Preliminary Investigation into the Effects of Friction, Work-piece
Temperature, Die Temperature, and Stroke Speed on Hot
Forging Die Life
By,
Thomas C. Grobaski, Dr. Bhavin Mehta*, Dr. Jay Gunasekera
Department of Mechanical Engineering
Ohio University, Athens, Ohio 45701
ABSTRACT
The goal of this research was to provide a preliminary step into developing a
complete forging die life model. The research involved analyzing the initial effects
of (1) friction, (2) work-piece temperature, (3) die temperature, and (4) forging
press stroke speed on effective die stresses, die surface temperatures, die/workpiece sliding velocities, die/work-piece contact pressures, and die surface
temperatures were examined. To obtain the results the forging process was
modeled (SolidEdge 3D Solid Modeling Software), simulated (MSC.Superforge
Software), and statistically setup and examined using two-level full factorial
design of experiments (Analyzed with Minitab & MS. Excel). The product
reviewed was a 10inch diameter differential ring gear forged at the American
Axle Manufacturing, North Tonawanda, New York forging plant. The ring gear is
used in the rear differentials for Ford and GM trucks.
Forging Data and Outputs
The multiple metal failure prediction models were analyzed to identify the
variables required to estimate metal failure. The parameters required to estimate
die failure must be qualified and quantified to produce a valuable working
prediction model. By breaking each failure model down and regrouping the
variables can be organized into three main categories.
1. Material/Process Data
2. Process Outputs
3. Empirical Constants
Material data is comprised of independent variables based on the different
tooling parts including the dies, work-piece, lubricant, press and environment. For
each of the separate tooling features there are the mechanical, chemical,
electrical, and thermal properties. Material data are the input parameters into a
forging operation.
The various material properties were obtained from the abundant sources
of previously tested materials included in the ASTM Metals and Materials
Handbooks, matweb.com, independent research laboratory testing (ORNL),
Metal Processing Journal, etc. These sources, and many others, provide
assumed accurate data for various materials that are forged, also the effects of
temperature, humidity, and other environmental factors that lead to altered
physical properties. For the failure models, the required material data is
displayed in Figure 1.1.
1.1
Table 1.1: Material/Process Data and Process Outputs
Forging Process Outputs
Material & Process Data
1. Die & Work-piece:
•Hardness
•Initial Temperature
•Ductility
•Ultimate Tensile Strength
•Modulus of Elasticity
•Tempering Curves
•Yield Strength
•Poisson’s Ratio
•Tempering Curves
•Surface Roughness
•Toughness
•Geometry
•Thermo-physical Properties
2. Surface Coatings:
•Physical Properties
3. Environment:
•Ambient Temperature
•Environmental Humidity
•Environmental Oxygen Content
•Preheat of Dies & Work-piece
4. Lubricant:
•Thermo-physical Properties
•Friction Factor
•Coverage and Thickness
5. Forging Press
•Press Type
•Stroke Velocity Curve
•Stroke Acceleration Curve
•Press Rigidity
•Press Stroke Length
•Press Repeatability & Accuracy
6. Miscellaneous
•Operator Quality
•Cooling Time: WP & Die
•Contact Time: Die, WP & Lube
1. Material Sliding:
•Distances
•Velocities
2. Heat Transferred:
•Conduction (WP to Die)
•Convection (WP & Die to Env.)
•Radiation (WP to Die & Env.)
3. Final Temperature of:
•Die
•Work-piece
•Environment
4. Forging Load Force
5. Contact Pressures at:
•Die/Work-piece Interfaces
6. Effective Stress:
•Ranges
•Amplitude
7. Total Strain Range From:
•Elastic Strain
•Plastic Strain
•Thermal Strain
8. Strain Rate
9. Strain Hardening of:
•Die Surfaces
•Work-piece
Process outputs are the results from forging process (stress, strain, final
surface temperature, etc.). They are dependent upon the material/process data
(load force, initial temperature, etc.), also known as independent variables [Table
1.1].
Once the effects of the independent variables upon the process outputs
are known, the number of cycles till failure of the dies, can be estimated using
current metal failure theories (Wohler or Basquin’s Stress Life Approach, Coffin &
Manson’s Strain Life Approach, Archard’s or Bayer’s Wear Model, Fracture
Mechanics, etc.).
The die failure models rely heavily upon the accuracy of empirically
determined constants. The constants and coefficients in Figure 1.2, are from the
failure models previously described.
Table 1.2: Empirically Determined Model Constants
Empirical Constants
1. Archard Model's:
•Adhesive Wear Constant
•Abrasive Wear Constant
2. Felder & Montagut’s:
•Hardness Coefficient
3. Stahlberg & Hallstrom’s:
•Friction Factor
4. Coffin-Manson’s:
•Cyclic Fatigue Stress Factor
•Cyclic Fatigue Strain Factor
•Cyclic Stress Coefficients
•Cyclic Strain Coefficients
5. Local Energy Approach’s:
•Coefficient of Fatigue
6. Fracture Mechanic Model's:
•Geometry Coefficient
7. Thermal Mechanical Fatigue Model’s
•Material Constants
After obtaining the material/process data and the process outputs and
empirically modeled constants are determined, the cycles till die failure can be
estimated. Using a multi-factor, two level full factorial design, the effects of the
independent variables on the die cycles till failure are estimated. From this
setup, a formula equivalent to the Taylor’s Tool Life [Appendix F] will be
constructed. This equation will be employed to quickly, efficiently, and accurately
determine the forging die life longevity.
1.2 Design of Experiments
The purpose of this research is to determine the effects caused by varying
the forging process input parameters on the process outputs that are required to
determine die life. This preliminary step will show how the model will be setup
and run.
1.2.1 Factor Design Levels
The design of the experiment is a full factorial 4 factor, high-low 2 level
test. The analysis was limited to four factors initially, because of time and
monetary constraints, therefore only requiring 24 =16 simulations. Once research
funds are secured, the number of factors tested can be expanded in the future.
The four factors were selected by deeming that which was believed to affect the
forging outputs the greatest. The factors chosen were:
1. Forging Press Stroke Speed
2. Work-piece Temperature
3. Die Temperature
4. Friction Factor at Die-Work-piece Interface
The four factors were then given high/low values based upon their
average industry values provided.
