NUCLEAR FUEL ASSEMBLY SPACER GRID FABRICATION
DSES-6620 SIMULATION MODELING AND ANALYSIS
Professor E. Gutierrez-Miravete
BRIAN WILLIAMSON
Fall 2000
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TABLE OF CONTENTS
Page
Abstract/Executive Summary
3
1. INTRODUCTION
- PRODUCT/PROCESS BACKGROUND
4
4
4
4
4
-
SCOPE
OBJECTIVES
REQUIREMENTS
2. MODEL
-
5
5
5
MODEL ELEMENTS
ASSUMPTIONS AND SIMPLIFICATIONS
3. INPUT DATA
6
4. VERIFICATION AND VALIDATION
8
5. EXPERIMENTAL RUNS AND RESULTS
9
6.
12
OUTPUT ANALYSIS
7. CONCLUSIONS
12
REFERENCES
13
FIGURE 1
14
APPENDICES
A Text File of Model for Existing Process
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ABSTRACT/EXECUTIVE SUMMARY
The support structure for a nuclear fuel assembly is comprised of three major components: spacer grid assemblies, guide
tubes, and a lower end fitting. This project models and analyzes part of the spacer grid assembly fabrication process. The
spacer grid assemblies are fabricated from strips of zircaloy material which are stamped from coil material. The zircaloy
strips are manually pre-assembled, egg-crate fashion, into a “preassembly”. These preassemblies are then manually
assembled into a welding fixture, and are laser welded on one of two automated laser welding systems. The welded grids
are manually removed from the welding fixture, and undergo several secondary manual operations (grinding/blending of
welds, bending of tabs, cleaning). The grids are then inspected visually and dimensionally at an inspection station, and then
are inspected on a Coordinate Measuring Machine (CMM). A flow chart of the spacer grid fabrication process is given in
Figure 1.
ProModel© was successfully used to simulate the existing nuclear fuel assembly spacer grid fabrication process. The verified
and validated model was used to conduct a performance analysis of the existing process. The performance metrics
considered were throughput (welded and completed), cycle time and time in operation of the spacer grid assemblies,
work-in-process of the preassemblies and spacer grid assemblies, and the utilization of the automated laser welding
machines and operators. A comparison analysis of several proposed changes to the process, as compared to the existing
process, was conducted. The changes considered in the comparison analysis were production scheduling (the number of
operators assigned and the number of shifts worked), equipment availability (a higher equipment availability factor for the
laser welders), process improvement (a reduced laser welding cycle time achieved by process improvements), and
production control (using a “pull” system versus the current “push” system).
A change in production scheduling from utilizing 1.5 PreAssembly shifts per day to 2 shifts per day, and scheduling two
inspectors per shift instead of one inspector per shift produced a 32% increase in welded grid throughput and an 18%
increase in completed grid throughput, compared to the existing process. This scheduling strategy also produced the
smallest increases in PreAssembly and Grid WIP, and cycle time (7.10, 1.42 and 1.71 X respectively) and the largest
increases in laser and operator utilization (1.31 and 1.32 X respectively) compared to the existing process.
The scheduling runs showed that the laser welders were idle a percentage of the time, and that inspection was a bottleneck in
the existing process. To eliminate theses two influences when evaluating equipment availability, process improvements,
and production control strategy, the scheduling strategy described above (Run 4) was used as the baseline in place of the
existing process.
Elimination of laser downtime provides a 12% increase in welded grid throughput and a 5% increase in completed grid
throughput with respect to Run No. 4. when a third PreAssembly shift is added per day. Otherwise, the resulting laser idle
time results in no improvement compared to Run No.4. These increases in throughput come at the expense of increased
WIP (Preassembly and Grid) and cycle time, and decreases in laser and operator utilization.
Improvement of the laser welding process, by improving machine cycle time and variation, produced an 11% increase in
welded grid throughput compared to Run No. 4, but only a 2 % increase in completed grid throughput. This was due to a
bottleneck created at the CMM machine, as determined by the size of the CMM queue. By adding a second CMM machine,
both welded and completed throughput increased (14% and 31%respectively) when the laser processing time was reduced
due to process improvement.
