,
or environmentally introduced via airborne particulates that are deposited on the surface.
When these contaminants are found on rough metal surfaces, diffuse IR reflectance methods
must be able to find the contaminant in the presence of roughness and further, must be able to
distinguish contaminant concentration variations from roughness variations. Optical methods
and data acquisition and reduction methods for providing speed and reliable detection of
hydrocarbon contamination on rough (grit blasted) metal surfaces are specifically addressed in
this paper.
_ ,_ 573
https://ntrs.nasa.gov/search.jsp?R=19950025383 2020-06-16T06:15:24+00:00Z
Methodology
Hydrocarbons can be readily detected on a surface by looking for the presence of their
characteristic carbon-hydrogen (C-H) stretch band at about 3.4 micrometers (_m). Previous
work (4) has indicated that backscattered IR methods provide greater sensitivity to small
concentrations of contaminants on rough metal surfaces than do specular methods.
Backscatter optics must be coupled with a means for rapid detection of scattered light intensity
over a continuous band of wavelengths or at several discrete narrow wavelength bands that
include the 3.4 #m absorption band and at least two reference wavelength bands (side-bands).
This is accomplished by using an acousto-optic tunable filter (AOTF) for continuous or
discrete wavelength band detection, or by using fixed band-pass filters for discrete wavelength
band detection. Figures 1 and 2 show schematic diagrams for these two optical methods.
Both the AOTF and the discrete optical filter methods shown in the figures provide
sufficiently rapid data acquisition capability so that an instrument based on either method can
collect data on-the-fly when scanned over a surface at speeds up to a few inches per second.
Hence, measurement rates on the order of 10's to 100's of complete spectral measurements
(described below) per second are achievable. The AOTF optical method is described in detail
in reference (4) and will not be further described in this paper. The discrete optical filter
method, shown in Figure 2, splits the scattered beam into three beams by using spectral beam
splitters and band-pass filters. Each beam has its own detector, hence, parallel data collection
is made possible for each wavelength band, significantly increasing data collection speed. A
more complete description of this optical setup is found in reference (7). All data presented
in this paper was obtained with the discrete optical filter design implemented in a packaged
instrument cooperatively developed by TMA Technologies and Thiokol Corporation and given
the trade name of SurfMap-II TM.
The objective of the optics is to rapidiy obtain measurements from which the peak
height of the C-H stretch absorption band is determined. The required measurements are the
absorbance at two side-band wavelengths, with one on each side of the band peak, and the
absorbance at the wavelength of the band peak. Figure 3 shows the spectrum of the C-H
stretch absorption band for a typical hydrocarbon contaminant with the wavelength bands of
the three measurements indicated. In order to determine the peak absorbance, a line is fit to
the two side-band measurements to approximate the baseline of the spectrum. The
interpolated value of the baseline at the wavelength of the peak is subtracted from the
measured peak absorbance. The resultant value, AA, is indicated in Figure 3 and has been
found to be directly related to contamination concentration (4-6). The process is
mathematically described below.
The linear approximation of the absorption spectrum baseline is calculated from
1) AbCA)= a + b h
where a and b are constants determined from the side-band measurements, 1 and 2, shown in
Figure 3, and _, is the wavelength. Constants a and b are given by
2) b = (A2- At) / (3.7-3.2) = (A2- AI) / 0.5
3) a = A, - b (3.2)
574
_ ELECTRONICS I----------_OPTICAL
INTERFACE
PC GRAPHICS
DISPLAY
SCAN
OPTICAL
INTF_2,FACE
CONTROLLER
Figure 1. Schematic diagram for AOTF based optical setup.
Lamp
Chopper
Long Ref
Receiver
Filter,
Detector
Lens
Lens
Absorption
Receiver
Spectral
Beam
Short Ref
Receiver
Collector
Lens
Position
Sensitive
Detector
/
Surface
Figure 2. Schematic diagram for discrete band-pass filter based optical setup.