1.2.1.1
Stroke Speed High-Low Level
For stroke speed, the upper bound velocity the press was capable of was
47 strokes per minute. To find the lower bound for stroke velocity (36spm), the
difference between the upper bound (47spm) and average stroke speed (41spm)
and subtracting it from the average.
1.2.1.2
Work-piece Temperature High-Low Level
The Work-piece upper bound, or high-level, temperature was set at the
maximum possible forging temperature for AISI 4320 Steel (2300˚F). The lower
bound was set at approximately 65% of the melting temperature (2600˚F
*0.65≈1700˚F, the simplistic definition of hot forging) of the AISI 4320 Steel.
1.2.1.3
Die Temperature High-Low Level
The high/low factor levels for the die temperature were obtained from AAM
as die temperature after preheating prior to the first forging cycle (300˚F Flow
level), and the maximum temperature obtained during forging shift (800˚F high
level).
1.2.1.4
Friction Factor High-Low Level
The friction factor levels were determined from the ASM Handbook V.11:
Failure Prevention, Protection, and Analysis. The low level for friction (0.2) was
empirically determined from the test of lubricated mild steel sliding on hardened
steel. The high level for friction (0.7) was from un-lubricated mild steel sliding on
hardened steel.
Table 1.3: D.O.E. Uncoded and Coded Levels
D) Avg. Stroke
Speed (in/s)
C) Temp WP {°F}
B) Temp Die {°F}
A) Friction (NA)
Run #
Stroke Speed (D)
WP Temp. (C)
Die Temp. (B)
Friction (A)
Yates Code
(ABCD)
Coded DOE
Run #
Uncoded DOE
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
8.4
8.4
8.4
8.4
8.4
8.4
8.4
8.4
11
11
11
11
11
11
11
11
1700
1700
1700
1700
2300
2300
2300
2300
1700
1700
1700
1700
2300
2300
2300
2300
300
300
800
800
300
300
800
800
300
300
800
800
300
300
800
800
0.2
0.7
0.2
0.7
0.2
0.7
0.2
0.7
0.2
0.7
0.2
0.7
0.2
0.7
0.2
0.7
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
-1
-1
-1
-1
-1
-1
-1
-1
1
1
1
1
1
1
1
1
-1
-1
-1
-1
1
1
1
1
-1
-1
-1
-1
1
1
1
1
-1
-1
1
1
-1
-1
1
1
-1
-1
1
1
-1
-1
1
1
-1
1
-1
1
-1
1
-1
1
-1
1
-1
1
-1
1
-1
1
1
A
B
AB
C
AC
BC
ABC
D
AD
BD
ABD
CD
ACD
BCD
ABCD
Steps were taken to determine the forging press outputs to be studied.
First, the multiple forging die failure models were reviewed to find the necessary
process outputs required to predict die life. Second, the necessary process
outputs were cross-referenced with the outputs that are obtained from testing
and those obtained by simulation. Since there is no forging press located at Ohio
University, alternative methods were used to initiate the research and perform
the experimentation. Therefore, the results of this simulation were compared with
actual results from AAM manufacturing.
To calculate the process outputs, the MSC.Superforge finite element
modeling software used specifically for simulating the forging process was
employed. The results of the multiple MSC.Superforge simulations were verified
by visiting the American Axle plant in North Tonawanda, where the actual RG3607 ring gear being modeled and simulated are originally formed. The AAM
specs for RG-2607 are provided in Figure 1.3. The forging simulations were
performed at the CAD/CAE laboratory at Ohio University, and the validity of the
results were verified by comparing them with industry results.
American Axle Supplied Specifications:
The process parameters for the Differential Ring Gear Process
Parameters are shown in Table 1.3.
1.2.2
Table 1.4: Differential Ring Gear Process Parameters
Material Data:
Work-piece = AISI 4320 Steel
Die Material = H-13 Tool Steel
Lubricant = AML-145 (Synthetic Graphite)
Lubricant Temperature = Room Temperature
Ambient Temperature = 70-75F
Surface Coating = None
Surface Treatment = Nitriding (.008-.012dp)
Die Surface Hardness = 45-47 Rockwell
Forging Load = 1st Stage = <450 tons
2nd Stage = 450-600 tons
3rd Stage = 1950-2200 tons
Press Type = Diesel Mechanical Yoke
Press Stroke Speed = 45 Strokes Per Minute
Press Stroke Length = 14"
Work-piece Temperature Initially = 2300F
Work-piece Temperature Finally = 2200F
Die Surface Temperature Initially = 300F
Die Surface Temperature Mid-Shift = 600F
Height Reduction = 1st = 6.5" to 3.22"
2nd = 3.22" to 2.215"
3rd = 2.215" to 1.906"
*Also all die geometries were supplied via AutoCAD Drawings
Using the supplied parameters the mechanical, thermal, electrical, and
chemical properties of the die, work-piece, and lubricant were researched in the
ASTM Metals and Materials Handbook, along with Matweb.com, and material
supplier information [Appendix B]. To construct an accurate model of the forging
process in MSC.Superforge these factors needed to be as accurate as possible.
3D-Model Construction using SolidEdge
The dies for all three stages were drawn to scale using SolidEdge 3DSolid Modeling Software prior to actual simulation in MSC.Superforge. The AAM
supplied AutoCAD drawings were used to model the dies. A revolved protrusion
command of a 2D sketch from the supplied drawings provided the die models
(Figure 1.1). The solid models are displayed in Figure 1.2.
1.2.3
UPPER DIES
Figure 1.1: SolidEdge Sketch for 3D Model Lower Die
c) Blocker Stage
e) Finisher Stage
b) Buster Stage
d) Blocker Stage
f) Finisher Stage
LOWER DIES
a) Buster Stage
Figure 1.2a-e: SolidEdge Dies .par Files (Clam Shell View)
Post construction of the dies, the SolidEdge part files (.par) were saved as
Stereo Lithography files (.stl) to be imported into MSC.Superforge models.