The use of a “pull” production control strategy (Just-In-Time) versus the existing “push” production strategy produced the
number of welded and completed grids (approximately 200) required by the downstream process , while reducing
PreAssembly WIP to 6% of Run No. 4, Grid WIP to 34% of Run No.4, and cycle time to 27% of Run No. 4.
Significant improvements in the existing spacer grid fabrication process can be achieved by changes to production
scheduling, improvements to equipment availability, process improvement, and production control strategies. The specific
changes to be made need to be evaluated by plant management. The results of these simulation model runs can be useful in
selecting the desired path to process improvement.
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1. INTRODUCTION
PRODUCT/PROCESS BACKGROUND
The support structure for a nuclear fuel assembly is comprised of three major components: spacer grid assemblies, guide
tubes, and a lower end fitting. This project models and analyzes part of the spacer grid assembly fabrication process. The
spacer grid assemblies are fabricated from strips of zircaloy material which are stamped from coil material. The zircaloy
strips are manually pre-assembled, egg-crate fashion, into a “preassembly”. These preassemblies are then manually
assembled into a welding fixture, and are laser welded on one of two automated laser welding systems. The welded grids
are manually removed from the welding fixture, and undergo several secondary manual operations (grinding/blending of
welds, bending of tabs, cleaning). The grids are then inspected visually and dimensionally at an inspection station, and then
are inspected on a Coordinate Measuring Machine (CMM). A flow chart of the spacer grid fabrication process is given in
Figure 1.
SCOPE
The scope of the simulation model includes Step 7 through Step 16 of the process. See Figure 1. The process being
modeled begins with the pre-assembly of spacer grid strips into a “pre-assembly” and ends with a completed grid. The
process includes fixturing of the pre-assemblies for laser welding, laser welding, removing the welded grid from the weld
fixture (or de-fixturing), performing the secondary operations of weld blending and tab bending, cleaning, visual and
dimensional inspection, and inspection on a Coordinate Measuring Machine (CMM).
OBJECTIVES
The objectives of the simulation are to conduct a PERFORMANCE ANALSYIS of the existing process and a
COMPARISON ANALYSIS of several proposed changes to the process compared to the existing process. The
PERFORMANCE ANALYSIS will focus on the metrics of throughput, cycle time and time in operation of the spacer grid
assemblies, work-in-process of the preassemblies and spacer grid assemblies, and the utilization of the automated laser
welding machines and operators. A COMPARISON ANALYSIS will evaluate the improvement possible in these
performance metrics when the following aspects of the process are changed:
- Production Scheduling – Evaluate the impact of the number of operators assigned and the number of shifts worked.
- Equipment Availability – Evaluate the impact of a higher equipment availability factor for the laser welders.
- Process Improvement
– Evaluate the impact of a reduced laser welding cycle time achieved by process
improvements.
- Production Control
– Evaluate the impact of using a “pull” system versus the current “push” system.
REQUIREMENTS
In order to build a model of the existing process and perform simulation runs, information on the existing process
had to be obtained. The physical layout of the equipment and process flow, or routings, were obtained from the
grid processing engineer. Information on scheduling and production control was gathered from interviews with
production supervisors and production control personnel. Data on processing times was collected and sampled for
a time period spanning five years. Throughput metrics for the existing process were also obtained.
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2. MODEL
The spacer grid fabrication process was modeled using the student version of the ProModel © software package (ProModel
Corporation).
MODEL ELEMENTS
Model elements used were locations, entities, processing and routing, arrivals, shifts, and variables. Each operation,
whether manual or machine, was modeled as a location. Queues were also included in the model between operations. No
resources were associated with locations since a dedicated operator is assigned to each operation. The one exception to this
is fixturing and defixturing, where one operator does both operations. In this case, a resource was included, and had to be
available for the operation to occur. Two entities were modeled, preassemblies (the egg-crate style assembly of zircaloy
strips) and grids (spacer grid assemblies). The preassemblies enter the system at the preassembly operation. Shifts were
defined and assigned to each operation and resource. Variables were used to track throughput of welded and completed
grids, preassembly and grid work-in-process, and cycle time. The standard ProModel© report was used to determine
utilization, and time in operation. The text file of the model is included as Attachment A. The following table summarizes
the model elements.
