INSTITUTO TECNOLOGICO Y DE ESTUDIOS SUPERIORES DE MONTERREY
CAMPUS MONTERREY
DIVISION DE INGENIERIA Y ARQUITECTURA PROGRAMA DE GRADUADOS EN INGENIERIA
PREDICTION OF DIE WEAR IN WARM FORGING PROCESS WITH FORWARD EXTRUSION OPERATIONS
TESIS
PRESENTADA COMO REQUISITO PARCIAL PARA OBTENER EL GRADO ACADEMICO DE
MAESTRO EN CIENCIAS
ESPECIALIDAD EN SISTEMAS DE MANUFACTURA POR:
JOSÉ LUIS GONZÁLEZ MÉNDEZ
INSTITUTO TECNOLOGICO Y DE ESTUDIOS SUPERIORES DE MONTERREY
CAMPUS MONTERREY
DIVISION DE INGENIERIA Y ARQUITECTURA PROGRAMA DE GRADUADOS EN INGENIERIA
Los miembros del Comité de Tesis recomendamos que la presente Tesis del Ing. José Luis González Méndez sea aceptada como requisito parcial para
obtener el grado académico de Maestro en Ciencias con especialidad en:
SISTEMAS DE MANUFACTURA
Comité de Tesis
__________________________________
Dr. Ciro Angel Rodríguez González Asesor
_______________________________ __________________________________
Dr. Alex Elias Zúñiga Dr. Nicolás Jorge Hendrichs Troeglen
Sinodal Sinodal
APROBADO
_______________________________________
Dr. Joaquín Acevedo Mascarúa
Director del Programa de Graduados de la Escuela de Ingeniería
ABSTRACT
In order for forging companies to remain competitive in a global market, they must utilize modern techniques that shorten development time and expedite the production process. Along with using knowledge based techniques, companies can also utilize Finite Element Analysis (FEA) to increase die life and profitability.
Accordingly, the main focus of this research is to predict die life. Within the scope of this study, a die wear prediction methodology is proposed.
The methodology encompasses FEA techniques to determine the steady-state process parameters. Production data is used to conduct further analysis into the behavior of the die material during forging. Wear models are necessary to estimate the wear coefficient K, which is an inherent value to die material. By means of the methodology application, die wear can be predicted, and subsequently reduced to increase profitability.
The proposed methodology predicts die wear and allows process improvements through consideration of different process parameters.
DEDICATORY
To my family.
A mi familia.
ACKNOWLEDGMENTS
The success of this research has been due to the invaluable contribution of various individuals. First and foremost, I would like to express my gratitude to my adviser Dr. Ciro Rodríguez. This research work would not have been possible if not for his trust, guidance and insight throughout my graduate studies.
I am also grateful to Dr. Alex Elías for his encouragement and support during the last three years. I would also like to thank Dr. Nicolás Hendrichs for serving on my thesis committee and providing valuable suggestions and guidance. Special thanks go to Dr. Taylan Altan for the given opportunity, his vision, valuable advice and trust.
Sincere thanks are extended to: all the students, visiting scholars and staff of the Engineering Research Center at The Ohio State University; and the graduate students and staff of the Department of Mechanical Engineering at ITESM Campus Monterrey.
My heartfelt acknowledgments go to my colleagues: Ana Jáuregui, Angel González, David Olvera, Samantha Rodríguez and Víctor Calva.
I want to acknowledge the collaboration to this work to the CONACYT project Synthesis and constitutive modeling of biocompatible polymers for micro fluidic devices #61061.
TABLE OF CONTENTS
ABSTRACT... III DEDICATORY...IV ACKNOWLEDGMENTS ...V TABLE OF CONTENTS ...VI LIST OF FIGURES ...VIII LIST OF TABLES ...X LIST OF SYMBOLS ...XI
1 INTRODUCTION ... 13
2 RESEARCH FOCUS AND OBJECTIVES ... 3
3 DIE WEAR MODELS IN FORGING ... 5
4 PROPOSED METHODOLOGY FOR DIE WEAR PREDICTION ... 9
4.1 WEAR MODEL FORMULATION... 9
4.2 DISCRETIZED WEAR MODEL... 11
4.3 MODEL CALIBRATION... 12
4.4 WEAR MODEL VERIFICATION... 14
4.5 WEAR MODEL APPLICATION... 14
5 CASE STUDY ... 15
5.1 OVERVIEW... 15
5.2 PROCESS PARAMETERS... 16
5.3 DIE WEAR MEASUREMENTS... 20
5.4 PROCESS FE MODEL... 23
5.4.1 Parameter determination ... 25
5.5 WEAR MODEL APPLICATION AND CALIBRATION... 28
5.6 WEAR MODEL VERIFICATION... 30
5.7 WEAR MODEL APPLICATION FOR PROCESS IMPROVEMENT... 31
6 SUMMARY AND CONCLUSIONS... 34
6.1 SUMMARY... 34
6.2 CONTRIBUTIONS... 34
6.3 CONCLUSIONS... 35
6.4 FUTURE WORK... 36
LIST OF REFERENCES ... 37
APPENDIX A. WEAR MODELS ... 39
APPENDIX B. MATERIALS PROPERTIES... 44
APPENDIX C. SIMULATION OPERATIONS DESCRIPTION ... 48
APPENDIX D. DEFORMTM RESULT VALUES... 63
APPENDIX E. DIE COUPON PREPARATION... 68
APPENDIX F. MATLAB CODE PROGRAMS ... 71
APPENDIX G. FUNDAMENTALS OF FEM IN METAL FORMING ... 77
LIST OF FIGURES
FIGURE 4.1SCHEMATIC DEMONSTRATION OF ABRASIVE WEAR AND AFFECTING
PARAMETERS... 10