1.2.4 MSC.Superforge Simulation Construction:
1.2.4.1
Simulation Models Setup
The dies and work-piece were imported as .stl files into the forging
software MSC.Superforge 2004, and were then meshed using MSC’s default
size. Later the parts are remeshed for optimization.
Figure 1.3: Upper Die Blocker Stage, Isometric Position
Figure 1.4: Work-piece, Post Buster Stage
Figure 1.5: Lower Die Blocker Stage, Isometric Position
The upper die and lower die were aligned with the work-piece [Figure 1.6].
Figure 1.6: Aligned Forging Process Setup:
By shortening the upper dies stroke length from the actual 14 inches to
3.58 inches simulation time is reduced. To do this the Positioner function in
Superforge moved the upper die down until it was touching the top of the workpiece which was resting a top the bottom die as shown in Figure 1.6. The
properties and bounds of the forging process are added to insure the models
accuracy:
Figure 1.7: Positioned (Front View, Transparent Dies)
1.2.4.2
Modeled Material Properties
The materials are from the MSC.Superforge Materials Library and the
mechanical and thermal properties were compared with properties found in the
ASTM Handbook and matweb.com. Any discrepancies were changed to match
the ASTM Handbook values. The material for the upper and lower dies was H13 tool steel (900-1200˚C) ion nitrided and for the work-piece was AISI 4337
steel which had been altered to meet the properties of the AISI 4320 steel used
at AAM. The mechanical, chemical, and thermal properties for both dies and
work-piece are in Appendix C.
1.2.4.3
Modeled Forging Press
The next step was to create the customized forging press with the
appropriate properties. Using the MSC.Superforge function to manually create a
modeled press. A mechanical scotch yoke press was selected and given the
14inch stroke length and the high/low stroke speeds chosen for the design of
experiments. The high/low velocity graph of the press are included in the
Appendix.
1.2.4.4
Modeled Friction at Interface
The friction factor added is a combination of the Coulumb Friction Model
(τ=µ*N, τ= Friction Force, µ = Friction Coefficient, and N = Forging Load) and
Plastic Shear Friction (τ=M*τYIELD M = Friction Factor, τYIELD= Flow Stress in
Shear). The numerical values for the friction factors were chosen from ASTM
Handbook values for steel on steel sliding with no lubricant (µ=0.7), this was the
assumed high, and the low was also attained from the ASTM values for,
lubricated steel on steel sliding (µ=0.2) and were incorporated in the high/low
experimental design.
1.2.4.5
Modeled Thermal Dynamic Setup
The last steps for the material setup pertained to the initial bounds for the
temperature and thermal properties of the work-piece and dies. The work-piece
and die initial temperature was set using two separate values from the DOE. The
thermal properties matched the thermal properties obtained from the 2002 ASTM
Handbook. The final additions were the ambient temperature of the environment,
and the time allotted between processes. The process is modeled by
incorporating the work-piece and die heat transfer to the surrounding
environment, which is dependent upon the time of exposure.
1.2.4.6
Modeled Forming Process Setup
With the material, press, thermal, and frictions properties specified, the
actual forging simulation specifics were set. Actual stroke length of the
simulation after positioning is entered for each process. The mesh size of the
dies and work-piece were set at 0.10 inch. Chosen by using a trial and error
approach, which was started at .25inch (1/2 the smallest geometry size) and
reduced to less than 0.01% change in maximum effective stress was observed.
The total forging stroke is divided into 10 analysis steps, and was designed as a
closed die forging operation with a flange. The forging process was modeled as
a finite element solution set [Appendix B] rather than a finite volume, to increase
accuracy.
The forging dies and work-piece are cylindrical in shape and geometry.
Therefore, a 2D axisymmetric slice was used to model the forging process
[Figure 1.8].
Figure 1.8: Axisymmetric 2D Model of Forging Process
Reducing the amount material modeled, lessons the number of nodes
analyzed, therefore accelerating computational time without weakening the
integrity of the analysis [Appendix].
Results
The simulation, of each stage of the three stages required to forge the
AAM ring gear, were simulated in MSC.Superforge. The process outputs are
then analyzed using Microsoft Excel and Minitab Release 14. The results of the
main, secondary, tertiary, and fourth order effects on the process outputs were
calculated. To save space and time, the effects on the upper die of Blocker Stage
(Stage B) of the ring gear forging process is explained in detail, and the
remaining two stages’ results are summarized in the Appendix.
2.0
MSC.Superforge Buster Stage Outputs
The results of the MSC.Superforge simulations for the Buster Stage are in
Table 2.1.
2.1
Run #
Yates
Table 2.1: Blocker Stage Process Outputs
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
1
A
B
AB
C
AC
BC
ABC
D
AD
BD
ABD
CD
ACD
BCD
ABCD
Z-Load
Force {lbf}
Upper
12300.0
12190.0
9939.0
13160.0
6406.0
7801.0
6376.0
5481.0
15790.0
10710.0
7307.0
16450.0
6441.0
5101.0
6407.0
9318.0
STAGE B) PROCESS OUTPUT RESULTS
Temp.
Net
Eff. Stress
Sliding
Final {°F} Energy {J}
{Psi}
Vel. {in/s}
Upper
Upper
Upper
Up Int
504.8
1.392E+05
6333.0
11.060
488.6
1.764E+05
3041.0
10.910
937.2
1.385E+05
3714.0
11.170
931.9
1.345E+05
6768.0
10.920
570.4
7.477E+04
2358.0
11.410
561.5
1.074E+05
2929.0
11.040
1004.6
7.473E+04
4256.0
11.290
1003.7
6.860E+04
3287.0
11.050
417.5
1.244E+05
6055.0
14.420
457.1
1.379E+05
7496.0
14.290
905.1
1.232E+05
2499.0
14.380
904.7
1.372E+05
7806.0
14.690
515.3
7.713E+04
4454.0
14.750
506.9
1.101E+05
1840.0
14.610
963.2
7.711E+04
2635.0
14.470
956.8
1.100E+05
4337.0
14.560
Cont. Press.
(Psi)
WP-Die Int.
