REAL TIME CONTROL OF MULTI-PHASE
ELECTRONIC ASSEMBLY CLEANING AGENTS
Ram Wissel, Mike Bixenman, Jason Chan & T.C. Loy
Kyzen Corporation
Nashville, TN 37211
ram_wissel@kyzen.com
ABSTRACT
Aqueous cleaning fluids are engineered to remove the most
challenging flux residues from electronic assemblies of
varying complexities. Multiple equipment configurations,
such as conveyorized inline or batch cabinet, are commonly
employed to deliver the cleaning agent to the device at
elevated temperatures often using pressurized spray
impingement. Having the ideal amount of chemistry in the
wash solution is critical to maintaining an efficient, high
yield cleaning process. Cleaning systems can be very
dynamic processes, with wash concentration changing
significantly in only a few hours in some cases.
Real time monitoring is relatively straightforward; however,
monitoring and controlling a wash bath is a highly complex
achievement. The purpose of this research is to present
advanced technology for monitoring, controlling and
measuring contamination levels. The goal of this technology
is to provide the assembler with real time data for
controlling the cleaning process.
Key words: Electronic assembly defluxing, Concentration
measurement, Soil loading, Differential density, Sonic
velocity, Refractive index, Flux cleaning
Introduction
Assemblers must know that the wash bath is within control
limits. Controlling the wash bath requires the ability to
monitor the chemistry/water ratio, adjust for build-up and
depletions, as well as predict when the wash bath is
depleted. Real time monitoring is relatively straightforward;
however, monitoring and controlling a wash bath is a highly
complex achievement.
Electronic assemblies incorporating QFN’s and other low
gap leadless devices are increasingly washed with aqueous
cleaning solutions even if built with water soluble flux
technology. While a diverse series of aqueous cleaning
agents are available in the marketplace today, they can
generally be grouped into one of two categories:


Homogeneous (fully water soluble)
Multi-Phase (partially water soluble)
Flux and solder technology have undergone significant
change over the past several decades. The transition from
rosin (e.g. activated and mildly activated) to “no-clean”
(low residue) and water soluble fluxes combined with alloys
shifting from eutectic tin-lead to the numerous Pb-free
compositions has forced cleaning agent formulators to
develop new compositions that better match and dissolve
the ever evolving residues. Multi-Phase cleaning agents are
one of those innovations. This cleaning agent design
exhibits excellent performance on all flux types, but due the
two phases, it is difficult to control.
While there are advantages to both homogeneous and multiphase technologies, for about the last decade the leading
product(s) that remove a broad array of no-clean, RMA, and
water soluble flux residues in a single process fall into the
multi-phase category; which are therefore the focus of this
study.
For over 20 years, several technologies have been employed
to measure cleaning agent concentration of both
homogeneous and multi-phase aqueous solutions. This
research investigated the performance of three common
methods and one novel technique to determine (1) how
accurately each measures virgin solutions and (2) how
variations in flux loading and flux types affect the precision
of these readings.
Additionally, when compared with not cleaning or DI water
alone, the use of an engineered aqueous cleaning agent adds
complexity and expense to the production process. In order
to establish a reliable, cost effective process, important
considerations include:

What is the best method to accurately measure the
concentration?





How much does soil load affect the concentration
measurement?
How dynamic is the process and does the
concentration change rapidly?
How is the concentration maintained?
o manual vs. automatic
Operator exposure and the risk of spills during
manual measurements and additions.
Logging and trending critical parameters to detect
anomalies before they become larger issues.
Real Time Cleaning Agent Control
Two common questions assemblers ask when selecting a
cleaning agent.
1. How much does it cost?
2. How long will it last?
In reference to cost, aqueous cleaning agents are formulated
as concentrates. Cost is a function of the concentration level
required to clean soils, volatility of the active materials in
the cleaning agent, drag-out potential, number of boards
cleaned, bath life, make-up concentration, ability to control
and discharge. A best practice cleaning agent engineered to
optimize each of these factors is typically less costly to run,
irrespective of the cost per gallon.