575
Figure 3.
lil/ 
linear baseline /
approximation
A=a+b_,
,_ cq
wavelength
Typical C-H stretch absorption band with filter band locations indicated.
where At and A 2 are the measured absorbances for the two side-band wavelengths. For the
C-H stretch absorption band, side-band 1 wavelength is assumed to be 3.2 #m, side-band 2
wavelength is assumed to be 3.7 #m, and the peak wavelength is assumed to be 3.4 #m for
the specific optical filters used in the SurfMap-Ir ru. Using equation 1, AA is calculated from
4) AA = Ap - Ab(X=3.4)
where Ap is the measured absorbance at the C-H stretch band peak.
The absorbance measurements Ap, A1, and A 2 are determined from the backscattered
IR light power measured from the surface and from corresponding measurements taken from a
clean reference plate with a known average root mean square roughness (RA). That is,
5) At = - Ioglo(Pl./Pl_r)
6) A 2 = - loglo(P2JP2ref)
7) Ap = - loglo(Pp,/Pp_r)
The quantities P,,, P2,, and Pp, are the measured backscattered power levels from the sample
surface at the respective wavelengths, 3.2, 3.7, and 3.4 #m. Likewise, Pl,,f, P_,t, and Pp_,t
are the corresponding power measurements for the clean reference plate.
Subtracting the baseline from the peak absorbance measurement is important as it has
been determined experimentally that this procedure significantly reduces the effect of
roughness variations on the measurement as will be further discussed in the next section. The
SurfMap-II TM performs the calculations listed above and outputs a, b, and AA in the form of a
computer image file which is constructed while raster scanning the instrument over a surface.
Since The effect of roughness on absorbance measurements is reduced by applying
576
equation 4, it is plausible that one or both of the constants a and b in equation 1 relate to
roughness. In the following section, it is shown that the baseline slope, b, can be correlated
with RA.
Results
A series of grit blasted steel (D6AC) plates with clean surfaces and known RA
roughness were mapped with the SurfMap-II TM. The average AA, a, and b were determined
from scan data for each plate. Reference measurements were taken from a clean plate with
known RA. These tests were repeated several times using reference plates with different RA
values. The average AA, a, and b values were plotted with respect to average RA for each
plate and it was found that b was proportional to the difference between the RA of the sample
and the RA of the reference plate. That is,
8) RA - RA_f = Sb b
where Sb is a proportionality constant determined by linear regression.
The samples were then prepared with known concentrations (approximately 5, 10, and
15 mg/ft 2) of a hydrocarbon grease (CONOCO HD-2). Measurements of average AA, a, and
b were repeated and it was further found that b was insensitive to the presence of
contamination at these low concentrations. Hence, b provides an independent measurement of
surface RA if RA,,r is known and Sb has been determined. Figure 4 shows a plot of RA-RA,,f
verses b for all measurements from both clean and contaminated samples.
0.0001
gh
o
m
_rJ
5E-05
0
-5E-05
-0.0001
-80 -60 -40
m Fit by Linear Regression
I
-20 0 20 40 60 80
RA-RA(re0
Figure 4. Dependence of approximated spectrum baseline slope, b, on RA.
577
When correlating AA with RA and with contamination concentrations it was found
that AA is strongly dependent upon contamination concentration as expected since AA is a
measure of the C-H band peak height, but it is also weakly dependent on roughness, that is,
upon RA-RA_f. The dependence on RA-RA,.f was found to be linear, but the slope of the
line was empirically determined to be quadratically related to contamination concentration (see
Figure 5) and the intercept was found to be proportional to contamination concentration (see
Figure 6). That is,
0
-5E-05
_ -O.O001
u
© -0.00015
o
-0,0002 , , , ,m
0 2 4 6 8 10 12 14 16
Contamination Concentration (mg/ft^2) •
Figure 5. Dependence of slope (SAA) of AA verses RA curves on contamination
concentration.
0.015
0.012
,_ 0.009
M
0.006
0.003
0
.............. Least-Squares Fit [ .............................................................._l
t........................................................................................
0 2 4 6 8 10 12 14 16
Contamination Concentration (mg/ft^2)
Figure 6. Dependence of AA on contamination concentration.