2.203E+04
2.513E+04
2.312E+04
2.261E+04
1.263E+04
1.187E+04
1.246E+04
1.070E+04
2.264E+04
2.255E+04
1.786E+04
2.119E+04
1.435E+04
1.171E+04
1.369E+04
1.276E+04
These results were ripped from the results of simulations run in
MSC.Superforge. The numerical values are the maximum values that occurred at
the specified location. The simulations were run using 2D analysis for all runs,
but were compared to previously run 3D simulations to maintain the integrity of
the results. The percent differences between the 2D and 3D results were
negligible. The following shows the achieved results for the first run (run#: 0):
Figure 2.1a & b: Initial & Final 2D Setup of Forging Process
Figure 2.2: 2D Simulation Z-Force Load {lbs} on Upper Die
Figure 2.3: 2D Simulation Maximum Final Temp. of Upper Die Surface {°F}
Figure 2.4: 2D Simulation of Net Energy Supplied by Upper Die {J}
Figure 2.5: 2D Results: Max. Effective Stress {psi} in Upper Die
Figure 2.6: 2D Simulation of Maximum Sliding Velocity {in/s} at Upper Die
and Work-piece Interface
Figure 2.7: 2D Simulation of Maximum Contact Pressure {psi} at Upper Die
and Work-piece Interface
Figures 2.2 through 2.7 show the output results obtained from
MSC.Superforge. Each image shows a colored contour image of the 2D blocker
stage of the work-piece, upper and lower dies.
The values for each output process were entered into Minitab Statistical
Analysis Software. Since the simulations were run using computer software,
there are a few assumptions that are made:
1. The run order of the simulations had no bearing on the out come of the
tests
2. The simulations are repeatable, with accurate repeatable results
3. No outside or environmental factors could affect the simulations
To display the results of the design of experiment and the simulations, the
data was formulated into “Normalized Plot of the Effects Charts”. These charts
display the data with the non-significant effects following a normal distribution.
Each chart is fitted with a linear trend-line and significant effects will vary from the
normal distribution and have a high residual from the trend line. The further the
point from the line the larger the effects on the response variable. These charts
are graphed with a Significance Level (α=0.25), which states any values that has
a t-value greater than the 0.25 significance level will be marked as a significant
effect.
“Yatesing” was employed for analyzing the data, and normalizing the plots
[4] the Yates code for each run is shown in Table 2.1.
Table 2.2: Yatesing of Estimated Main Effect of Stroke Speed {in/sec}
Est. D
d-1=
ad - a =
bd- b =
abd – ab =
cd – c =
acd – ac =
bcd - bc =
abcd - abc =
AVG =
Est. D
d-1=
ad - a =
bd- b =
abd – ab =
cd – c =
acd – ac =
bcd - bc =
abcd - abc =
AVG =
EST. EFFECT OF STROKE SPEED {in/s}
Z-Load Force{lbf}
Temp. Final{°F}
Net Energy{J}
Upper
Upper
Upper
3.490E+03
1.480E+03
2.632E+03
3.290E+03
3.500E+01
2.700E+03
3.100E+01
3.837E+03
4.839E+02
8.730E+01
3.148E+01
3.208E+01
2.716E+01
5.515E+01
5.463E+01
4.140E+01
4.694E+01
4.702E+01
-1.480E+04
-3.850E+04
-1.530E+04
2.700E+03
2.360E+03
2.700E+03
2.380E+03
4.140E+04
-2.133E+03
Eff. Stress{Psi}
Sliding Velocity
{in/s}
Pressure (Psi)
Upper
Up Int
WP-Die Int.
2.780E+02
4.455E+03
1.215E+03
1.038E+03
2.096E+03
1.089E+03
1.621E+03
1.050E+03
5.545E+02
3.360E+00
3.380E+00
3.210E+00
3.770E+00
3.340E+00
3.570E+00
3.180E+00
3.510E+00
3.415E+00
6.060E+02
-2.580E+03
-5.260E+03
-1.420E+03
1.720E+03
-1.600E+02
1.230E+03
2.060E+03
-4.755E+02
The Yates difference between specified runs (d-1=d, df-f=d, etc.) was
performed to find the specific effect and then averaged. In Table 2.2, the yatesing
for the estimated main effect of stroke speed is shown.
The averaged estimated effects of stroke speed on each of the various
process outputs were graphed with results of the other main effects, secondary,
tertiary, and fourth order effects. The graph was setup with the effects as the
independent variable, and the percent value as the dependent variable.
Performed to determine which would produce the largest effect on the individual
process outputs. The normal probability plots are shown in Figure 2.8 through
Figure 2.15, and span the range of effects of the process outputs found in Table
5.3. These charts were obtained from Minitab, but the calculations were checked
using Microsoft Excel calculations.
Normal Probability Plot of the Effects
(response is Sliding Velocity Up {in/s}, Alpha = .25)
99
D
95
90
Percent
F actor
A
B
C
D
C
AD
80
Effect Type
Not Significant
Significant
70
60
50
40
30
N ame
F riction
D ie Temp
WP Temp
S troke S peed
20
10
A
5
BC
1
0.0
0.5
1.0
1.5
2.0
Effect
2.5
3.0
3.5
Lenth's PSE = 0.084375
Figure 2.8: Normal Probability Plot of the Effects for Sliding Velocity {in /s}
The Figure 2.8 shows the normalized responses of sliding velocity, as a
result of the varied process inputs. Obviously, the stroke speed (D) has the
greatest effect on the sliding velocity. As the press speed increases the workpiece will be caused to flow at a faster pace. This increased sliding velocity
would show an increase in die wear according to all the wear models, but maybe
offset by an increase in work-piece temperature. Less obvious in this plot is the
effect of WP Temp. (C), and the secondary interaction of Friction and Stroke
Speed each have a positive effect on sliding velocity greater than the significance
level (α=0.25). The main effect of friction causes a negative effect of -0.011, and
the interaction of Die Temperature and WP Temperature causes the greatest
negative effect of -0.115. The normalized plots are extremely important tools in
estimating which effects and interactions are most vital or significant.