Bath life is also a multi-variant issue. Bath life is a function
of the cleaning agent design. Flux residues are acidic in
nature. To achieve extended bath life, these acidic materials
must be neutralized and held in solution. Polar activators
formulated into the cleaning agent are critical in inducing
attractive forces, stabilizing the wash bath as soil loads and
providing reserve alkalinity needed to achieve desired
cleaning effects. Controlling the wash bath is another
critical factor. An uncontrolled wash bath functions like a
“saw tooth” where the concentration may frequently exceed
the upper and lower control limits. When this occurs, bath
life is compromised. Cleaning agents controlled within the
upper and lower limits extends the wash bath. Real time
control is an important factor, especially when running in a
highly dynamic inline spray-in-air cleaning system.
Aqueous Cleaning Machine Designs
A high percentage of assemblers operate aqueous spray-inair cleaning machines. For high volume production, inline
cleaning machines are often selected. For moderate to high
volume product, batch cabinet cleaning machines are
popular. Machine dynamics can vary significantly between
the two design options.
Inline cleaning machines use an open tunnel design. Boards
track through the cleaning machine on a conveyor belt.
There are a number of sections that makes up the inline
cleaning machine. Each brings a level of important in both
controlling concentration and consumption (Figure 1).
 Pre-Wash ~ Low energy design to soak and wet the
board, which softens the residue and starts the
solubilization process
 Wash ~ High energy spray designed to deliver the
cleaning agent to the source of residue. The
greatest challenge is the energy required to deflect
the cleaning agent under bottom terminated
components
 Chemical Isolation ~ Chemical isolation section
has two important functions: 1. Designed to wipe
and recover the cleaning agent from the boards and
pallets and 2. Pre rinse boards to remove bulk of
cleaning agent and soils before entering rinse
section
 Rinse Section ~ Designed to remove cleaning agent
and ionic residues from boards
 Final Rinse ~ Board is only as clean as the final
rinse. The final rinse is fed with ionically pure
water in an effort to leave the board ion free
 Dry ~ Designed to strip the water from the boards
and render the assemblies free of moisture
Figure 1: Inline Cleaning Machine
The dynamic nature of the inline cleaning machine requires
process control to maintain cleaning within control limits.
Concentration changes occur rapidly from exhaust draw,
wash pump operation creating mist, water losses and dragout. It is not uncommon to lose 10 gallons (38 liters) of
wash tank solution per hour of machine operation. A factor
not often considered is the volatility of water. Water’s heat
of vaporization is lower than that of higher boiling materials
that make up the cleaning agent. As such, water is lost at a
faster rate than is the cleaning agent (Figure 2). Injecting
cleaning agent at the desired target concentration level will
result in the wash concentration climbing over time.
Adjusting for this factor is critical to maintaining the wash
concentration within the preset upper and lower limits.
•
•
Ran typical cycles (time & temp)
Verified concentration every few cycles
Cleaning agent concentration was monitored and charted
over time (Figure 4).
Figure 2: Wash Concentration Rises when adding back at
the Designed Concentration Level
In contrast to the aqueous spray-in-air inline cleaning
machine design, the batch spray-in-air cabinet style machine
has a sealed chamber with very low exhaust (Figure 3). The
batch machine exhibits reduced drag-out and exhaust loss
potentials. It was postulated that wash concentration would
more naturally hold within a tight concentration range due
to closed design.
Figure 4: Batch Cleaning Study Losses between Water and
Cleaning Agent over 20 Cycles
When comparing Figures 2 and 4, the data finds that the
batch system is much more consistent than the high volume
and unbalanced cleaning solution loss rate for an in-line
cleaning process. This allows for less adjustment in the
make-up ratio while maintaining it close to the target
concentration. This result also suggests that it is much more
important to monitor and actively control the water /
cleaning agent ratio in the non-closed cabinet applications.