FIGURE 4.2SCHEMATIC DEMONSTRATION OF THE DISCRETIZED WEAR MODEL... 11
FIGURE 4.3METHODOLOGY FOR WEAR MODEL APPLICATION IN ABRASIVE WEAR PREDICTION... 12
FIGURE 5.1FORWARD EXTRUSION STATION IN A WARM FORGING PROCESS... 16
FIGURE 5.2:SCHEMATIC OF A FORGING CELL SETUP [SHELJASKOW, ET AL.2001] ... 17
FIGURE 5.3:DESCRIPTION OF A FORGING PROCESS ON A MECHANICAL PRESS... 18
FIGURE 5.4WEAR MEASUREMENT OF DIE A.DIE LIFE N=2,200 PIECES... 22
FIGURE 5.5WEAR MEASUREMENT OF REPLICATION DIE B.DIE LIFE N=1,800 PIECES. 22 FIGURE 5.6OBJECTS SET UP IN DEFORM-2DTM... 24
FIGURE 5.7:SIMULATION RESULTS SHOWING SLIDING VELOCITY VALUES AT 1ST,2ND AND 3RD REDUCTIONS... 26
FIGURE 5.8:SIMULATION RESULTS SHOWING CONTACT PRESSURE AT 1ST,2ND AND 3RD REDUCTIONS... 26
FIGURE 5.9SIMULATION RESULTS SHOWING THE FACTOR NORMAL PRESSURE TIMES SLIDING VELOCITY AT 1ST,2ND, AND 3RD REDUCTIONS... 27
FIGURE 5.10:WEAR MODEL CALIBRATION WITH DIE A.DIE LIFE=2,200 PIECES... 29
FIGURE 5.11:WEAR MODEL VERIFICATION WITH REPLICATION DIE B.DIE LIFE=1,800 PIECES... 31
FIGURE 5.12SLIDING VELOCITIES BEHAVIOR FOR 30 SPM,40 SPM, AND 50 SPM... 32
FIGURE 5.13CONTACT PRESSURE BEHAVIOR FOR 30 SPM,40 SPM AND 50 SPM... 33
FIGURE B.1BILLET FLOW STRESS DATA [SOURCE:DEFORM-2DTMV.9.0 DATABASE] ... 45
FIGURE B.2IMPACT RESISTANCE IN HIGH TEMPERATURE [SOURCE:WALTER METALS
LLC]... 45
FIGURE B.3TENSILE STRENGTH IN HIGH TEMPERATURE [SOURCE:WALTER METALS LLC]... 46
FIGURE B.4WEAR RESISTANCE [SOURCE:WALTER METALS LLC] ... 46
FIGURE B.5TEMPERED HARDNESS CHART [SOURCE:WALTER METALS LLC]... 47
FIGURE C.1DIE ASSEMBLY... 52
FIGURE C.2SIMULATION OBJECTS SET UP... 56
FIGURE C.3:TEMPERATURE DISTRIBUTION AT FIRST REDUCTION OVER 25 CYCLES.... 57
FIGURE C.4:INSERT TEMPERATURE DISTRIBUTION AT VARIOUS TIMES DURING FORGING STROKE... 58
FIGURE C.5BILLET TEMPERATURE GRADIENT AFTER 14S OF EXPOSURE TO AMBIENT CONDITIONS... 59
FIGURE C.6LUBRICATION CALIBRATION CURVES WITH VARIOUS VALUES FOR THE CONVECTION COEFFICIENT [SHIRGAOKAR,2008] ... 61
FIGURE E.1CROSS SECTION OF THE EXTRUSION DIE... 69
LIST OF TABLES
TABLE 3.1TOOL WEAR BACKGROUND SUMMARY... 6
TABLE 5.1SIMULATIONS SET UP... 24
TABLE 5.2.MODEL FINE-TUNING... 30
TABLE 5.3ESTIMATED DIE WEAR FOR 30 SPM,40 SPM AND 50 SPM FOR DIE A ... 33
TABLE C.1DIE ASSEMBLY SIMULATION SET UP... 51
TABLE C.2TWO LEVEL SENSITIVITY ANALYSIS FOR FRICTION AND PRESS VELOCITY. ... 62
LIST OF SYMBOLS
B Shaw’s wear module BDC Bottom Dead Center Fn normal load [MPa]
G Shear modulus [MPa]
H Hardness [MPa]
K Wear model coefficient L Sliding distance [mm]
S Mechanical press stroke [mm]
V Ram velocity of a mechanical press [mm/s]
Vsa Yamaguchi’s specific abrasive wear depth [mm]
Z Wear depth [mm]
T Temperature [°C]
TDC Top Dead Center
TM Melting temperature [°C]
a,b,c Archard’s wear model constants
h Actual displacement of the forging stroke [mm]
i Number of forging stroke
j Discretization of die surface length k Discretization of time
n Die life [pieces]
p Normal pressure [MPa]
tf Ending time [seconds]
to Starting time [seconds]
u Specific energy [MPa]
∆t Contact time [seconds]
δ Kronecker delta ε Strain [mm/mm]
ε
Effective strain [mm/mm]ε &
Effective strain rate [(mm/mm)/sec]σ
Effective flow stress [MPa]µ Friction coefficient Ν Poisson ratio
1 INTRODUCTION
Forging processes represent an important facet in manufacturing; forged products exhibit excellent mechanical properties with a small amount or no material being wasted.
Forging operations can be classified based on the working temperature range:
hot, warm and cold. The first one is characterized by dynamic recovery, no strainhardening and a temperature range between 0.7TM and 0.8TM, where TM is the incipient melting temperature. In warm forging some strainhardening and/or precipitation hardening may occur and temperature ranges between 0.3TM and 0.5 TM. Cold forging takes place under 0.3TM and strainhardening occurs [Mielnik, 1991].
In comparison with hot forging, warm forging presents the following advantages:
a) it is more cost friendly due to reduced energy costs of heating the workpiece, b) there is no scale formation, c) tighter tolerances and d) less subsequent machining; when compared to cold forging warm forging requires lower forming loads. However, temperatures and pressures are high in warm forging leading to high tooling costs due to modifications in cold forging dies that would allow increased temperatures, internal die cooling and venting of coolants [Altan et al., 2005].
Forward extrusion is a process where a punch compresses a billet confined in a container so the billet material flows through a die in the same direction that the punch. This operation is characterized by high sliding velocities, temperature
generation and very high normal pressures localized at die corner radii where a large reduction in area of the workpiece takes place.