Table 2.3: Normalized Results of MSC.Superforge Simulations
SIGNIFICANT EFFECTS
Positive
Negative
RESPONSE STUDIED:
Main 2nd 3rd 4th Main 2nd 3rd 4th
EFFECTIVE STRESS {psi}
AB
C
A
CD ABD
B
AB
NET ENERGY {J}
BD
C
B
BD
D
FINAL DIE SURFACE TEMP{F}
C
Z-LOAD FORCE {lbs}
AB ABD
C
ABC
CD ABD
B
AC
MAX. CONTACT PRESSURE
{psi}
C
Actual
Code Letter
Friction A
Die Temperature B
Work-piece Temperature C
Stroke Speed D
Table 2.3 displays significant effects and interactions on various output
responses vital in determining cycles till die failure. The table shows which
effects, or interaction of effects, caused a significant (α>0.25) response of the
parameters. These results can be used to develop a significant die life model,
when used in conjunction with
Conclusion
This research is a preliminary step in developing a universal metal forging
die life equation. The point of researching (1) friction, (2) initial die temperature,
(3)work-piece temperature, and (4)forging press stroke speed effects on (1)die
stress, (2)net energy, (3)sliding velocity, (4)contact pressure, (5)final surface
temperature, and (6)load force is to ultimately predict the most likely mode of die
failure. Once the most probable mode of die failure is calculated, a forecast of the
forging press cycles until die failure can be determined.
The results of this research show that the forging process die outputs
responses are affected significantly by only a small number of main effects, or
interaction of those effects. The significance level for this research was set high
(α=.25) showing that any effect that had an effect greater than 25%, above or
below the zero effect was deemed significant. For instance, analysis of the load
force [Figure 2.12] revealed that the only significant main effect was the change
of work-piece temperature. As research on this subject is furthered, and actual
effect of input processes on the cycles till failure is calculated, the significance
level of these primary studies can be altered to optimize the final equation.
3.0
Future Research & Recommendations
To develop a full-scale die life model capable of predicting die failure
mode, location and cycles, all the effects of forging inputs on die life must be
3.1.1
researched and quantified. This research will have to span the spectrum of
forging temperatures, die formations, and estimate the effects of future die
geometries. An equation can then be formulated that calculates the number of
cycles till failure based off the significant process inputs raised to an empirically
determined exponents (Appendix F).
As mentioned this is an extremely time consuming and tedious process
that depends upon the accuracy of the measured empirical constants in the
metal failure equations and upon the accuracy of the effects of the forging
process inputs on the quantified outputs.
To decrease the massive time that is required for this research, finite
element modeling and forging simulations must be incorporated, and its results
must be validated via actual forging process results. Therefore, a faster method
of determining actual significant forging effects on die life can be estimated and
non-significant effects will be weeded out. This process will allow the time
required to develop the model decrease as the number of factors incorporated is
increased.
Finale
Just as the forging die life prediction equation itself will be a valuable tool
in the cost reduction, design and optimization of forging dies for the forging
industry, the research preformed for this thesis will be a valuable primary step in
developing the die life equation.
3.1.2
APPENDICES
Appendix A: MSC.Superforge Ring Gear Simulation Setup
MODELS: Drawn in SolidEdge imported to Superforge via .stl files.
AutoCAD Drawings in Appendix D.
II. STAGE B - BLOCKER STAGE
A. Upper Die = Dynamic with Heat Transfer
1) Model = RG (2) Blocker - Top Ins TF6706.stl
2) Material = H-13 Tool Steel Modified to meet ASTM Handbook Specs.
3) Press = Mechanical Scotch Yoke
a) Stroke Length = 14" Total
b) High Level Velocity (Chart B.1)
47 Strokes Per Min.
c) Low Level Velocity (Chart B.2)
36 Strokes Per Min.
4) Friction = Combination Coulomb & Plastic Shear Theory
a) High Level Friction
µp = 0.7
µc = 0.7
b) Low Level Friction
µp = 0.2
µc = 0.2
5) Die Temperature (initially)
a) Heat Transfer Coefficient to Ambient = 50 {W/m^2*K}
b) Heat Transfer Coefficient to Work-piece = 6000 {W/m^2*K}
c) Emissivity for Radiation to Ambient = .25 Unitless
d) High Level Temperature
Tdie = 800F
e) Low Level Temperature
Tdie = 300F
B. Lower Die = Rigid Stationary with Heat Transfer
1) Model = RG (2) Blocker - Bottom Ins TF6706.stl
2) Material = H-13 Tool Steel Modified to meet ASTM Handbook Specs.
3) Friction = Combination Coulomb & Plastic Shear Theory
a) High Level Friction
µp = .7
µc = .7
b) Low Level Friction
µp = .2
µc = .2
4) Die Temperature (initially)
a) Heat Transfer Coefficient to Ambient = 50 {W/m^2*K}
b) Heat Transfer Coefficient to Work-piece = 6000 {W/m^2*K}
c) Emissivity for Radiation to Ambient = .25 Unitless
d) High Level Temperature
Tdie = 800F
e) Low Level Temperature
Tdie = 300F
C. Work-piece
1) Model = Auto Shape – Cylinder
a) Radius = 1.75"
b) Height = 6.50"
2) Material = AISI_4337 Steel From MSC.Superforge Library, Customized to meet ASTM
Handbook Specs.
3) Work-piece Temperature (initially)
a) Heat Transfer Coefficient to Ambient = 50 {W/m^2*K}
b) Heat Transfer Coefficient to Work-piece = 6000 {W/m^2*K}
c) Emissivity for Radiation to Ambient = .25 Unitless
d) High Level Temperature
Twp = 2300F
e) Low Level Temperature
Twp = 1700F
D. 2D Simulation = Axisymmetric Forging Process
* Initially performed in 3D, but simulation took ~3hrs, results then compared to 2D which
took less time
E. Simulation Control = Forming Process
1) Stroke = 3.9136" (Once dies and WP are positioned)
2) Element Size
a) Work-piece Element Size* = 0.10"
b) Die Element Size* = 0.10"
* Element size was chosen based on preliminary trial and error: result v. simulation time
tradeoff.
3) Simulation Steps* = 10 Equal Divisions
* Number of Steps were chosen based on preliminary trial and error: result v. simulation
time tradeoff.