When controlling the wash bath, the following three factors
must be understood in order to make accurate process
adjustments:
Need to Know:
• Current Concentration
• Current Tank Vol.
• Total Tank Capacity
Figure 3: Cabinet Style Batch Cleaning Machine
Other key factors that the process owner may want to know:
• Temperature
• Accumulated Wash Hours
• Total Consumption & Usage Rate
• Soil Load / When to Dump
Experimental #1
The purpose is to measure wash/cleaning agent ratio over an
extended time period. For Batch Cabinet Style cleaning
machines the following methodology was studied:
• Three (3) leading manufacturers
• Filled wash tank to normal operating level with
known chemical concentration
• Isolated all make-up (chemisty & water)
• Loaded chamber with boards to create surface area
When “control” or a process adjustment is required, how is
it accomplished? It starts with accurate concentration
measurement. There are several methods available, and the
ideal choice depends on the type of cleaning agent being
employed (homogeneous or multi-phase) and the operating
range. Then use algebra to calculate how much water and
fresh cleaning agent are needed to restore the ideal operating
level and desired concentration. Then the fluid additions
must be performed – accurately and safely.
Once
completed, the cycle starts over where after a period of
operating time the concentration must be re-verified and
corrected again.
Manual Additions
• Often driven by low level switch or alarm
– Maybe OK for Batch
– In-Line can change dramatically
• Imprecise additions
– “That looks about right”
• High risk of spills / exposure
True Process Control
• Automatic, Controlled
Chem. & Water additions
• Maintain concentration
+/- small tolerance
• Eliminate operators
making additions
• Reduce routine
sampling.
• Best for highly dynamic systems.
Figure 5: Manual Control
Figure 7: Automated Process Control
Semi-Automatic
• Water driven proportioner
– Years of proven experience
– Adjustable injection rate
• Batch:
– Preferred solution
– Set it and forget it….In Control
• In-Line:
– Works OK, but, often requires frequent
operator adjustments
Process Control Trending
• True control enables powerful trending.
• Early detection of issues before they escalate.
– Valve leaks / mispositions
– Spray misalignment
• Real-Time alarms and notifications when threshold
exceeded.
Figure 8: Water / Cleaning Agent Additions using Process
Control Additions
Figure 6: Semi-Automatic Water / Solvent Proportioner
When running an inline cleaning process, process control is
necessary. When running Multi-Phase cleaning agents,
process control is not straight forward. Process Control
Methods must account for loss differences between the
cleaning agent, water and soil to achieve accurate control.
Process Control Methods:
Refractive Index
The speed of light varies as it passes through different
materials. For example, light travels 1.33 times slower in
water than it does in a vacuum. Refractive Index (RI) is
determined by measuring the deflection of light as it passes
between two materials of differing composition.
While RI is a dimensionless number, the electronics
industry traditionally measures the solute concentration in
homogeneous solutions in degrees Brix. The Brix scale,
which was developed in food & beverage industry, is based
on the percentage of sugar dissolved in water; where 1°Brix
equals 1% sugar by mass. Refractive index is therefore an
approximate and relative measurement of the density of a
solution. A near linear correlation is often found between
concentration and RI.
Most manual (analog and digital) and in-line refractometers
are temperature compensated because of the impact that
temperature has on fluid density. Because the refractometer
optically “sees” into the solution the measurement can be
affected by poor clarity or air bubbles. Non-homogeneous
mixtures, where the beam of light passes through localized
pockets of multiple liquid phases, are also problematic for
consistent and accurate RI readings.
Sonic / Acoustic Velocity
Sonic Velocity (SV) and refractive index are similar in that
they measure transmissive physical properties of a solution.
However, where RI indirectly measures velocity change by
angle of light deflection, sonic velocity is a direct
measurement of the speed of sound in a single medium.