578
9) AA = AA,_ + S_ (RA -RA,,,f)
where
10) AA,_f = So h
11) S_ = y. + S, h2
Substituting equations 10 and 11 into equation 9 gives
12) AA = S,h + [y. + S.h 2]Sbb
The known hydrocarbon contamination concentration is h, and So, y,, and S. are constants
determined by a least-squares fit of measured AA, b, and known h to equation 12.
When RA=RA,,f, it is found that b=0 and equation 12 becomes
13) AA = Sc h
It is observed that the second term in equation 12 is the correction for RA when RA,_f is
different than RA for the sample (when b # 0). When the constants, S,, y,, S., and Sb have
been determined, it is possible to estimate the roughness corrected contamination
concentration from
-So
14) h =
+ [S, 2-2S.Sbb(y.sbb-AA)] v:
2S, Sbb
which is obtained by solving equation 12 for h. If roughness is ignored, then contamination
concentration is estimated from equation 10 by solving for h to give
15) h = AA/S_
Figure 7 shows the correlation of estimated h with known h when using equation 15 and
Figure 8 shows the correlation using equation 14. Note that when correcting for RA (using
equation 14), the data spread is reduced measurably so thai at low contamination
concentrations (h near zero), the accuracy is improved by about a factor of 2 from about + 2
mg/ft 2 to about + 1 mg/ft 2.
Conclusions
Optical methods have been recommended for providing rapid acquisition of
absorbance data needed for detecting hydrocarbon contaminants on rough metal bonding
surfaces. Signal processing methods have been developed for allowing simultaneous
determination of contamination concentration and RA roughness. These optical and signal
analysis tools provide a practical system for on-line inspection of large area bonding surfaces
such as is found on solid rocket motors.
579
25
¢q
<
E
all
= 10
o
_ 5
!
E 0
-5
0
I I t 1 J I l I I I I I I t t
2 4 6 8 10 12 14 16
Known Concentration (mg/ft^2)
Figure 7. Contamination concentration estimate from AA measurement compared with
known concentration without correction for RA (equation 15).
,_, 25
¢q
<
20
E
© 15
"43
t
v...
= 10
o
_ 5
E 0
-5
m
m
I
.................................................... m ........................................................................................................................................
I I i t I I I ! ( I t I I I
2 4 6 8 10 12 14 16
Known Concentration (mg/ft^2)
Figure 8. Contamination concentration estimate from AA measurements compared with
known concentration using RA corrected equation 14.
580
References
. Hansen, W. N., "Reflection Spectroscopy of Adsorbed Layers," Symposium of the
Faraday Society, 1970, No. 4, pp. 27-35.
. Hansen, W. N., "Reflection Spectroscopy of Optical Coatings," J. Opt. Soc. Am.,
Vol. 69, No. 2, 1979, pp. 264-272.
. Hansen, W. N., Pearson, L. H., and Hansen, G., "Characterization of Small
Absorptions in Optical Coatings," Laser Induced Damage in Optical Materials, NBS
Spec. Publ. 568, 1979, pp. 247-251.
o Pearson, L. H., "Bond Surface Contamination Imaging Using an AOTF
Spectrometer," Review of Progress in Quantitative Nondestructive Evaluation, Vol.
12, Edited by D. O. Thompson and D. E. Chimenti, Plenum Press, New York, 1993,
pp. 1619-1623.
° Pearson, L. H., "Diffuse Reflectance IR Spectroscopy for Bonding Surface
Contamination Characterization," Review of Progress in Quantitative Nondestructive
Evaluation, Vol. 10, Edited by D. O. Thompson and D. E. Chimenti, Plenum Press,
New York, 1991, pp. 581-588.
, Pearson, L. H., "IR Spectroscopy for Bonding Surface Contamination
Characterization," Review of Progress in Quantitative Nondestructive Evaluation, Voi.
9, Edited by D. O. Thompson and D. E. Chimenti, Plenum Press, New York, 1990,
pp. 2017-2024.
, Swimley, B. D., Knighton, M. W., Khurdal, V. C., Pearson, L. H., Stover, J. C.,
"Design review of an Instrument to Map Low-Level Hydrocarbon Contamination,"
Proc. SPIE, 1995-10, July 1993, pp 92-100.
581



Cleanliness evaluation of rough surfaces with diffuse IR reflectance

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