4) Forging Process = Closed Die with Flash
F. Cooling Time
1)Cooling Time = 2 seconds
G. Simulation
1) Check Data
2) Run Restart
H. Die Stress Simulation Upper Die
I. Die Stress Simulation Lower Die
J. Results
1) Effective Stress - Upper Die
2) Effective Stress - Lower Die
3) Net Energy - Upper Die Chart
4) Z-Force Load Chart - Upper and Lower Loads
5) Temperature - Upper Die Surface
6) Temperature - Upper Die Interior
7) Temperature - Lower Die Surface
8) Temperature - Lower Die Interior
9) Maximum Sliding Velocity - Upper Die
10) Maximum Sliding Velocity - Lower Die
11) Contact Pressure - Work-piece/Die Interface
Appendix B: Material Properties
%C
0.32-0.45
Elastic Properties
Density
Hardness Rockwell C
Knoop
Brinell 3000
Vickers
Tensile Strength,
Ultimate
Yield
Elongation at Yield
Reduction of Area
Modulus of Elasticity
Bulk Modulus
Shear Modulus
Poissons Ratio
H-13 Steel, 0.008-0.012dp Nitride
H-13 Steel
AISI
T20813
UNS
Composition
%Fe
%Mn
%Cr
96
0.20-0.50
0.80-1.20
Mechanical Properties
Param.
SI
Units
ρ1
7761
{kg/m^3}
%Ni
.30 Max
English
0.2818
%Mo
1.1-1.75
Units
{lb/in^3}
HR
HK
HB
Hv
45-48 (46)
570
422-455
549
{NA}
{NA}
{NA}
{NA}
45-48 (46)
570
455
549
{NA}
{NA}
{NA}
{NA}
σu
σy
1.50E+09
1.41E+09
0.13
47
2.07E+11
1.40E+11
8.10E+10
0.3
{Pa}
{Pa}
%
%
{Pa}
{Pa}
{Pa}
{NA}
2.18E+05
2.04E+05
0.13
47
3.00E+07
2.03E+07
1.16E+07
0.3
{psi}
{psi}
%
%
{psi}
{psi}
{psi}
{NA}
English
580
0
0
1.30E-04
Units
{psi}
{NA}
{NA}
{NA}
E1
V1
Plastic Properties
Minimum Yield Stress
Yield Constant
Strain Rate Hard. Exp.
Wear Coefficient
V1
C1
M1
K1
SI
Units
4.00E+06
{Pa}
0
{NA}
0
{NA}
1.30E-04
{NA}
Thermal Properties
Heat Capacity
C1
460
{J/Kg-K}
0.11
Thermal Conduct. @ 27C
K1
17.6
{W/m-K}
122
at 204C
K1
23.4
{W/m-K}
162
at 649C
Coeff. Of Therm. Exp. @
0C
CTE @ 100C
CTE @ 250C
K1
26.8
{W/m-K}
186
{BTU/lbdegF}
{BTU-in/hrft²-°F}
{BTU-in/hrft²-°F}
{BTU-in/hrft²-°F}
α1
α1
α1
10.40
11.30
12.40
{µm/m-K}
{µm/m-K}
{µm/m-K}
5.80
6.30
6.90
{µin/in-F}
{µin/in-F}
{µin/in-F}
4320 Steel, Normalized above 1168K (1640 degF)
%C
0.17-0.22
Elastic Properties
Density
Hardness, Rockwell C
Knoop
Brinell 3000
Vickers
Tensile Strength, Ultimate
Yield
Elongation at Break
Modulus of Elasticity
Bulk Modulus
Shear Modulus
Poissons Ratio
Izod Impact
Plastic Properties
Minimum Yield Stress
Yield Constant
Strain Rate Hard Exp.
Wear Coefficient
4320
AISI
G43200
UNS
Composition
%Fe
%Mn
%Cr
96
0.55
0.5
Mechanical Properties
Param.
SI
Units
ρ2
7850 {kg/m^3}
21 {NA}
HR
HK
255 {NA}
HB
232 {NA}
Hv
247 {NA}
σu
7.93E+08 {Pa}
σy
4.60E+08 {Pa}
20.8 %
E2
2.05E+11 {Pa}
1.40E+11 {Pa}
8.00E+10 {Pa}
V2
0.3 {NA}
73 {J}
SI Units
V2
2.60E+07 {Pa}
C2
0 {NA}
M2
0 {NA}
K2
1.30E-04 {NA}
Thermal Properties
%Ni
1.83
English
0.284
21
255
232
247
1.15E+05
6.67E+04
20.8
2.97E+07
2.03E+07
1.16E+07
0.3
53.8
English
3770.98
0
0
1.30E-04
Heat Capacity
C2
472
{J/Kg-K}
0.114
Thermal Conductivity
Coeff. of Thermal Expansion
K2
49.8
1.50E-05
{W/m-K}
{1/K}
309
8.37E-06
α2
%Mo
0.25
Units
{lb/in^3}
{NA}
{NA}
{NA}
{NA}
{psi}
{psi}
%
{psi}
{psi}
{psi}
{NA}
{ft-lb}
Units
{psi}
{NA}
{NA}
{NA}
{BTU/lbdegF}
{BTUin/hr-ft²°F}
{1/degF}
Appendix C: Forging Press Velocity Curves
Figure F.1: Velocity v. Time Plot of Mech. Yoke Press
40
30
Upper Die 47 strokes per min
Upper Die 36 strokes per min
Velocity of Upper Die {in/s}
20
10
Stage A Figure F.2
0
0
0.005
0.01
0.015
0.0178
0.02
BDC
BDC
0.0245
0.025
0.03
-10
Figure F.3
-20
-30
-40
Time (s)
Appendix D: Taylor’s Tool Life Equation
History: 1906, Frederick W. Taylor determined a relationship between cutting
speed (V) and tool life (T). This formula of:
V x Tn = C
Equation A.1: Taylor’s Tool Life Formula
This formula determines the tool life and is dependent upon the constants n and
C. Graphed on a log-to-log graph this relationship provides the user with a linear
relationship and an approximate time length till tool failure. This basic form of
Taylor’s original study can be expanded to include more effecting variables and
take on the form:
V x Tn x fn1 x dn2 = C
Equation A.2: Expanded Taylor’s Tool Life Formula
In this expanded version, (f) represents the feed rate, and (d) represents the
depth of cut. Both are raised to individual constants. Using this basic strategy a
forging die life formula can be created. As an example:
Nf = P + Tdn1 + Twpn2 + Vn3 + Hn4 + tn5……
Equation A.3: Example of Die Life Prediction Formula
(P) = Forging Load
(Td) = Temperature of Die
(Twp) = Temperature of Workpiece
(V) = Velocity of Dies
(H) = Hardness Ratio of Dies
(t) = Forging Process Length
(Nf) = Cycles till Failure
Above is an example of how a die life prediction model could appear once
research is finished.