This is accomplished by generating a specific signal using a
transducer at one point and “listening” for the signal at a
fixed distance away. The most common unit of
measurement in sonic velocity is meters per second (m/s).
Sonic velocity behaves similarly to the principles of sonar;
it is affected by temperature and composition of the fluid.
In general, sound travels faster in liquids than in air, and
faster in solids than liquids. This phenomenon is often
misinterpreted as resulting from the greater density of the
faster medium. Sonic velocity is dependent on the elasticity
and density of the medium. Elasticity refers to how a
deformed object (or molecular lattice) “springs” back to its
resting shape when the force is removed. Sound travels in
compressive waves, which deform the medium as it
propagates. Example 1: Air and hydrogen have similar
elastic properties (bulk modulus for gases), while the
density of hydrogen is less than air. Sound travels
approximately 4 times faster in hydrogen due to its lower
density. Example 2: Although the density of air is a fraction
that of iron, iron’s elasticity is several orders of magnitude
greater than air; thus sound travels faster (~14x) in iron than
air. The greater the elasticity and the lower the density, the
faster sound will travel through the medium. Temperature is
also an important factor as it affects the density of aqueous
solutions.
Because the specific gravity of the excess solvent phase in
typical splitting chemistries is near 1, the hypothesis is that
sonic velocity measurements will have less interference than
RI. Cleaning agent concentration correlations can be made
by analyzing the changes in acoustical signature of the
generated wave. The temperature coefficient of sound
velocity in water is non-linear, which is reflected in the nonlinearity of sound curves of water-soluble substances. The
temperature coefficient is approximately linear for other
liquids, which is reflected in their concentration curves. Due
to the signal processing requirements, manual analog
measurements are not practical.
Only within the last few years has this technology existed in
a form exact enough to measure complex liquids, like multiphase aqueous cleaning solutions.
Differential Density
Differential density (DD) is a technique that is applicable to
multi-phase cleaning agents because it is capable of
differentiating between unique liquid phases. Differential
density is sensitive to the amount of solvent present beyond
the saturation point of the aqueous phase, making it
fundamentally different from RI and SV in that it does not
rely on transmissive properties alone.
Formulators of multi-phase cleaning agents well understand
the saturation point of their materials and have developed
manual methods that correlate concentration to phase
separation. By nature of the partial solubility, this is also a
non-linear correlation.
For nearly 10 years, differential density has been
successfully used in automated concentration control and
monitoring systems around the world and has proven
accurate and reliable in controlling and monitoring complex
cleaning processes.
Method X:
Experimental method to monitor both the wash bath
concentration and soil loading introduced into the wash
bath. This paper does not talk to technology behind this
method. The method was included for comparison purposes
for accurate control of wash concentration.
Experimental #2
Phase 1 evaluated four measurement technologies versus
known concentrations of a popular multi-phase aqueous
cleaning agent. Using an analytical laboratory balance,
solutions of known concentration were carefully prepared
gravimetrically. To represent the typical operating range for
modern aqueous cleaners, five samples each of 13%, 15%
and 18% solutions were prepared for a total of fifteen
samples.
Statistical Discussion for Phase 1:
To determine which statistical methods should be used to
analyze the data gathered, the gravimetric samples were
evaluated for a random distribution. The data was then
tested for normality using the Anderson-Darling [1]
normality test at a 95% confidence interval. The P-value
was <0.05, confirming the data was not from a normal
distribution; therefore, non-parametric statistics were used
for the data set.
The next step was to test for equal variances in the
distribution of data between the gravimetrically known
concentrations and the instrument readings. Levene’s Test
[2] was selected due to the non-parametric data distribution
and confirmed that at a 95% confidence interval there was
equal variance between the methods and that the results
were not due to random sampling of the population. The
Mann-Whitney [3] test was then chosen to determine if
there was a statistical correlation between the instrument
readings and the known gravimetric concentrations.
