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AFDEX와 HyperStudy를이용한베어링단조공정의소성유동선최적설계
발표자 : ㈜엠에프알씨정석환
Optimal design of grain flow lines in forging processfor bearing using AFDEX and HyperStudy
Contents
1. AFDEX 소개
2. 단조 공정 관심사항 및 설계 변수
3. 단조공정 최적화 사례 소개
4. 단조 공정 최적 설계 문제점 및 대응 방안
5. 단류선의 중요성 및 정량화 방안
6. 단류선 최적 설계 적용 사례
7. 결론 및 향후 계획
1. AFDEX 소개
- 소성가공 공정해석 CAE SW- 빠른 계산시간, 결과의 정확성, 쉬운 사용환경과 요소망 재구성 기술- 2D, 3D 유동해석, 금형구조해석, 열전달해석 기능 제공- 단조, 인발, 압출, 압연 등 체적소성가공 및 판단조 성형해석 가능
SW 해석분야
소성가공 해석
- 유동해석- 금형구조해석- 열전달 연계해석- 탄성변형고려해석
결함, 미성형예측
- 소성유동선- 결육- 손상도
금형, 하중예측
- 금형 하중- 금형 마모- 금형 변형- 금형 수명
타SW와 연계해석
- 잔류응력(탄소성)- 온도(비등온)
1. AFDEX 소개
Pre-processor
Input data
Output data
Finite Element Solver
Post-processor
CAD DXF, STL
Law of nature Governing equations Finite element equations Derived variables
Newton’s law
of motion
Algebraic equation
(Velocity, Hydrostatic pressure)
Deformation,
Forming load, Stress,
Strain rate, Effective
strain, Plastic flow
lines, etc.
Law of energy
conservationEquation of heat
conduction
Algebraic equation
( Temperature rate )
Law of mass
conservation
Metallurgical-Microstructure
-Heat treatment
Mechanical
ThermalTemperature,
Heat flux
Equation of
equilibrium
Life time prediction
Process design optimization
Equation of
continuity
Rigid or elasto-
thermoviscoplastic FEM
Material and dies
can be coupled
Altair APA
Maxwell equation
Induction heating
1. AFDEX 소개 - Accuracy
1. AFDEX 소개 – Advanced functions
0.58 mm0.50 mm
Springback
1. AFDEX 소개 – Only by AFDEX
➢ Hollow Shaft : Hyundai Wia ➢ Ring rolling: KISTI
1. AFDEX 소개 – Metal flow lines
B
Section B
Stage 1
Stage 2
Stage 3
Arbitrary cross-section
5.5
1.0
0.4
2.2
1. AFDEX 소개 – Metal flow lines
1. AFDEX 소개 – Development strategy
Parts Design
Process/DieConceptual design
MF simulation
Detail design/CAM
EDM
Die evaluation
Try-out
High speedmachining
Heat treatmentMicrostructure
Dimension check
Performance test(Dynamic, static)
This loop is very verycostly and time-consuming.
Life-span assessmentForming loadGrain flow
Minimized process design failure
Minimized parts development cycle and cost
Metal forming industry
Steel makerMotors company
Electronic device co.Engineering co.Aerospace co.
Plant co.Construction mach. Parts and material
Metal forming…
ALTAIR
Weight minimizationForgeability
Metallo-mechanicaloptimization
Part design
2. 단조공정 관심 사항 및 설계 변수
Damage
Internal crack
Optimized
Non-optimized
Metal flow line Scrap
Crack Wear and fracture of die
Under filling / Overlapping
2. 단조공정 관심 사항 및 설계 변수
Workpiece dimension
Engineering strain (mm/mm)
En
gin
ee
rin
gstr
ess
(MP
a)
0 0.1 0.2 0.3 0.40
200
400
600
800
1000
1200
Experiment (SCM435)
Analysis (SCM435)
Experiment (ESW95)
Analysis (ESW95)
Experiment (ESW105)
Analysis (ESW105)
Material and temperature
Air hole (Air pocketing)
Die shape and stroke
Stage 1 Stage 2
Assigned force/pressure
3. 단조공정 최적화 사례 소개 - 1
✓Objective: Maximum load of finisher process
✓Design variables and their constraints and initial designs:-Radius of Point 1: 2~13 mm (0.5 mm incremental), Initial value: 6.0
-Radius of Point 2: 2~8.5 mm (0.5 mm incremental), Initial value: 4.0 mm
-Initial forming loads: 1800 ton for blocker + 2030 ton for finisher
✓Constraints: -forming load in the blocker process < 1600 ton
✓Optimization schemes-Algorithm: GRSM (Global Response Surface Method)
-Maximum repetition number of analysis: 100 times
-Initial sample points: 4 points
✓Optimal design: -Radii of point 1 and point 2: 11 and 7.5 mm, respectively
-Forming load in blocker and finisher stages: 1595 and 1589 tons, respectively
-Forming loads versus iteration
Design variable: Radii of point 1 and 2
FinisherBlockerInitial
✓ Process definition:
Number of iterations
-2-stage hot forging, isothermal, SCr420H
-Press: 2500 ton, mechanical
-Friction: μ = 0.2
MFCAE 2016.