Appendix E: ANOVA Tables from Statistical Analysis
Statistical analysis of design of experiments is validated by the analysis of
variance (ANOVA) of the effects. Pioneered by Ronald Fisher in the 1920’s, the
research run in this experiment is a random effects analysis. 16 simulations
were run resulting in 15 degrees of freedom.
General Linear Model: Z-Load Force, Temperature , ... versus Friction, Die Temp
Factor
Type Levels Values
Friction fixed
2 0.2, 0.8
Die Temp
fixed
2 300, 800
WP Temp
fixed
2 1700, 2300
Stroke Speed fixed
2 8.4, 11.0
Analysis of Variance for Z-Load Force Up Die {lbs}, using Adjusted SS for Tests
Source
DF Seq SS Adj SS Adj MS F P
Friction
1 737452 737452 737452 0.16 0.698
Die Temp
1 4111770 4111770 4111770 0.89 0.367
WP Temp
1 93629814 93629814 93629814 20.19 0.001
Stroke Speed 1 234983 234983 234983 0.05 0.826
Error
11 51017288 51017288 4637935
Total
15 149731306
S = 2153.59 R-Sq = 65.93% R-Sq(adj) = 53.54%
Analysis of Variance for Temperature Up Surface {F}, using Adjusted SS for
Tests
Source
DF Seq SS Adj SS Adj MS
F P
Friction
1
3
3
3 0.02 0.890
Die Temp
1 803309 803309 803309 5418.02 0.000
WP Temp
1 17923 17923 17923 120.88 0.000
Stroke Speed 1 8841 8841 8841 59.63 0.000
Error
11 1631 1631 148
Total
15 831706
S = 12.1765 R-Sq = 99.80% R-Sq(adj) = 99.73%
Analysis of Variance for Net Energy {J}, using Adjusted SS for Tests
Source
DF
Seq SS
Adj SS
Adj MS F P
Friction
1 1464210225 1464210225 1464210225 7.07 0.022
Die Temp
1 435348225 435348225 435348225 2.10 0.175
WP Temp
1 10581208225 10581208225 10581208225 51.06 0.000
Stroke Speed 1 18190225 18190225 18190225 0.09 0.773
Error
11 2279581675 2279581675 207234698
Total
15 14778538575
S = 14395.6 R-Sq = 84.58% R-Sq(adj) = 78.97%
Analysis of Variance for Z-Force Up (Cht) {lbs}, using Adjusted SS for Tests
Source
DF
Seq SS
Adj SS
Adj MS F P
Friction
1 1.35387E+11 1.35387E+11 1.35387E+11 1.91 0.194
Die Temp
1 1.61765E+11 1.61765E+11 1.61765E+11 2.29 0.159
WP Temp
1 22710490000 22710490000 22710490000 0.32 0.582
Stroke Speed 1 38239802500 38239802500 38239802500 0.54 0.478
Error
11 7.77996E+11 7.77996E+11 70726936591
Total
15 1.13610E+12
S = 265945 R-Sq = 31.52% R-Sq(adj) = 6.62%
Analysis of Variance for Effective Stress Up {psi}, using Adjusted SS for Tests
Source
DF Seq SS Adj SS Adj MS F P
Friction
1 1690000 1690000 1690000 0.55 0.476
Die Temp
1 39601 39601 39601 0.01 0.912
WP Temp
1 19395216 19395216 19395216 6.26 0.029
Stroke Speed 1 1229881 1229881 1229881 0.40 0.542
Error
11 34092486 34092486 3099317
Total
15 56447184
S = 1760.49 R-Sq = 39.60% R-Sq(adj) = 17.64%
Analysis of Variance for Sliding Velocity Up {in/s}, using Adjusted SS for
Tests
Source
DF Seq SS Adj SS Adj MS
F P
Friction
1 0.048 0.048 0.048 2.33 0.155
Die Temp
1 0.000 0.000 0.000 0.00 0.946
WP Temp
1 0.112 0.112 0.112 5.40 0.040
Stroke Speed 1 46.649 46.649 46.649 2243.23 0.000
Error
11 0.229 0.229 0.021
Total
15 47.038
S = 0.144206 R-Sq = 99.51% R-Sq(adj) = 99.34%
Analysis of Variance for Contact Pressure {Psi}, using Adjusted SS for Tests
Source
DF Seq SS Adj SS Adj MS
F P
Friction
1
4225
4225
4225 0.00 0.971
Die Temp
1 4536900 4536900 4536900 1.49 0.248
WP Temp
1 370177600 370177600 370177600 121.51 0.000
Stroke Speed 1 902500 902500 902500 0.30 0.597
Error
11 33510950 33510950 3046450
Total
15 409132175
S = 1745.41 R-Sq = 91.81% R-Sq(adj) = 88.83%
ACKNOWLEDGEMENTS:
I would like to thank American Axle Manufacturing and there North Tonawanda Forge
Facility for opening there doors and allowing access to there forge facility. A special
thanks to Dr. Bamidele Oyekanmi and Matt Gersley for there time and cooperation.
REFERENCES
[1.]
[2.]
[3.]
[4.]
[5.]
[6.]
[7.]
[8.]
[9.]
[10.]
[11.]
[12.]
[13.]
[14.]
[15.]
[16.]
[17.]
Bannantine, Julie A., Comer, Jess J, Handrock, James L. Fundamentals
of Metal Fatigue Analysis. Prentice Hall. Englewood Cliffs N.J. 1990.