Table 1: Statistical Summary for Each Method
Median (%)
Avg Rank
Z
RI
11.25
19.2
-2.89
SV
15.72
38.4
2.03
DD
14.30
32.7
0.29
Method X
14.94
32.7
0.57
Solvent Phase
Aqueous Phase
Figure 9: Image of Unmixed 15% Multi-Phase Solution.
Because the solubility of multi-phase cleaning agents in the
aqueous phase varies with temperature, each solution was
heated to 150°F / 65°C (upper limit of most cleaning
processes) before taking any measurements. This was also
important due to the temperature / density sensitivity of the
instruments discussed previously. If a portion of any layer
remained separated it could adversely affect the
measurements; therefore, the samples were well agitated to
ensure that all phases were thoroughly mixed. Each sensor
technology was evaluated by immersing the probe into, or
drawing a small sample from the heated reference solution.
Refractive index measurements were taken manually using
an analog hand-held refractometer due to observed
fluctuations in the readings from digital refractometers.
The Mann-Whitney test found that RI readings do not have
a statistically significant correlation with the known
concentrations of virgin chemistry. This was not unexpected
due to the intrinsic challenges of measuring the refractive
index of multi-phase cleaning agents. For the other three
methods, the Mann-Whitney test found that there was a
statistical correlation between their readings and known
concentrations at a 95% CI.
Figure 2 graphically summarizes this portion of the study by
showing the means and standard deviation intervals for each
of the four methods. The zero line represents the known
concentration. For the intervals that cross (or touch) zero,
there is no significant difference between the two means at a
95% confidence level.
5
Wave Solder
Rosin
Mann-Whitney tests were performed on each of the three
sensor technologies to determine if there is a significant
correlation with known concentrations when measuring the
array of 15 flux loaded solutions. Using the 95% confidence
level, the test result needed to be above 0.05 to show
significant correlation to the gravimetric known values.
Table 3: Mann-Whitney Test Results for Flux
Figure 10: Interval Plots of Reading Differences @ 95% CI
While RI has been successfully used for decades with
homogeneous aqueous cleaning agents, it is not an accurate
method for measuring virgin solutions of multi-phase type
cleaning agents. The other three methods all cross or touch
the zero line; therefore, they have statistically significant
correlations to known concentrations. Sonic velocity and
differential density are approximately equal in accuracy.
Sonic velocity tended to overvalue the concentration, while
differential density underestimated the concentration by a
similar amount. The experimental Method X was the most
accurate and had the tightest distribution. Future work is
planned to allow for parametric analysis.
Phase 2: The Effects of Dissolved Flux Residue
The second portion of this study examined how varying soil
load (dissolved flux) affected the concentration
measurements. Because RI failed to have a significant
correlation with virgin solvent splitting solutions, the
method was excluded from further evaluation in Phase 2.
Future work using similar methodology and statistical
analysis is planned for RI and other applicable methods on
homogeneous cleaning agents.
The concentration was fixed at 15% using the same multiphase cleaning agent as in Phase 1. Samples were
gravimetrically prepared in a consistent manner with Phase
1 using an analytical laboratory balance. Five diverse
soldering materials were selected based on known
acceptance in the industry.
Table 2: Flux Materials Studied
Flux ID
Class
Type
Notes
1
2
Wave Solder
No-Clean
HF
Paste
Water-Soluble
HF
3
Wave Solder
Water-Soluble
VOC-Free
4
Paste
No-Clean
HF
Test Value
Result
Sonic Velocity
0.0014
Not Significant
Diff. Density
0.0000
Not Significant
Method X
0.3615
Significant
Only experimental Method X demonstrated significance.
This was a surprising result after years of successful field
experience with the Differential Density sensor in process
control applications.
While no technique is completely immune to the effects of
soil loading, this small sampling suggests that large
discrepancies in measurement accuracy are possibly due to
the nature of a particular soil rather than the method itself.