All these physical and process factors impact on the tool dies performance and, therefore on their cycle life. Literature has classified modes of failure as follows (See Appendix A):
• Abrasive and Adhesive Wear
• Thermal and Mechanical Fatigue
• Plastic Deformation
The primary failure mode in forward extrusion is generally abrasive wear, causing: a) poor workpiece dimensional tolerances, b) poor workpiece surface finish and/or c) sticking between workpiece and die. [Altan et al., 2005]
The cost of forging tooling has proved to be one of the most important factors in the overall process cost. Die costs range from 10% to 15% of the whole process economy [Doege, et al., 1994]. It is also important to consider that tool set up times can range anywhere from under 10 minutes to over 3-4 hours, resulting in additional overhead costs [Babu, 2004].
Finite element analysis (FEA) has become a useful tool for forging companies that are in the search of improving and developing new forging processes, with little or no trial and error and significantly decrease lead time. One of the most important applications of FEA in the forging studies is the estimation of die wear.
Through these tools forging companies are now able to produce parts with tighter tolerances and better surface finishes as well as maximize material utilization.
2 RESEARCH FOCUS AND OBJECTIVES
In forging, the final cost of the die is directly affected by: a) die design, b) the cost of the tool material, c) subsequent machining to achieve production specifications, and d) heat and/or surface treatments on the dies. In turn, die life will affect the die cost portion added to the unit cost of the product. Dies that fail prematurely create production stoppages which can lead to press downtime of up to several hours [Babu, 2004]. Since the cost of the dies is amortized over the entire life of the die, it is essential to improve die life to increase profitability.
In forward extrusions, a large reduction in cross section of the workpiece substantially increases the velocity at which the material is flowing. This velocity, in conjunction with the pressure at which the material flows, affects the wear rate of the material.
Finite Element Analysis (FEA) has been widely used to study metal forming processes. Specifically in forging, the analysis can be used to evaluate the forming processes and tool designs in the designing stage as well as in the trouble shooting stage. For example: metal flow information predicted by FEA enables a designer to check if an intended part geometry can be obtained without any defects. Reviewing the tool contact pressure predicted by FEM enables designers to understand and/or to eliminate any possible mistakes in die design. Tool stress analysis by FEA is effectively used in tool life improvement [Kim et al., 2000].
The following objective was selected for this research project:
• Prediction of die wear for a warm forging process with forward extrusion operations, using wear models and FEA solutions.
The study is conducted through the following approach:
• A methodology for die wear prediction is proposed based on wear models.
The methodology needs to go through calibration and verification processes.
• FEM simulations and experimental trials have to be conducted in order to input the necessary data into the model.
• Analysis of possible process improvement through application of the methodology proposed.
The outline of the presented thesis goes as follows: Chapter 1 presents an introduction to the modes of failure in forging, specifically abrasive wear in warm forging with forward extrusion operations. Chapter 2 outlines the motivation and objectives of this study. Chapter 3 presents the literature review on wear models.
Chapter 4 summarizes the proposed methodology for this study. The methodology is later corroborated in Chapter 5 where a study case is exposed.
Finally Chapter 6 summarizes and concludes the research.
3 DIE WEAR MODELS IN FORGING
Die wear can be classified in abrasive and adhesive. Abrasive wear arises when a hard, rough surface slides against a softer surface, digging into it and plowing a series of grooves in the soft surface. In more process related words, abrasive wear can be defined as a die failure phenomenon where the die loses its original geometry over a period of time due to a constant abrasive action of particles across the die surface under high pressures during forging [Babu, 2004].
Adhesive wear occurs when the sliding metal tends to dissolve the surface features of the die. In warm and hot forging this phenomenon is generally exposed by the die picking up portions of the billet material. Thus, adhesive wear is prone to occur with softer billet materials such as aluminum [Shirgaokar, 2008].
Because of the extreme conditions in warm forging with a forward extrusion operation (high temperatures, high sliding velocity and high contact pressures), wear is affected mainly by abrasion [Dahl et al., 1999a, Painter, 1995]. For this reason, this study only focuses on abrasive wear models.
In most of the reviewed literature, abrasive wear, whether it is expressed in terms of volume or length, is predicted using Archard’s type models. Although, there are other studies that analyze wear from different scopes, such as Shaw (1989) and Yamaguchi (1990), who base their models in other approaches: the energy that is required to remove a unit of material, and the work done to detach material is proportional to the ultimate strength of the material and the strain at breaking point, respectively. The process parameters that have an effect in most
pressure, contact time between the soft and hard material, tool’s cycle life, die material hardness, and a wear coefficient K that is experimentally estimated. A summary of the models reviewed is presented in Table 3.1.
Table 3.1 Tool Wear Background Summary
WEAR MODEL COMMENTS
Archard (1953)
H F n K L
W = ⋅
Where:
W= wear volume L= sliding distance K=wear coefficient Fn=normal load
H=yield stress or hardness
- Wear coefficient K must be determined experimentally
- H is a function of temperature for each material
- L is sliding velocity multiplied by time
- Fn= pressure * area
- The model is not specified for abrasive nor adhesive
Archard (1953):
H L p W = K ⋅ ⋅
Where:
W= wear volume K= wear coefficient p= normal pressure L= sliding distance
H= hardness of the worn material
- An abrasive wear coefficient K is introduced in this model, a dimensionless number of the order of several times 10-4
Shaw (1989):
u L P
B = µ
⋅
Where:
B= wear module
L= distance traveled by abrasive particle
- u is found from deformation energy
- P= pressure * area - L = velocity *time
P= applied load
µ= coefficient of friction
u= specific energy to produce a chip Yamaguchi (1990)
ε σ µ
⋅
⋅
= ⋅ H 0
C V sa
Where:
Vsa=specific abrasive wear depth
C=wear constant depending on type of material.
µ= friction coefficient H= Hardness
σ0=Maximum Yield Strength ε= Strain
- Wear constant C is experimentally determined.