MODEL ELEMENT
ENTITIES
RESOURCES
LOCATIONS
VARIABLES
SHIFTS
DESCRIPTION
PreAssemblies, Grids
Operator 1, Operator 2
PreAssembly, Fixturing Queue, Fixturing 1, Fixturing 2,
Laser Queue 1, Laser Queue 2, Laser 1, Laser 2, Defixturing
1, Defixturing 2, Secondary Ops Queue, Secondary
Operations, Cleaning Queue, Cleaning, Inspection Queue,
Inpsector, CMM Queue, CMM
GridWIP, PreAssemblyWIP, Welded, Completed,
Cycle_Time
Grid.sft – 3 8-hour shifts per day with breaks
Gridsecond.sft – 2 8-hour shifts per day with breaks
Girdpreassbly.sft – 1.5 8-hour shifts per day
Gridpreassbly1,sft – 2 8-hour shifts per day
Gridfab.sft - 3 8-hour shifts per day without breaks
ASSUMPTIONS AND SIMPLIFICATIONS
The following assumptions and simplifications were made in building the model. These were obtained from the grid
process engineer and production supervisor.
1. It was decided to model one specific grid assembly type, which accounts for 75% of the yearly grid production. It was
further decided to model a production period where this same grid type is being run on both laser welders.
2. It was decided not to model individual zircaloy strips. There are thirty strips per grid assembly. These strips get
preassembled into the egg-crate style “preassemblies”. Instead, the entity “preassembly” was chosen to enter the system
at the “preassemble” operation, and to be processed at this operation for the time it takes to preassemble the thirty strips
into a preassembly.
3. It was assumed that there was an unlimited availability of zircaloy strip at the preassembly operation. This was modeled
as having the entity “preassembly” arrive at the “preassemble” operation at a rate (one every 3 minutes) that was
approximately four times as fast as the preassembly operation itself. This approach was effective in that there were
always failed arrivals.
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4. The automated laser welding systems were modeled with equipment downtimes, based on mean-time-between failures
(MTBF) and mean-time-to-repair (MTTR).
5. The secondary operations of “grid/blend welds” and “bend tabs” were combined into a single “secondary operations”.
These two operations are performed sequentially, at the same location and same time by the same operator. Thus, there
seemed to be no need to include them separately.
6. All operations are performed on one grid at a time, except for the cleaning operation. In cleaning, twenty grids are
processed in a batch, and the elapsed time is one hour. To simplify the model, i.e. eliminate temporary batching and
subsequent unbatching, it was decided to model the cleaning on a per grid basis, of one grid every three minutes.
7. It was desired to evaluate the performance metrics on a weekly basis. The factory is normally run for three shifts per day,
five days per week (Monday through Friday). Overtime on Saturday is scheduled when it is needed to meet the
scheduled weekly throughput. The model thus was set up to include shifts, and a five day week schedule. Saturday
overtime was not included.
8. Most operations were scheduled in the model using a shift file that involved three shifts per day, five days per week.
These shifts included breaks representing lunch and coffee breaks, and time at the beginning of each shift to log in, get
procedures and equipment ready, etc. Several operations were not given these shifts. Based on discussions with the
production supervisor on how work is scheduled, the “preassembly” operation was modeled using 1 ½ shifts per day, and
the “secondary operations” was scheduled using two shifts per day. All queue locations were modeled using 24 hour a
day shifts.
9. According to the production supervisor, all operations have one operator assigned per shift. Thus, all locations were
given one unit. The capacity of all locations is one, except for the queues. The laser queues were modeled with a
capacity of two, since the welding system has physical space for two grids to be waiting while one grid is welding. All
other queues were modeled with infinite capacity. All queues, including the laser queues, were set to zero length.
10. There are two of the automated laser welding systems, operated in parallel. These were modeled as separate locations,
to allow for potentially different processing times and equipment downtimes.
11. Process improvement was modeled by using a constant process time of 35 minutes for the laser welding operation, as
opposed to the normal distribution obtained from analysis of the existing process times.
12. The “pull” system of production control was modeled by limiting each queue to eight grids or two grids, except for the
laser queues which can physically hold only two grids. This limits the amount produced by an upstream operation to the
size of the queue in front of the downstream operation. In effect, the queues become kanbans of size equal to eight or
two.
3. INPUT DATA
Data on processing times for each operation and laser downtime was collected, analyzed, and used as inputs to the model.