Likewise, the negative effect of soil loading is not likely to
have the same error factor for every measurement method.
Upon closer inspection of the data, Flux #1 (halogen free,
no-clean, wave solder) created the most variation in all three
techniques. Table 4 shows the delta between measured
reading and the known control concentration. If soil had no
effect the difference would be 0%. The data is shown using
a heat-map to help visually depict the magnitude of the
measurement delta. Green is centered at 0%, red is used to
show the maximum and minimum (5%, -5%).
Table 4: Reading Differences in (%) for Flux #1, Average of
Combined Average of Other Fluxes.
Soil Load
1%
2%
3%
1%
2%
3%
Method X
-0.84
-1.56
Sonic Vel.
1.02
1.43
-2.21
3.36
Average of Other 4 Soils:
-0.54
0.46
0.08
0.35
-0.09
0.31
Diff. Density
1.64
4.46
4.80
1.19
1.63
1.60
Modern fluxes (liquid, pastes, tacky, etc.) are complex
formulations engineered to provide specific soldering
properties. While there is some commonality in the alloys
used, every solder manufacturer uses their own proprietary
blend of additives to achieve specific fluxing characteristics.
As a result, there are hundreds of distinct fluxes used in the
marketplace today. Experience has shown that these minor
differences lead to some materials being significantly more
challenging or easier to clean. No two flux residues are the
same. In much the same way, it is not surprising that some
materials interfere with the various concentration
measurement technologies more than others.
This study shows that no technology is truly immune to the
effects of soil loading and suggests that large discrepancies
in measurement accuracy are possibly due to the nature of a
particular soil rather than the method itself.
Monitoring Soil Load
Dissolved flux residues have long interfered with
concentration measurement technologies applied to both
homogeneous and multi-phase cleaning agents. Accurate
process control is difficult to achieve without knowing how
the dissolved soil load impacts the concentration reading.
Non-volatile Residue (NVR) is the most accepted method of
quantifying soil load today, however it must still be applied
on a site-by-site basis to understand how a specific reading
concentration reading may be affected based on the fluxes
used.
It is known that wash bath concentration cannot be
accurately measured by conductivity analysis due to the
ionic contribution from dissolved flux residues. This is true
for both homogeneous and multi-phase cleaning agent
types. Research is ongoing if it could be a contributing data
point for measuring soil load in real-time.
Inferences from Data Findings
Experiment #1
Experiment #1 finds that the dynamic nature of the inline
cleaning machine is far different from the batch cleaning
machine. The batch cleaning machine contains the wash
droplets within the chamber. There is less effect on
evaporative and carry out losses. As such a proportioning
device is suitable for controlling the batch cleaning machine
process.
The dynamic nature of the inline cleaning machine increases
the evacuation losses out the exhaust stack. Water, with a
higher vapor pressure than the wash chemistry ingredients,
is lost at a faster rate than the ingredients within the cleaning
agent. Additionally, loses from wetted parts tracking to the
next section of the machine can vary dependent on the
number of boards processed through the machine. Making
up the wash bath with the preset water to cleaning agent
ratio is not an accurate method for maintaining the wash
concentration.
Experimental #2: Phase 1
Multi-Phase electronic assembly aqueous cleaning agents
separate into a phase with some of the ingredients soluble in
water and other ingredients partially soluble in water. The
insoluble phase is made up of solvents, which by design
have partial solubility in water. The water phase carries the
solvent droplet to the soil. The solvent droplet has a full
concentration effect when encountering the soil. The full
solvent effect improves cleaning.
Multi-Phase cleaning agents are difficult to control. When
either the water or solvent phase is lost preferentially, an
adjustment must be made to account for the differential to
accurately control the wash bath. Four control technologies
were studied. Three of the four technologies, Sonic
Velocity, Differential Density and Method X were
successful in accounting for the water and solvent phases.