- Hardness H is a function of temperature
- strain ε corresponds to the strain
at breaking point
Painter et al. (1995):
c H d
b t a V p K
Z ab ⋅ ( ⋅ ⋅ ∆ )
=
Where:
Zab= abrasive wear depth K=experimental coefficient p= local pressure
V=local sliding velocity Hd=tool hardness
a, b, c= experimental constants
- Wear coefficient K is
experimentally obtained
- Hardness Hd is a function of
temperature
Kang et al. (1999)
) ,
, (
3 H T t w initial H H
L p fin K
d ⋅
⋅
⋅
= ∑ ⋅
- Hardness H(T,t,winitial) is a function of temperature, time and the hardness conditions of the previous layer
Where:
dfin= allowable amount of wear of the dies
H(T,t,winitial)=function of hardness softening considering tempering parameter
H= hardness of the die at steady-state temperature
K= experimental coefficient P= local pressure
L= sliding distance
- H is the hardness under initial conditions
- Wear coefficient K is
experimentally obtained
Behrens (2005)
∑
=
⋅ ∆
= n i
rel t t v H w N
1 ( θ , ) σ
Where:
w= amount of wear σN= normal pressure
H= Hardness dependant on temperature and time
Vrel= sliding velocity
∆t= interval of time n= number of cycles
- Hardness H(θ ,t) is obtained by
first estimating a tempering parameter that later is included into a hardness quadratic equation.
- This model works for hot forging operations.
- The die reaches the tempering range; under such statement hardness will be calculated through the thermal softening curves.
4 PROPOSED METHODOLOGY FOR DIE WEAR PREDICTION
4.1 Wear model formulation
The wear model proposed in this study for warm forging with forward extrusion operations is based on the models developed by Archard and Painter [Archard, 1953; Painter, 1995], and is shown in Eq. (4.1),
t c H
b t a v K p Z
) (∆
∆
⋅
⋅ ⋅
= (4.1)
where Z represents the abrasive wear depth localized in a given surface on the die during one forging stroke and is measured in a direction normal to the surface. P and v are the normal pressure and sliding velocity measured on a given point over the die surface. ∆t is the interval of time during the forging stroke in which normal pressure p and sliding velocity v were measured, it is also defined as contact time. K is the abrasive wear coefficient that helps to scale the magnitudes of the predicted wear profile. H(∆t) represents the die hardness corresponding to the actual forging stroke. The constants
a, b
andc
are model- tuning constants, the first two usually take a value of 1 whilstc
a value of 2 for steel die materials [Holm, 1946]. Figure 4.1 shows a schematic representation of abrasive wear with its associated process parameters.Figure 4.1 Schematic demonstration of abrasive wear and affecting parameters.
It is important to note that the proposed model does not directly include the temperature effect, specifically in the hardness parameter. However, the methodology is valid under the following considerations: a) through FEM, the heat generation and heat transfer before, during and after deformation influence on the models parameters behavior, viz. normal pressure, sliding velocity, and material flow stress b) FEM in real-production conditions takes account for the quasi-stable temperatures effect on the process, and c) in this study, hardness values are obtained through experimental trials.
The determination of abrasive wear coefficient K is of primary interest for this study since literature defines it as a tool material property. In other words, the obtained K value can be used in other warm forging processes that involve the same die material and similar die geometry. The K coefficient is found experimentally by comparing the predicted wear to the actual measured wear profile. Normal pressure and sliding velocity data is provided by FEA through simulation of the warm forging operation. Contact time ∆t is estimated from the kinematics of the forging press, whilst a hardness variation equation is built in
order to input the corresponding hardness value H(∆t) into the model. Real wear depth Z is obtained with a high resolution measuring device.
4.2 Discretized wear model
The generalized proposed model has to be discretized to make it suitable for abrasive wear prediction over an arbitrary die surface shape, for a defined period of time and a certain number of forging cycles by means of K coefficient estimation. The discretized model’s equation is shown in Eq. 4.2,
∑ ∑
= =
∆
⋅
= n i
t t k
c t i H
b vjk a pjk j K
Z
f
1 ( )
0
(4.2)
the die surface length of interest is divided by equally distant points; each point registers a local normal pressure and sliding velocity value. Wear depth Z will be estimated for every point j. The model is also discretized time-wise, the contact time in which the billet contacts the previously defined die surface length is divided in smaller intervals of time, ∆t=t(k)-t(k-1) until the range tf-t0 is completely covered. Normal pressure and sliding velocity also change in these intervals of time; therefore, the parameters are input in the model as pjk and vjk since they are time and location dependant.
Figure 4.2 Schematic Demonstration of the Discretized Wear Model
pj(Δt),vj(Δt) pj+1(Δt),vj+1(Δt)
pm(Δt),vm(Δt) pj(tk+1),vj(tk+1)
pj+1(tk+1),vj+1(tk+1)
pm(tk+1),vm(tk+1) pj(tk),vj(tk)
pj+1(tk),vj+1(tk)
pm(tk),vm(tk)
t=tk t=tk+1 Δt
4.3 Model calibration
Figure 4.3 describes the methodology for the wear model calibration. Normal pressure, sliding velocity and intervals of time are provided by FEA through a forging stroke simulation. Finite Element codes simulate operations through steps, whether they are time-steps or die-movement-steps. Thus, the information will be discretized beforehand in order to be inserted in the model.
Figure 4.3 Methodology for Wear Model application in Abrasive Wear Prediction
The die cycle life n is the number of cycles that the die withstood until failure, i.e.
the forged pinions were out of tolerance or poor surface quality, and is experimentally measured.
Regarding die hardness in warm forging, its variation will be throughout the total die life, meaning that operating time dictates the decrease in hardness. Hardness does not vary during a forging stroke due to temperature variation on that single cycle.
In light of the previous statement, an initial and a final hardness value will be necessary to construct the linear equation that determines the hardness as a function of the i-th forging cycle. Microhardness testing of virgin coupons (initial hardness) and worn die coupons (final hardness at n forging stroke) is conducted to obtain such values. The hardness assumption considered for this model is valid since in warm forging the dies do never reach a temperature in which thermal softening would influence on wear behavior. However, studies still have to be conducted on hardness considerations for warm forging, in order to measure properly the effects of thermal softening in warm forging parameters.
Once all the parameters are available, the wear model is calibrated by summations. First, adds the parameters effect on location j through the intervals ∆t from t0 to tf, considering all the contact time in the forging stroke.