The processing times from ten different contracts were used as input. The data for each contract is an average time for each
operation for the total number of grids produced on that contract, which is typically 400 – 600 grids. The ten contracts span
the time frame from 1995 through 2000. The processing time data is given in Table 1, along with calculated mean and
standard deviation values, using Microsoft Excel, assuming all the process times are normally distributed.
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TABLE 1
(Minutes per Operation)
Contract PreAssembly Fixture Weld Defixture
1
2
3
4
5
6
7
8
9
10
11.8
13.4
9.5
8.4
12.5
13.3
17.9
18.4
10
15
Mean
Stdev
13.0
3.4
0.3
1.0
1.6
2.2
2.8
2.9
9.0
9.0
45.6
52.2
37.3
40.3
43.9
42.8
42.7
48.2
35.0
36.0
0.5
1.2
1.5
1.8
4.3
5.0
8.0
13.0
3.6
3.4
42.4
5.5
4.4
4.3
Sec.
Ops
10.7
13.3
7.2
9.8
10.1
1.5
11.4
13.3
Clean
Inspect
VIEW
0.5
4.2
2.1
3.4
3.0
3.4
3.7
5.3
2.0
4.0
32.3
29.4
18.8
30.2
31.7
33.2
31.4
34.6
34.0
29.0
23.3
22.3
16.4
23.3
27.7
20.5
26.2
30.2
32.0
34.0
9.7
3.8
3.2
1.3
30.5
4.5
25.6
5.5
The above processing time data for two of the operations were entered into the Stat-Fit module of ProModel©, to determine
the goodness-of-fit of a normal distribution . Results are presented below, and confirm the applicability of a normal
distribution for processing times.
Preassembly (92% Fit)
Weld(99% Fit)
Laser downtime data was available for a time period of four months from earlier this year. Data was available in the form
of mean-time-between failures MTBF (calendar time from the start of one downtime to the start of the next downtime) and
mean-time-to-repair MTTR. The data indicated that laser # 1 was down 14% of the time, and laser # 2 was down 17% of
the time. Less than ten data points were available for laser # 2, which is less than the ten data points needed for the Stat-Fit
module of ProModel© to perform an Auto-Fit test. Therefore, the data from laser # 1 was used for both lasers in the model.
As seen below, an Erlang distribution was used for the MTBF and an Exponential distribution was used for MTTR.
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MTBF(99.9% Fit)
MTTR(93.8% Fit)
This combination of MTBF and MTTR produced an average laser downtime of approximately 9%, which is less than the
actual values of 14% and 17%. The MTTR was adjusted to 240 minutes, versus the 150 minutes from Auto-Fit, and this
combination produced an average of 15% downtime, which was consistent with the actual values.
4. VERIFICATION AND VALIDATION
The model for the existing process was built one location at a time. This allowed for easier troubleshooting and debugging.
Once the model was complete and running, deterministic (i.e. mean values only with no distributions) processing times were
input and the model was run with no laser downtime. The model throughput results compared favorably with hand
calculated results, i.e. the welded grid throughput from the model was 226 grids versus 224 hand calculated and the
completed throughput from the model was 207 grids versus 207 hand calculated. These results indicated that the model was
working as intended.
Throughput is the only performance metric which is currently tracked for the existing process. Cycle time and
work-in-process are not tracked. In order to gather data for model validation, throughput data was reviewed for a 10 week
time period from earlier this year. This data is presented below.
Throughput – Welded Grids
Throughput – Completed Grids
Mean
209
208
Standard Deviation
32
24
The process time and laser downtime distributions from Table 1 were input into the model, and the model was run to
simulate the existing process, in accordance with the assumptions stated above. It was determined that a two week warm-up
period was required to reach steady state. This was determined by looking at time series plots of laser contents The
simulation was run for one week, with a two week warm-up period. Ten replications were run.
The throughput results from the model are compared to the actual results from the existing process in the table below.