Those technologies provided insight into the proper levels
needed to replenish the water bath. Refractive Index was not
an accurate method for controlling Multi-Phase cleaning
agents.
Experimental #2: Phase 2
Flux residue cleaned from electronic hardware accumulates
within the wash bath. There is a point where the “critical
soil loading level” is reached. At this point, the wash bath is
dumped and a new charge is added to the machine. The
desire is for a method to monitor loading “real-time.”
One method provided statistical significance for real-time
soil loading data. Sonic Velocity and Differential Density
methods are suitable for monitoring and controlling wash
concentration. Only the experimental Method X
demonstrated statistical significance on a wide range of
samples.
Real Time Process Control
From the three technologies studied for controlling a MultiPhase cleaning agent, Differential Density has been
successfully integrated to control the water bath real time.
Over time, patterns and detailed correlations can be
measured to make the necessary adjustments to the wash
bath (Figure 11). The process control system constantly
monitors and adjusts the wash batch chemistry
concentration. The system accurately maintains the wash
bath within ±2%.

Chemistry and Water Consumed
The stored data can be used to monitor the health of
cleaning process. Should PCBs be washed outside their
specification limits, the system will alarm the operator.
Manufacturing Engineers can use the data to make
improvements, detect equipment issues and produce
hardware within predesigned specifications.
Figure 11: Process Control System Read Out
During operation a sample is taken and captured by the
control system. The sample is analyzed for both the water
and solvent phases. Temperature of the wash bath is
compensated for. The proper additions of water and
cleaning agent to the wash bath occurs real time during
make up. When charging the machine with a fresh bath, the
system makes those additions based on the present
concentration level desired.
Conclusion:
Manufacturers rely on precise control and monitoring of
wash bath concentration for an accurate process window
and confidence in their product quality. This research
investigated four distinct concentration measurement
technologies as they apply to non-homogeneous aqueous
cleaning agents (multi-phase):




Refractive Index
Sonic Velocity
Differential Density
Experimental Method X
Through non-parametric statistical analysis, three of the
technologies show a strong correlation with chemical
concentration. As suspected, and now statistically
demonstrated, refractive index is not a reliable measurement
of multi-phase cleaning agents. However, RI is a reliable
measurement tool for homogeneous cleaning agents.
From the methods that statistically control the water and
solvent phases, a real time process control system can be
designed to adjust and monitor process factors.
Figure 12: Process Control Schematic
Other real time information can be captured utilizing
analog-to-digital I/O encoder interface and a barcode reader.
Temperature probes, pressure transducers, pressure
switches, resistivity meter and encoders enable process
tracking over time. The system monitors and stores all
process data in a Microsoft SQL database including:
 Date & Time
 PCB - CCA number
 PCB Serial number
 Wash & Rinse Tank Temperatures
 Wash & Rinse Spray Pressures
 Conveyor Speed
 Rinse Bath Resistivity
 Exhaust Stack Pressure
 Chemistry Concentration
 Wash Bath Life
Future Research:
This study provides new insight into commercially available
and developing technologies for measuring the
concentration of multi-phase aqueous cleaning agents. The
authors plan to:
1.) Perform a similar statistical DoE on homogeneous
cleaning agents, where refractive index is the most
commonly used technique.
2.) Expand the data set of this study so that parametric
statistical tests can be applied.
3.) Study the potential for secondary bath
measurements to provide information on real-time
soil loading.
Acknowledgements:
Kyzen would like to thank the following companies for
providing flux and paste samples on a regular basis, which
enable this soil study and others like it:
Aim, Almit, Alpha (Alent), Ametech, Balver Zinn, Cobar,
EFD, Elsold, Felder, Florida Cirtech, Heraeus, Indium,
Interflux, Inventec, Kester, Koki, Multicore, Nihon
Superior, Qualitek, Senju, Shenmao, Stannol, and Tamura.
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