Then, summation helps to accumulate the wear effect through the forging cycles until the experimental factor n is reached. Upon inserting all the necessary parameters the wear coefficient K is determined matching, with a low percentage of error, the predicted wear profile with the previously measured wear profile on the die.
The model can be finetuned with the modification of coefficients
a, b
andc
. Constantsa
andb
must remain very close to the unity. Constant c allows more flexibility, but should remain in the range 0.5 to 2.5 [Holm, 1946; Painter, 1995].These modifications will influence on the relations between normal pressure, sliding velocity and hardness in the factor .
Based on the discretization of surface length and time assumed in the model mentioned above, a matrix of values for local pressure and sliding velocity is obtained, as can be inferred from Figure 4.2¡Error! No se encuentra el origen de la referencia.. The model can be simplified for computational time saving purposes, with the following considerations:
• Average Approach: Calculate average values for normal pressure and sliding velocity, over contact time for one forging stroke at each point j.
• Maximum Approach: Take the maximum value of pressure and velocity during contact time at each point j as input in the model since this is the worst case scenario.
The analyzed surface and contact time are usually so small that the deviation in the model parameters may not be significant. Then the generalization of parameters through the approaches considered will not affect greatly the estimated K coefficient and will simplify computation.
4.4 Wear model verification
An experimental verification of estimated wear coefficient K is required. A replication die should be used to conduct new trials under the same process parameters and conditions upon which K value was found. Then, the proposed wear model will be applied to the replication die, inserting the model parameters and the estimated coefficient K. For this replication die, hardness variation equation according to microhardness testing and die life n is obtained from trials.
The acceptance range for verification is set to a 15% error between the real replication wear depth and the resulting prediction with the estimated K value.
4.5 Wear model application
Once the die wear model has been calibrated and verified, the forging process can be analyzed in search for reduced cost or increased productivity. The
generic nature of the die wear model allows for the detailed study of any particular area along the die surface.
5 CASE STUDY
5.1 Overview
In this work, the case study involves forward extrusion operation is included in a three-step warm forging process for an automotive pinion. The warm forging process is divided into three mayor forming operations: 1) forward extrusion, 2) head forming and 3) head coining. However, the critical abrasive wear occurs in the forward extrusion operation.
Figure 5.1 shows the forward extrusion station with all its components including the extrude die. At the extrusion station the critical wear area is located in the extrude insert where the stem of the pinion is formed and the majority of deformation takes place. There are three billet reduction areas in the extrude insert with a 27%, 22% and 9.2% reduction respectively.
Since the main failure mode in the forward extrusion die is abrasive wear, die materials should posses adequate hot hardness, hot yield strength, thermal conductivity, and tempering resistance. The die inserts consist of matrix high speed steel (MHSS) with the following chemical composition: 0.036 Carbon, 0.9 Silicon, 2.85 Molybdenum, 0.6 Manganese, 5.0 Chromium and 0.25 Vanadium, (all values given in weight %) and a Young’s Modulus of 210 GPa. The tool assemblies are formed by press-fitting the dies into steel containers at room
generic nature of the die wear model allows for the detailed study of any particular area along the die surface.
5 CASE STUDY
5.1 Overview
In this work, the case study involves forward extrusion operation is included in a three-step warm forging process for an automotive pinion. The warm forging process is divided into three mayor forming operations: 1) forward extrusion, 2) head forming and 3) head coining. However, the critical abrasive wear occurs in the forward extrusion operation.
Figure 5.1 shows the forward extrusion station with all its components including the extrude die. At the extrusion station the critical wear area is located in the extrude insert where the stem of the pinion is formed and the majority of deformation takes place. There are three billet reduction areas in the extrude insert with a 27%, 22% and 9.2% reduction respectively.
Since the main failure mode in the forward extrusion die is abrasive wear, die materials should posses adequate hot hardness, hot yield strength, thermal conductivity, and tempering resistance. The die inserts consist of matrix high speed steel (MHSS) with the following chemical composition: 0.036 Carbon, 0.9 Silicon, 2.85 Molybdenum, 0.6 Manganese, 5.0 Chromium and 0.25 Vanadium, (all values given in weight %) and a Young’s Modulus of 210 GPa. The tool assemblies are formed by press-fitting the dies into steel containers at room
temperature. Further information on die material properties is included in Appendix B.
Figure 5.1 Forward Extrusion Station in a Warm Forging Process
5.2 Process parameters
The forging cell is fully automated, discarding the human-mistake factor that could influence the analysis. The forging cell consists of a pre-coating station, induction heater, and a mechanical press as shown in Figure 5.2. The transfer system and the lubricant application are also automated.
Figure 5.2: Schematic of a forging cell setup [Sheljaskow, et al. 2001]
A vertical mechanical press with load capacity of 2,500 tons and a 490 mm stroke length is used in the process. The billet is a round cross section AISI 8620 alloy with diameter of 66.68 mm and length of 214 mm approximately. The billet mechanical properties and composition are given in Appendix B. There is an induction heating process for the material, where it reaches a temperature of 960°C – 980°C. A temperature control systems keeps the workpiece temperature in range. The lubrication system employs graphite in aqueous solution lubricant as is usually recommended for warm forging of steels [Altan et al., 2005].
Prior to the forging process, the billet goes through the following operations:
• Billet pre-heating: The billets are preheated to 100-150°C.
• Billet pre-coating: The billets are coated using a graphite-based pre-coat to prevent decarburization and scale formation. The coating also acts as an additional lubricant in the extrusion stage.
• Induction heating: The billets are heated to 960-980°C.
The forming operation in the mechanical press is divided into:
Induction heater
Induction heater
• Waiting Station: Upon exiting the induction heater, the billet rests at the first station before being transferred to the next station via the automated transfer system. No deformation takes place during this time.
• Forward Extrusion Station: The majority of deformation takes place in the first extrusion stage, to form the stem of the pinion. Figure 5.1 shows the three reductions to forge the part. Production stoppages are influenced by the wear that occurs at the die corner radii of extrude insert.
• Head forming Station: The workpiece is transferred to the upsetting stage where the head of the pinion is formed.