Model Throughput – Welded Grids
Actual Throughput – Welded Grids
Model Throughput – Completed Grids
Actual Throughput – Completed Grids
Mean
228
209
225
208
Standard Deviation
13
32
7
24
The paired-t confidence interval method was used to compare the model and actual results. The null hypotheses is that
there is no difference between the mean values of the model and actual results. For the welded grid throughput, the paired-t
confidence interval was +5.8 to –45. Since the confidence interval contains the value zero, the null hypotheses cannot be
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rejected, and the conclusion is that the two means come from the same population. For the completed grid throughput, the
confidence interval was –0.2 to –35. This just misses including the value zero. Based on the results of both welded and
completed grid throughput confidence intervals, it was concluded that the model statistically represents the actual (existing)
process. Since the model was determined to be valid for the throughput performance metrics, it is assumed that other model
performance metrics for which no existing process data exists are representative of the existing process.
Another validation technique used was that of direct observation of the existing process, and comparing it to the model.
The physical layout and routings were determined to be what was used in the model. Periodic sampling of WIP on the shop
floor was also performed and compared to the model predictions.
5. EXPERIMENTAL RUNS AND RESULTS
A total of 12 production runs were made, in 5 categories corresponding to the objectives, as follows:
Run No.
1
2,3,4
Category
Existing Process
Production Scheduling (No. of PreAssembly Shifts per day and
No of Inspectors per shift.
Equipment Availability (Laser Downtime)
Process Improvement (Laser Welding Processing Time and
Distribution)
Production Control (Pull vs. Push)
5A,5B,5C
6A,6B,6C
7A,7B
Each run consisted of a two week warm-up period to reach steady state followed by a one week simulation. Ten replications
were performed in each run. The model parameters that were varied between runs were:
(A) Number of PreAssembly Shifts per Day
(B) Number of Inspectors per Shift
(C) Laser Downtime
(D) Laser Welding Processing Time and Distribution
(E) Pull vs. Push Production Control Strategy
The following test matrix describes the parameters used in each run:
No. of
Inspectors
per Shift
Laser
Downtime
Laser Processing
Time and Distribution
Productio
n Control
Strategy
1
2
3
4
5A
5B
5C
6A
6B
6C
No. of
PreAssembly
Shifts per
Day
1.5
2
3
2
1.5
2
3
1.5
2
2
1
1
1
2
1
2
2
1
2
2
Yes
Yes
Yes
Yes
No
No
No
Yes
Yes
Yes
Normal (42.5,5.5)
Normal (42.5,5.5)
Normal (42.5,5.5)
Normal (42.5,5.5)
Normal (42.5,5.5)
Normal (42.5,5.5)
Normal (42.5,5.5)
Constant @ 35 min.
Constant @ 35 min.
Constant @ 35 min.
Push
Push
Push
Push
Push
Push
Push
Push
Push
Push
7A
7B
1.5
2
1
2
Yes
Yes
Normal (42.5,5.5)
Normal (42.5,5.5)
Pull
Pull
Run No./Parameter
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Other
2 CMM
machines
8 grids per queue
2 grids per queue
The results of each run, in terms of the performance metrics, are presented below in Table 2.
TABLE 2
Run
No.
1
2
3
4
5A
5B
5C
6A
6B
6C
7A
7B
M=
PreMean
Welded
Completed Assembly
S= Std.
Throughput Throughput WIP
Dev.
(Qty)
(Qty)
(Qty)
M
228
225
6.2
S
13
7
3.6
M
284
234
54
S
25
2.5
25
M
285
234
437
S
16
1.6
23
M
300
265
44
S
15
4
21
M
226
231
1.3
S
3.6
3.7
0.2
M
301
272
2.2
S
4.2
4
0.4
M
336
277
317
S
2.7
4.9
8.3
M
223
228
1.6
S
12.1
6.9
0.8
M
333
269
383
S
23.3
7.8
34.7
M
343
346
366
S
25.2
27.4
50.4
M
204
206
2.5
S
10.5
8.8
0.4
M
198
201
2.8
S
8.1
7.9
0.02
Grid
WIP
(Qty)
28.8
6.1
124
24
123
21
41
7
20.6
2.5
37.7
2.7
58
3.1
23.6
2.9
55.7
11.6
25.8
3.1
16.9
1.2
13.9
0.7
Processing
Time
(min)
133
0.5
133
0.6
133
0.5
133
0.7
133
0.6
133
0.5
133
0.9
125
0.6
125
0.45
125
0.48
133
0.6
133
0.8
Cycle
Time
(min)
1670
373
6008
185
10992
122
2853
810
1109
95
1277
48.5
8231
129
1249
132
9195
630
8151
896
1186
71
772
91
Value
Laser
Added Laser Idle
Oper.
