Authors: Marilyn & Robert Hart
A steel mill quality problem arose in the manufacture
of cold-rolled steel for use in applications
such as automobile hoods. In order to form a
long continuous band, several hot-rolled coils
were welded together end-to-end. The long continuous
band then included the welds that were made
to join the original coils together. Unfortunately,
many of these welds were failing under tension,
causing damage as well as extreme danger as
these coils then flailed about.
In order to discover why these coils were failing
at the welds, a functional test was performed
at the weld station. After removing the long
ridge of previously molten metal, the weld was
removed in a 12" strip of steel from the
full width of the coil. This was done four times
each 8-hour shift. (In the steel mill an 8-hour
shift is called a "turn".) A one-inch
diameter tool steel ball was pressed down into
the test piece until a "bulge" a half-inch
high was raised on the opposite side. A "failed"
weld bulge was one where a crack appeared with
some portion of that crack running parallel
to the direction of the weld. The rationale
for this definition was that such a crack implied
that the weld had less ductility than the parent
metal.
Every two hours a completed weld would be tested
using six bulges equally spaced along the weld.
Starting at the north edge of the coil, these
bulges would be numbered 1 through 6 with odd
numbered bulges toward the top of the sheet
and even toward the bottom. This was done in
case there turned out to be a preference for
the top or the bottom of the sheet in the welding
process.
After establishing standard procedures for
the weld and for the bulge test, we were ready
to consider the various methods of subgrouping
that could be used to study the test results.
We needed to improve the weld process so welds
would uniformly have sufficient ductility to
prevent fracture. To do this we needed to look
at the data in time-order but first we wanted
to address the potential sources of variation
in weld quality, based upon expert knowledge
of the process. We would then subgroup the same
failure data in many different ways for repeated
p-chart analyses. From a long list of possibilities,
the following subgrouping methods were selected
as easiest to apply.
If there had been, say, 73 bulges failed, the
fraction defective for the week would have been
p = # failed/ # inspected = 73/432 = 0.17 or
17% defective.
Note that the 17% defective was an intrinsic
property of the week regardless of the subgrouping
method. We chose to use 2-sigma limits when
using rational subgroups because of the small
number of subgroups. After we had obtained the
data on bulge test failures for the first week,
we could make a different p-chart for each proposed
method of subgrouping. Week after week, we could
compare these charts seeking evidence of "assignable"
causes of excessive variability—points
outside of the control limits, particularly
those that would repeat for more than one week.
Control charts for several potential sources
of uncontrolled variation showed no out-of-control
points. Our first success in identifying an
"assignable cause" for uncontrolled
variability in weld quality came when we subgrouped
the data by "crew", as seen in Figure
1.
Figure 1. p Chart Subgrouped by Crew, 2-Sigma
Limits
The first week showed "Crew C" to
be above the upper 2-sigma control limit, indicating
an assignable cause of variation. This was reinforced
when it happened two weeks in a row. Crew C
was doing something different from Crews A and
B. We had identified a significant source of
excessive variability, which had to be removed.
It took several weeks of talking with the crews
before the Crew C problem disappeared. Crew
C operators had finally started using the specified
weld parameters. We had eliminated a major source
of variability in the weld quality and we saw
a decrease in the number of actual weld fractures
reported in the cold rolling division.
Subgrouping the data by bulge position along
the weld (positions 1 through 6) showed ample
evidence of lack of control, but gave little
indication of the root cause. The problem was
solved by regrouping the data so that positions
1, 2 and 3 (the north half of the coil) formed
one single subgroup, and positions 4, 5 and
6 (the south half of the coil) formed another.
As seen by example in Figure 2, week after week
we found the north side to have "too many"
failures and the south side to have "too
few" to be explained by chance. We asked
maintenance personnel to look for an out-of-square
condition in the welding equipment. We were
looking for something which would allow the
two pieces to repeatedly come together cocked
toward the same side, rather than butting up
squarely, one to another.
Figure 2. p Chart Subgrouped by North vs. South,
2-Sigma Limits
After considerable effort, it was found that
one corner of the leading coil tail, a corner
that should have been firmly anchored, was slipping
under load when the head end of the trailing
coil was forced against it. Repair procedures
to assure that the anchored coil tail was securely
fastened in place corrected the problem, but
the most impressive result was the impact of
the experience upon the maintenance crew. They
were suddenly completely convinced of the power
of the control chart—it had accurately
pinpointed a serious problem that they otherwise
would not have discovered.
It was now possible to detect that there were
significantly more failures near the center
of the weld than at the two extremities. By
then, maintenance personnel were true believers
in the control charts and they were able to
quickly find and correct the problem. Following
engineering theory, the dies for trimming the
coil ends had been designed with a slight bow,
but the problem vanished as soon as a simpler
square cut was used. Engineering must "listen
to the process", rather than just doing
it "by the book."
By this time, the cold-rolling mill quality
problem had been largely corrected, but we continued
to study the weld bulge test results. Now we
looked at the data by order-of-production, setting
3-sigma limits based upon the results of the
most recent two weeks. We quickly discovered
that all of the points above the upper control
limit came from only eight out of the total
of over two hundred grades of steel. These eight
grades were subsequently identified by metallurgical
consultants as "hard-to-weld" grades
that inherently suffered from low ductility
after welding and new weld parameters were established
for this family of steels.
The bottom line was clear: Problem-solving
through the use of control charts allowed us
to improve weld quality until weld failures
in manufacturing were completely eliminated.
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