• Head Coining Station: The final forging station is used for coining the part, after which the part is cooled at a controlled rate.
In order to understand the forward extrusion cycle, the operation is described in Figure 5.3.
0 2 4 6 8 10 12 14
0 0.5 1 Time (sec)1.5 2 2.5 3
Part Location Clutch Activation Forging Stroke Knockout Lube Spray
Part Removal
to ta tb tc td te tf
I II III IV V VI
TDC
BDC t1 t2
Figure 5.3: Description of a forging process on a mechanical press
• Part location (t0 – ta): The billet is exposed to the environment for 14 seconds from the time it exits the induction heater until it is finally placed on forward extrusion station.
• Die chill time (ta – t1): Time from when the billet is placed on the die until the punch makes contact with the billet ≈ 1.15 seconds.
• Deformation time (t1 – t2): Determined to be 0.14 seconds from press kinematics.
• Knockout and part removal (t2 – te): Estimated to be approximately 2.71 seconds.
• Lube spray (te – tf): The lubricant is sprayed into the upper and lower dies for 1 second, which is followed by a 1.5 second dwell until the start of the next cycle.
• The total cycle time for one part is roughly 6.5 seconds.
The dynamics of the mechanical press addressed for the study have to be analyzed; the driving system is based on a slider crank mechanism that translates rotary motion into reciprocating linear motion. According to Altan (2005) the ram velocity V can be expressed as:
30 ⋅ ⋅ ⋅ − 1
= h
h S V π n
(5.1)
where h is the actual displacement and S represents the press stroke.
The process cycle time and forging loads are available from the press data acquisition system. The total cycle time for each part is 6.5 seconds. The wear model proposed will be used to predict die life in order to increase and optimize tool life.
5.3 Die wear measurements
Once the die inserts are assembled into the steel containers, the dies are put into service at room temperature during a normal production schedule. Die life n is an experimental data obtained from trials. It is the number of cycles that the die withstood until failure, i.e. the forged pinions are out of tolerance or presented poor surface quality. Die life n is obtained as follows: during production, the forged parts are inspected at preset time intervals (20 minutes) as they are completed. Once the pinion no longer fulfills the tolerance and quality requirements, the present die is removed from service, meaning that the die is completely worn and has reached its total die life. Die life n will take the value of the last production stroke before the part inspection.
Trials were carried out with two MHSS dies that are named chronologically-wise as they went into production, Die A and a replication Die B. Forged parts coming from both dies were inspected resulting on the following die life.
• Die A n= 2,200 Pieces
• Die B n= 1,800 Pieces
During inspection it was observed that the location of the greatest amount of wear for both dies was at the third reduction. In order to properly assess the die study and obtain important further information for the die wear prediction (initial and final hardness), a metallographic analysis was conducted. The procedure followed to prepare the samples is addressed in Appendix E.
Sample coupon plates, i.e. virgin plates that have not been used in production, of the MHSS die material were heat-treated and surface-treated with the production dies in the same lot, with the objective of forming a benchmark for comparison before and after forging. Once the virgin coupons were metallographically prepared, hardness was measured through Vickers indentation (See Appendix
Since the location of maximum wear is placed at the third reduction, microhardness measurements were taken only on the bottom-most part of the die, setting the third extrusion as the focus analysis area throughout the rest of the study. Appendix E explains how coupons were obtained from the bottom of worn die A and replication die B. Microhardness readings were taken at 3mm, 8mm, and 18mm from the bottom of the die in the axial direction. Thus, hardness variation equations are as follows:
(5.2) (5.3) where HA and HB are dependant on the i-th forging stoke.
A coordinate measuring machine CMM with a laser scanning head is used to measure the worn die profile with a standard deviation less than 25µm. Die A and replication die B are scanned one by one. Multiple scans are needed, each time scanning from different x, y, and/or z positions until a useable profile is obtained, i.e. there are no vacant spots in the location of maximum wear.
The surface data is collected in terms of x, y, and z coordinates. This data is manipulated in MatLab software in order to: a) transform the data from the CMM coordinates into the die coordinate system, and b) overcome sample imperfections that would distort the worn dies surface profile (See Appendix F).
Points are then selected along the inner profile of the die in a direction normal to the original die surface, and used as the benchmark for die wear. This methodology establishes the actual die wear profile that has to be input in the wear model. Figure 5.4 and Figure 5.5 present the wear profile at the third extrusion in terms of depth for Die A and Die B.
Figure 5.4 Wear measurement of Die A. Die life n= 2,200 pieces
5.4 Process FE model
The forming operation is modeled as an axisymmetric process in DEFORM-2D™
code. The main goal is to collect the necessary information from the simulations and apply them to the wear model.
All data related to cycle time, billet temperatures, and forging loads is gathered during a production run and will be input to the simulation databases. The forging load for the first extrusion stage is found to be 250 tons as read from the press data acquisition system. The dies enter service at room temperature. The FE analysis encompasses the forging process only, with the die heating occurring only through heat transfer during deformation.
The FE model consists of 7 objects: billet, punch, top die, top container, extrude die, extrude container, and bottom die as seen Figure 5.6. The FE simulation strategy to determine the interface conditions splits the forward extrusion stage into several discrete parts (tf-t0), as shown in Figure 5.3, to account for die chill time, deformation, dwell time and lubrication spray times, and are summarized in Table 5.1.
All the material properties information is input into the model, including: elastic, thermal, plastic and hardness data. The billet flow stress behavior is included in the model in tabular data format
σ ( ε , ε & , T )
. This method is highly recommended due to its ability to follow the true behavior of a material. It is required to enter the values of effective strainε
, effective strain rateε &
, andtemperature T for which the user has flow stress values. Appendix B describes the flow stress model considered for the billet material. Lubrication, thermal and friction sensitivity analysis were conducted prior to define the process conditions to be input into DEFORM-2DTM (See Appendix C).