Time
Util.
Time Util.
(%)
(%)
(%)
(%)
8
68
10.6
13.8
8
6
1.3
2.2
84
0.11
17.1
8.8
.3
2.3
1.2
85
0
17.3
6
0
1.5
4.7
89.4
0
18.2
6.3
0
1.6
12
67.5
32.5
13.7
1.5
1.5
0.7
10.4
90
10
17.8
1.6
1.6
0.9
1.6
100
0
20.0
0
0
0.7
10.0
54
28
13.5
4.9
6.5
1.5
1.4
81.5
0
19.5
7.5
0
2.0
1.5
83.5
0
20
8
0
2.0
11.2
62
20(1)
12.1
4.8
4.1
1.4
(2)
17.2
83
0
2.0
8
0
1.7
(1) Laser Blocked 1.3% of the time
(2) Laser Blocked 25% of the time
The results (mean values) are restated in Table 3 on a relative scale, compared to either Run No. 1 or Run No. 4.
TABLE 3
PreWelded
Completed Assembly
Run
Throughput Throughput WIP
No.
(Qty)
(Qty)
(Qty)
1
1.00
1.00
1.00
2 vs 1
1.25
1.04
8.70
3 vs 1
1.25
1.04
70.50
4 vs 1
1.32
1.18
7.10
5A vs 1
1.00
1.03
0.20
5B vs 4
1.00
1.03
0.05
5C vs 4
1.12
1.05
7.20
6A vs 1
1.00
1.01
0.26
6B vs 4
1.11
1.02
8.70
6C vs 4
1.14
1.31
8.32
7A vs1
0.89
0.92
0.40
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Grid
WIP
(Qty)
1.00
4.31
4.27
1.42
0.72
0.92
1.41
0.82
1.36
0.63
0.59
Processing
Time
(min)
1.00
1.00
1.00
1.00
1.00
1.00
1.00
0.94
0.94
0.94
1.00
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Cycle
Time
(min)
1.00
3.60
6.58
1.71
0.66
0.45
2.89
0.75
3.22
2.86
0.71
Laser
Util.
(%)
1.00
1.24
1.25
1.31
1.00
1.00
1.12
0.79
0.91
0.93
0.91
Oper.
Util.
(%)
1.00
1.24
1.25
1.32
1.00
0.98
1.10
0.98
1.07
1.10
0.88
7B vs 4
0.87
0.89
0.06
0.34
1.00
0.27
0.93
0.11
Run No. 1 is a baseline for the existing process. The throughput values were shown to be representative of the existing
process (see Verification and Validation). One unexpected result was that the laser welders were idle approximately 11% of
the time. Further investigation using the model determined that the lasers were idle because the preassembly operation
could not keep up with the fixturing and laser operations.
Runs 2 through 4 investigated the effects of production scheduling on the performance metrics. Specifically, the number of
PreAssembly shifts per day and the number of inspectors assigned per shift were varied and the results compared to the
existing process (Run No. 1). Increasing the number of PreAssembly shifts per day from 1.5 to 2 (Run No. 2) reduced the
laser idle time to essentially zero (.11%), while increasing welded grid throughput by 25%. The completed grid throughput,
however, increased by only 4%. Increasing the number of PreAssembly shifts per day from 2 to 3 produced no further
increase in either welded or completed grid throughput, and only served to increase the PreAssembly work-in-process (WIP)
significantly (70X compared to the existing process). Run No. 4 added a second inspector per shift while keeping 2
PreAssembly shifts per day to avoid an unnecessary increase in PreAssembly WIP. Run No. 4 produced a 32% increase in
welded grid throughput and an 18% increase in completed grid throughput. Run No. 4 also produced the smallest increases
in PreAssembly and Grid WIP, and cycle time (7.10, 1.42 and 1.71 X respectively) and the largest increases in laser and
operator utilization (1.31 and 1.32 X respectively) compared to the existing process. Run No. 4 represents the improvement
expected in the existing process by varying the number of PreAssembly shifts per day and the number of inspectors assigned
per shift.