Bay, N., P. Skov-Hansen, J. Gronbaek, P. Brondsted. “Fatigue in Cold
Forging Dies: Tool Life Analysis.” Journal of Materials Processing
Technology. Vol. 95. 1999: 40-48.
Bayer, Raymond G. Wear Analysis for Engineers. Vestal, New York: HNB
Publishing, 2002.
Berger, Paul D. & Maurer, Robert E. Experimental Design: With
Applications in Management, Engineering, and the Sciences. Duxbury
Thomson Learning, 2002.
Caddell, Robert M., & William F. Hosford. Metal Forming: Mechanics and
Metallurgy. PTR Prentice Hall Inc. Englewood Cliffs, N.J. 1993.
Castro, J.T.P., M.A. Meggiolaro. “Statistical Evaluation of Strain-Life
Fatigue Crack Initiation Predictions.” International Journal of Fatigue.
Vol. 26. (2004). 463-476.
Collins, Jack A. Failure of Materials in Mechanical Design: Analysis,
Prediction, Prevention. Wiley-Interscience Publication 1993.
Committee. ASM Handbook: Volume 11, Failure Prevention, Analysis,
and Prevention. ASM International, USA, 1986
Dean, T.A., D.J. Jeong, D.J. Kim, J.H. Kim, B.M. Kim. “Effects of Surface
Treatments and Lubricants for Warm Forging Die Life.” Journal of
Materials Processing Technology. Vol. 113. 2001: 544-550.
Dobychin, M.N., I.V. Kragelsky, V.S. Kombalov. Friction & Wear:
Calculation Methods. Pergamon Press. USSR. 1982.
Engel, U., Falk, B., & Geiger, M., “Estimation of Tool Life in Bulk Metal
Forming Based on Different Failure Concepts.” Journal of Materials
Processing Technology. Vol. 80-81. 1998: 602-607.
Farahmand, Bahram. Fatigue and Fracture Mechanics of High Risk Parts.
Chapman & Hall International Thomson Publishing. 1997.
Fatemi, Ali, Fuchs, Henry O., Stephens, Ralph I., & Stephens, Robert R.
Metal Fatigue in Engineering. 2nd edition, John Wiley & Sons. 2001.
Fatemi, Ali, Zoroufi, Mehrdad. “A Comparative Study of Forged Steel, Cast
Aluminum, and Cast Iron: Monotonic Properties, Cycle Deformation,
and Fatigue Behavior. Forging Industry Educational and Research
Foundation. March 2003.
Gorczyca, Fryderyk E. Application of Metal Cutting Theory. Industrial
Press Inc. New York, New York. 1987.
Hallstrom, Jonas, Ulf Stahlberg. “A Comparison Between Two Wear
Models.” Journal of Material Processing Technology. Vol. 87. 1999:
223-229.
Iwama, Tatsuro, Morimoto, Yashiro. “Die Life and Lubrication in Warm
Forging.” Journal of Material Processing Technology. Vol. 71. 1997:
43-48.
[18.]
[19.]
[20.]
[21.]
[22.]
[23.]
[24.]
[25.]
[26.]
[27.]
[28.]
[29.]
[30.]
[31.]
[32.]
[33.]
[34.]
Kang, J.H. et. al. “A Study on Die Wear Model Considering Thermal
Softening (II): Application of the Suggested Wear Model.” Journal of
Materials Processing Technology. Vol. 94. 1999: 183-188.
Kim, B.M., Kim, K.H., Lee, H.C. “Estimation of Die Service Life in Hot
Forging, Considering Lubricants and Surface Treatments.” Institution of
Mechanical Engineers. Vol. 217. 24 Feb. 2003: 1011-1022.
Lapovok, R. “Improvement of Die Life by Minimization of Damage
Accumulation and Optimization of Preform Design.” Journal of
Materials Processing Technology. 1998: p. 608-612.
Minami, Marumo, Saiki, Sonoi. “Effects of the Surface Structure to Plastic
Deformation of a Hot Forging Tool.” Journal of Materials Processing
Technology. Vol. 113. 2001. 22-27.
Persson, Anders. “Strain-Based Approach to Crack Growth and Thermal
Fatigue Life of Hot Work Tool Steels.” Scandinavian Journal of
Metallurgy. Vol. 33: 53-64.
Polak, Jaroslav. Cyclic Plasticity and Low Cycle Fatigue Life of Metals.
Elsevier. USA. 1990.
Schijve, Jaap. Fatigue of Structures and Materials. Kluwer Academic
Publishers. Dordrecht, Netherlands. 2001.
Shivpuri, Rajiv. “Friction and Wear of Dies & Die Materials .” Adapted
from “Wear of Dies and Molds in Net Shape Manufacturing.” Report
ERC/NSM-88-05 N.S.F. Engineering Research Center, Ohio State
University. 1998.
Shivpuri, R. Babu, Sailesh, & Ribeiro, Dilmarj. “Materials and Surface
Engineering for Precision Forging Dies.” Precision Forging Consortium
Ohio Aerospace Institute and National Center for Materials Sciences.
June 10, 1999:
Simons, Eric N. Metal Wear: A Brief Outline. Fredrick Muller Limited.
London Great Britain. 1972.
Sobczyk, K. Stochastic Approach to Fatigue: Experiments, Modeling, and
Reliability Estimation. CISM, Udine. Italy. 1993.
Subramanian, C., Sumerville, E., & Venkatesan, J.,”Wear Process in Hot
Forging Press Tools.” Materials & Design. Elsevier Science Ltd. Apr.
1996: 289-294. Vol. 16 Number 5.
Unknown. Product Design Guide for Hot Forging. Forging Industry
Association (F.I.A.). www.forging.org. 2002.
Unknown. “Help Files.” MSC.Superforge 2004.
Weronski, Andrzej & Tadeusz Hejwowski. Thermal Fatigue of Metals.
Marcel Dekker, Inc. New York, New York. 1991.
Norton, Robert L. Design of Machinery. Elsevier Science Ltd., New York,
New York. 2000.
McEviley, J. Mechanical Failures. Elsevier Science Ltd., New York, New
York. 2000.