Figure 5.6 Objects Set up in DEFORM-2D TM
Table 5.1 Simulations Set Up
Operation Simulation Controls
Thermal Parameters (N/sec/mm/C°)
Deformation Parameters Die
Assembly
600 steps @ 0.020535 mm/step
[12.321 mm]
Room Temperature 0.12 shear friction Part
Location
140 steps @ 0.1
sec/step [14 sec] 0.02 Convection None
Die Chill
20 steps @
0.0575 sec/step 1 (billet - Inner dies)
None Punch
Billet
Top die
Top container
Extrude die container
Bottom Die Extrude die
axisymmetry
Deformation
400 steps @ 0.2313 mm/step
[92.52 mm]
11 (billet – inner dies) 11 (inner dies – containers)
0.25 shear friction
Dwell
50 steps @ 0.00952 sec/step
[0.812 sec]
1 (billet – inner dies relation)
11 (inner dies – containers) None Pre-Spray
Dwell
40 steps @ 0.04745 sec/step
[1.898 sec]
0.02 (environment – inner dies)
11 inner dies – containers
None
Spray 20 steps @ 0.05 sec/step [1 sec]
35(lubricant – inner dies)
11 (inner dies – containers) None
Spray Dwell 30 steps @ 0.05 sec/step [1.5 sec]
0.02 (environment – inner dies)
11 (inner dies – containers)
None
5.4.1 Parameter determination
As stated in chapter 3, die wear is a function of die hardness, sliding velocity, and normal pressure. Nevertheless, in a warm forging process, temperature obviously influences on the behavior of some process parameters, e.g. pressure, velocity and flow stress. Simulations are conducted under steady state conditions, i.e. running several forging strokes simulations in order to reach stable process conditions and surpass the start-up environment. Thus, the effect of temperature on the parameters is considered and the process is analyzed in steady-state production conditions.
Figure 5.7, Figure 5.8 and Figure 5.9 display the simulation results along the three reductions on the die surface. From these figures, it can be seen that maximum normal pressure occurs at the first reduction since it presents the greatest amount of cross sectional reduction and because it is exposed to elevated temperature the longest duration. For these reasons, the first reduction is a potential location of extreme wear.
Figure 5.7: Simulation results showing sliding velocity values at 1st, 2nd and 3rd reductions
Figure 5.8: Simulation results showing contact pressure at 1st, 2nd and 3rd reductions
Figure 5.9 Simulation results showing the factor Normal Pressure times Sliding Velocity at 1st, 2nd, and 3rd reductions
The second reduction experiences the highest sliding velocity, an important amount of wear can be expected to happen in this part of the insert. However, if the factor normal contact pressure times sliding velocity (P · V) is considered as seen in Figure 5.9 (this factor influences greatly on the proposed wear model since it is directly proportional to the estimated wear depth), then the third reduction will tend to present the maximum amount of wear.
From the FEA results exposed above, DEFORM-2D™ predicts the maximum amount of die wear in two probable locations: first (based on high pressure, Figure 5.8) and/or third reduction (based on high p·v factor, Figure 5.9).
However, production trials show that the maximum wear occurs in the third reduction. For this reason, only the bottom-most 20 mm of the die, where the third reduction is localized, is analyzed. The gathered data of this selected die surface is discretized by DEFORM-2D™ length-wise (20 points along the bottom
most 20 millimeters) and time-wise (forging stroke contact time) as seen in Appendix D.
5.5 Wear model application and calibration
The methodology exposed in 4.2 is now applied for wear prediction. A forging stroke was analyzed through FEM in 5.4.1. In addition, in 5.3 production trials were run with MHSS dies named Die A and replication Die B. In order to estimate wear coefficient K and calibrate the model, the methodology is applied on Die A initially. Replication die B will be used for verification.
Die life n, hardness variation equations, and wear depth profile Zj are experimentally obtained as described in 5.3. For Die A die life n is 2,200 pieces and for replication Die B die life n is 1,800 pieces. The hardness variation equations are given in Eq. (5.2) and Eq. (5.3).
Die wear measurements and FEA concluded that the critical abrasive wear takes place at the third reduction. For this reason, only the bottom most 20 millimeters of the extrude insert are discretized into 20 points. The FE code DEFORM-2DTM provides the values of normal pressure and sliding velocity time-wise (contact time) and length-wise (discretized die surface) under steady state conditions.
The contact time ∆t in which the billet contacts the bottom most two centimeters of the extrude insert has to be calculated by the kinematics of the press. This contact time equals to 0.059 seconds. Finally, the experimental wear depth Zj, estimated through the CMM machine and MatLab, is discretized (20 points) and input into the model.
The two criteria proposed for the model: the Average Approach and the Maximum Approach, can now be applied with the values collected from DEFORM-2DTM. Upon inserting all the necessary parameters, coefficient K is determined and both approaches are compared in Figure 5.10.
Figure 5.10: Wear model calibration with die A. Die Life= 2,200 pieces
The K value for the average approach is found to be 9.6194·10-04. On the other hand, the K value for the maximum approach is 1.8·10-03. Both criteria are compared to select the best fitting K coefficient, i.e., shows the smaller error percentage with respect to the real wear depth. The predictions are compared to the actual wear profile measuring the depth error in the normal direction of the die surface. The average approach has a 7.33% error and the maximum approach shows a 9.96% error. From these results, the average approach is closer to the real wear profile and was used for the rest of the study. However, the maximum approach is also a valid option since the error percentage is under 10%.
Now that the wear coefficient K has been estimated, it can be finetuned by modifications to constants
a, b
andc
as stated previously. In Table 5.1 it isobserved that the wear model is highly sensitive to any change in the constants, especially to constants
a
andb
. The closera
andb
remain to the unity, the better fit is found in the model predictions. In the case ofc
, the wider range used [0.5- 2.5] affects the prediction but not as drastically asa
andb
. From Table 5.1, it can be concluded that the wear constants should remain in the original value (a
=1,b
=1,c
=2).Table 5.2. Model Fine-tuning
a b c
% Error Real Wear 1 1 2 7.33% (case study)1.05 1.05 2 50%
0.95 0.95 2 48%
1 0.95 2 31.80%
0.99 0.99 2 15%
1 1 2.3 40%
5.6 Wear model verification
The calculated average K value needs to be verified by corroborating it with methodology application on replication Die B. The acceptance range will be under a 15% error between the real wear depth for replication Die B and the resulting prediction with the estimated K coefficient under the average approach.