Runs 5A, 5B, and 5C investigated the impact of increased laser availability. This was done by disabling the laser downtime
in the model. This simulates a 100% availability factor for the lasers and would represent the upper bound on what could
be expected from a higher laser availability factor. Run 5A simulated the existing process with no laser downtime. This
produced essentially the same results as Run No. 1, because the 1.5 PreAssembly shifts per day caused a 32.5% laser idle
time. The number of PreAssembly shifts per day was increased to 2 (Run No. 5B) and 3(Run No. 5C). Two PreAssembly
shifts (Run No. 5B) produced 10% laser idle time and resulted in performance measures similar to Run No. 4, which
represents the improvement expected from scheduling considerations. To get additional performance increases from
increased laser availability, a third PreAssembly shift is required (Run No. 5C). This reduces laser idle time to zero, and
provides a 12% increase in welded grid throughput (336 vs. 300) and a 5% increase in completed grid throughput (277 vs
265) with respect to Run No. 4. These increases in throughput come at the expense of increased WIP (Preassembly and
Grid) and cycle time, and decreases in laser and operator utilization.
Runs 6A, 6B, and 6C investigated the effects of an improvement to the laser welding process. Problems inherent to the
process slow the machine cycle time down, and add variability to the laser processing time. By improving the process, the
laser processing time can be reduced, and variation eliminated, i.e. having a repeatable, machine controlled cycle time. The
improvement in the laser welding process was modeled using a constant processing time of 35 minutes, compared to the
normal distribution of laser processing times obtained from production data. As with the production scheduling and laser
downtime cases, a comparison to the existing process produced no benefits because of the laser idle time caused by the
number of PreAssembly shifts (Run No. 6A). Run No. 6B, using two PreAssembly shifts, produced an 11% increase in
welded grid throughput compared to Run No. 4 (333 vs. 300), but only a 2 % increase in completed grid throughput (269 vs.
265). This was due to a bottleneck created at the CMM machine, as determined by the size of the CMM queue. By adding
a second CMM machine, both welded and completed throughput increased (343 and 346 respectively) when the laser
processing time was reduced due to process improvement.
The effects of a “pull” production control strategy (Just-In-Time) versus the existing “push” production strategy was
evaluated using Runs 7A and 7B. The assumption used in setting up the “pull” system was that the downstream process from
the grid fabrication process requires 200 grids per week, to support the fabrication of 20 cages, with each cage requiring 10
grids. Run No. 7A was modeled by limiting the queues between all process to 8 grids (except for the laser queues which are
physically limited to 2 grids). Again, Run 7A, using 1.5 PreAsseembly shifts per day as in the existing process, produced a
large laser idle time (20%). Run 7B, with 2 PreAsseembly shifts per day and 2 inspectors, as in Run No. 4, produced no
laser idle time. The results from Run No. 7B produced the required number of welded and completed grids (198 and 201
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respectively), while reducing PreAssembly WIP to 6% of Run No. 4, Grid WIP to 34% of Run No.4, and cycle time to 27%
of Run No. 4.
6.
OUTPUT ANALYSIS
Three runs were selected for a statistical analysis of the output. The Welch Confidence Interval method was used to
determine whether there were statistically significant differences in the mean welded grid throughput of Run No. 4 compared
to Run No. 1, Run No. 5C compared to Run No. 4, and Run No. 6C compared to Run No. 4. These runs were chosen since
they represent the improvement attained by scheduling (Run No. 4 vs. Run No.1), equipment availability (Run No.5C vs
Run No. 4), and process improvement (Run No. 6C vs. Run No.4).
RUN 4 VS RUN 1
MEAN 1
=
MEAN 2
=
228
300
RUN 5C VS RUN 4
RUN 6C VS RUN 4
MEAN 1
=
MEAN 2
=
MEAN 1
=
MEAN 2
=
300
336
300
343
s1 =
s2 =
n1 =
n2 =
df =
13
15
10
10
17.64357
s1 =
s2 =
n1 =
n2 =
df =
15
2.7
10
10
9.582588
s1 =
s2 =
n1 =
n2 =
df =
15
25.2
10
10
14.66624
t =
hw =
2.106
13.21924
t =
hw =
2.106
10.15018
t =
hw =
2.106
19.53069
CONF.
INT. =
MIN 58.78076
MAX 85.21924
CONF.
INT. =
MIN 25.84982
MAX 46.15018
CONF.
INT. =
MIN 23.46931
MAX 62.53069
In all three cases, the 95% confidence limit, as determined using the student-t distribution, did not contain the value zero.
Therefore, there is a statistically significant difference, at a 95% confidence level, in the means in these three comparisons.
This indicates that there is real difference in the performance of these three systems.
7.
CONCLUSIONS
ProModel© was successfully used to simulate the existing nuclear fuel assembly spacer grid fabrication process. The verified
and validated model was used to conduct a performance analysis of the existing process. The performance metrics
considered were throughput (welded and completed), cycle time and time in operation of the spacer grid assemblies,
work-in-process of the preassemblies and spacer grid assemblies, and the utilization of the automated laser welding
machines and operators. A comparison analysis of several proposed changes to the process, as compared to the existing
process, was conducted. The changes considered in the comparison analysis were production scheduling (the number of
operators assigned and the number of shifts worked), equipment availability (a higher equipment availability factor for the
laser welders), process improvement (a reduced laser welding cycle time achieved by process improvements), and
production control (using a “pull” system versus the current “push” system).
A change in production scheduling from utilizing 1.5 PreAssembly shifts per day to 2 shifts per day, and scheduling two
inspectors per shift instead of one inspector per shift produced a 32% increase in welded grid throughput and an 18%
increase in completed grid throughput, compared to the existing process. This scheduling strategy also produced the
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smallest increases in PreAssembly and Grid WIP, and cycle time (7.10, 1.42 and 1.71 X respectively) and the largest
increases in laser and operator utilization (1.31 and 1.32 X respectively) compared to the existing process.
The scheduling runs showed that the laser welders were idle a percentage of the time, and that inspection was a bottleneck in
the existing process. To eliminate theses two influences when evaluating equipment availability, process improvements,
and production control strategy, Run 4 was used as the baseline.
Elimination of laser downtime provides a 12% increase in welded grid throughput and a 5% increase in completed grid
throughput with respect to Run No. 4, when a third PreAssembly shift is added per day. Otherwise, the resulting laser idle
time results in no improvement compared to Run No.4. These increases in throughput come at the expense of increased
WIP (Preassembly and Grid) and cycle time, and decreases in laser and operator utilization.
Improvement of the laser welding process, by improving machine cycle time and variation, produced an 11% increase in
welded grid throughput compared to Run No. 4, but only a 2 % increase in completed grid throughput. This was due to a
bottleneck created at the CMM machine, as determined by the size of the CMM queue. By adding a second CMM machine,
both welded and completed throughput increased (14% and 31%respectively) when the laser processing time was reduced
due to process improvement.
The use of a “pull” production control strategy (Just-In-Time) versus the existing “push” production strategy produced the
number of welded and completed grids (approximately 200) required by the downstream process , while reducing
PreAssembly WIP to 6% of Run No. 4, Grid WIP to 34% of Run No.4, and cycle time to 27% of Run No. 4.
Significant improvements in the existing spacer grid fabrication process can be achieved by changes to production
scheduling, improvements to equipment availability, process improvement, and production control strategies. The specific
changes to be made need to be evaluated by plant management. The results of these simulation model runs can be useful in
selecting the desired path to process improvement.
REFERENCES
1. “Simulation Using ProModel”, Harrell, Ghosh, Bowden, 3rd Ed., 2000
2. “Simulation Modeling and Analysis” Law and Kelton, 3rd Ed., 2000
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FIGURE 1
1
Receive
Stampings
3
Clean and
Pickle
Stampings
2
Store
Stampings
6
Procure Cleaned
Stampings from Storage
7
Pre-Assemble
Stampings into
PreAssembly
9
Fixture
PreAssemblies
9
Fixture
10
Laser
Weld
10
Laser
Weld
4
Inspect and
Bag
Stampings
5
Store Cleaned
Stampings
8
Store
PreAssemblies
11
Remove Grid
from Fixture
12
Grind/Blend
Welds
12
Remove Grid
from Fixture
13
Bend Tabs
PreAssemblies
17
Store Completed
Grid
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16
CMM
Inspection
15
Bench
Inspection
Page 14 of 14
14
Clean
Grid
APPENDICES
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