Since the forging process is totally automated, the physical parameters remain the same. Die life n for the replication die changes according to the information obtained from trials. The hardness variation equation Eq. (5.3) introduced into the model was obtained in 5.3. Figure 5.11 shows the die wear prediction applying the K value on Die B, the error between real wear profile and prediction is around 7%. Therefore, the average K coefficient can be used to predict wear on this material for similar extrusion processes.
Figure 5.11: Wear model verification with replication die B. Die Life= 1,800 pieces
5.7 Wear model application for process improvement
Once the die wear model is calibrated and verified, an expanded scope of application can be considered. From the proposed model, it was observed that the operation parameters affect directly the die wear behavior, (i.e. normal pressure, sliding velocity and contact time). Being aware of these relationships provides the capacity to influence on die life by manipulating the physical parameters, i.e. resetting the press machine.
The wear prediction study shown in previous sections was carried out with the original press speed configuration of 50 spm (strokes per minute). The objectives of this second part of the study are: a) to analyze through FEA the die wear behavior under a slower press speed setting and b) to compare such prediction with the original press set up prediction conducted on this study. Simulations are
conducted in DEFORM-2DTM with mechanical press speed of 30 spm and 40 spm, in order to compare the results with the 50 spm original set up.
From Figure 5.12, it can be observed that sliding velocities decrease drastically in the slower press set ups, and these set ups (30 spm and 40 spm) show similar sliding velocities. On the other hand, in Figure 5.13 normal pressures show a similar behavior in the three press speeds set-ups, although the 30 spm simulation remains to present the highest normal pressure. It is important to note that contact time is highly affected by the press speed changes, influencing on the results shown in Figure 5.12 and Figure 5.13. Contact times obtained from simulations press kinematics are: 0.12136 seconds for 30 spm and 0.08706 seconds for 40 spm.
Figure 5.12 Sliding Velocities Behavior for 30 spm, 40 spm, and 50 spm
Figure 5.13 Contact Pressure Behavior for 30 spm, 40 spm and 50 spm
Table 5.3 shows the die wear depth predictions for the three press speed simulations. It can be seen that die performance is improved in the 40 spm configuration, but die life decreases in the 30 spm set up. Contact time is the parameter that influences the most in the predictions. Longer contact time reduces sliding velocity and therefore benefits the die life. However, if contact time is inappropriately long, then the factor ∆t in the wear model affects the heat transfer and consequently die life. The mechanical press works intermittently at 50 spm, meaning that a change to a lower press speed would not represent a dramatic modification and production would not be compromised. Unfortunately die life improvement, when a 40 spm set up is used, is not significant.
Table 5.3 Estimated Die Wear for 30 spm, 40 spm and 50 spm for Die A
Press Speed (strokes per minute) Estimated Wear Depth (mm)
30 3.8361
40 3.0770
50 (case study) 3.5358
6 SUMMARY AND CONCLUSIONS
6.1 Summary
A methodology to predict abrasive wear based on the studies by Archard and Painter was proposed for a warm forging process. This study was conducted through FEA, a wear model, and experimental data. Through the methodology proposed:
• A wear coefficient K was estimated for the die material for this forward extrusion operation.
• Two criteria for data input into the model were considered: average and maximum approaches. The average approach provides a better prediction.
• A model fine-tuning was conducted on constants
a, b
andc
; concluding that these constants should remain in their original value.6.2 Contributions
The research objective set forth in this study is intended to be of great importance to forging processes, mainly forward extrusions. The impact of this research study as contribution to industrial forging companies can be summarized as follows:
• A methodology for die wear prediction on warm forging processes.
• For the case study, a wear coefficient K for this die material that can be used to predict wear in a forward extrusion process with similar geometry.
• Determination of the die-workpiece interface conditions at steady-state by considering multiple cycles.
• Cooperation between industry and university to develop and apply accurate and practical methods to predict and estimate die life to improve the competitiveness of the forging industry.
6.3 Conclusions
Predicting die wear is feasible by utilizing a methodology that combines: Finite Element Analysis (FEA) and wear models.
FEA does not only encompass a proper geometric modeling and a process configuration. Parameters such as friction and heat transfer coefficient, lubrication, convection with environment etc., play a definitive role in the simulation results. All of these parameters have to be carefully defined by calibration.
Trends in recent wear models establish Archard studies as the fundamental base for any developments in the subject. Nonetheless, warm forging defines challenges in temperature effect considerations, mainly in hardness.
The information that was obtained directly in a forging production line and that later was input in the wear model demonstrates that cooperation industry – university can lead to benefits for both parties. Specifically in the case of the students involved, the present work led to a better understanding of how advanced research can be applied to manufacturing processes.
Overall, the proposed methodology was successfully applied in a case study;
therefore it can be considered for further predictions. However, there are the following limitations: a) some parameters that influence on die wear are still to be studied and be included in the model, b) estimated wear coefficient K can only be applied to similar geometry processes.
Finally, a company will be able to select, by means of the methodology proposed, which die material will perform best, i.e., undergo the least amount of die wear.
Reconfiguration of actual process parameters, i.e., change mechanical press setting, can lead to a better die performance. In that way, forging companies will decrease costs and improve profitability and productivity.
6.4 Future work
Future work on this study should be focused on investigating those parameters that influence die wear and were out of the scope of the present work.
• Die hardness considerations: Currently, the hardness of the die material is expressed only as a function of forging cycles. The tempering effect (thermal softening) is not taken into account in the analysis mainly due to the temperature range of warm forging. However, in order to accurately forecast die life, the tempering curves for the die material need to be included in the analysis.
• Die geometry updating: Updating the tool geometry is not factored into the current wear prediction analysis since only one simulation is conducted and wear values are scaled linearly to predict die wear and die life after multiple forging cycles. Step-wise simulations updating worn surface (FEM) are suggested.
• Surface roughness: The surface roughness is closely related to the tribological conditions and affects the forging process parameters, e.g.
filling of the material along the valleys of tool surface roughness (full contact condition) becomes difficult with the increase of sliding velocity.
Under this condition, the pressure difference in the valley of surface roughness increases, so that the apparent coefficient of friction increases [Saiki et al., 2003].