3. 단조공정 최적화 사례 소개 - 2
✓ Material: Elastoplastic for materials, rigid for dies
✓ 2D(Axisymmetric)
Summary of simulation
2D – Joining process
2D – Pulling test
Die
FixedDie
Fitting
Tube
3. 단조공정 최적화 사례 소개 - 2
𝜃1
𝜃2
ℎ
𝑃1(𝑥, 𝑦1)
𝑃2(𝑥, 𝑦2)
Description of variables
𝜃1: 30° ~ 75° 𝜃2: 30° ~ 85°
𝑃1: (𝑥, 𝑦1 + ℎ ∙ 𝑡𝑎𝑛𝜃1) 𝑃2: (𝑥, 𝑦2 − ℎ ∙ 𝑡𝑎𝑛𝜃2)
Range of variables 𝜃1 and 𝜃2
Objective: Pulling force
Maximum load
Optimal angles:
Optimal design
𝜃1 = 55°
𝜃2 = 70°
0.96 𝑡𝑜𝑛→ 1.54 𝑡𝑜𝑛
3. 단조공정 최적화 사례 소개 - 3
✓ Process definition: Clinching process
− 2-stage cold forging, isothermal
− Al6063 alloy sheet, 2.5mm thickness
− ത𝜎 = 310.83 ҧ𝜖0.057
− Friction: μ = 0.12
− Binder force – 10kN
Start of Stage 2 End of Stage 2
Animation
Note:
Stage 2 is for testing the
joint strength by pulling
the workpiece upwards
End of Stage 1Initial configuration
3. 단조공정 최적화 사례 소개 - 3
✓ Design variables:
r
w
h
xpParameter Range
Edge radius of punch [r] 0.48 – 0.60 (0.02 increments)
Radius of punch [Xp] 3.62 – 4.02 (0.1 increments)
Die height [h] 1.6 – 1.9 (0.05 increments)
Die width [w] 3.0 – 4.0 (0.1 increments)
Friction coefficient [µ] 0.08 – 0.16 (0.02 increments)
✓ Objective function:
Maximize the forming load of second stage
(Here it means, to obtain a process with higher joint strength)
✓ Optimization scheme:
Algorithm: GRSM (Global Response Surface Method)
✓ Optimal
design:
4. 단조 공정 최적 설계 문제점 및 대응 방안
✓ Absence of quantification technique of metal flow lines
✓ Lack of quantitative information regarding quality and productivity
✓ Complexity of input structure for CAE analysis
✓ Difficulty in parameterizing 3 dimensional die shape
Depending on the process designer’s experience
Main concerns in designing forging processDesign variables in forging process
4. 단조 공정 최적 설계 문제점 및 대응 방안
Optimization Template
AFDEX
✓ Development of optimization template
Reinforcement of connectivity with commercial optimization S/W
Development of practical object functionConvenience in assigning design variable5.5
1.0
0.4
2.2
5. 단류선의 중요성 및 정량화 방안
Ito, S., Tusuhima, N., Muro, H., “Accelerated rolling.
Contact fatigue test by a cylinder-To-Ball Rig”,
Rolling contact fatigue testing of bearing steels,
ASTM STP 771, J, J, C. Hoo, Ed., American society
for testing and materials, 1982, pp. 125~135
✓ MFL vs. life cycle ✓ Optimizations for MFL
1st generation hub bearing outer racer
MFL cutting / asymmetry
MFL cutting / macrosegragation
Double taper bearing for transmission
5. 단류선의 중요성 및 정량화 방안
5.5
1.0
0.4
2.2
Grain flow
Effective strainOverlapping
indexGrain flow
density
Effective strain has
nothing to do with grain
flow overlappingHigh correlation
MFL function, its gradient, overlapping index, etc. can be used as objective
or constraint functions in the process design optimization.
𝐺𝑝𝑞𝑖 =
𝜕2𝜙𝑖
𝜕𝑥𝑝𝜕𝑥𝑞
𝛻𝜙𝑖
𝜙𝑖 = constant
𝐺𝑖 =2
3𝐺𝑝𝑞𝑖 ′
𝐺𝑝𝑞𝑖 ′
✓ MFL Overlapping index
𝛻𝜙𝑖
normal direction of product surface
𝒏 MFL cutting index = 𝛻∅𝑖 𝑠𝑖𝑛𝜃
※ MFL cutting index increasing condition- As the crossing angle between MFL and product surface gets larger
- As MFL density gets larger
𝛻𝜙𝑖𝜙𝑖 = constant
✓ MFL Cutting index
5. 단류선의 중요성 및 정량화 방안
𝜃
5. 단류선의 중요성 및 정량화 방안
Product shape
Region of Interest
Calculation of object function
Effective strainDamageMFL overlapping indexMFL cutting index
MaximumAverageDeviationVolume weight averageVolume weight deviation
Product shape cutting
✓ Calculation of object function
6. 단류선 최적 설계 적용 사례 - Symmetry
Upsetting Finisher
✓ Hub bearing outer racer forging ✓ Design variable
✓ Object function
Difference between MFL
function values at blue
and red points
Stroke
Param. Initial Range
v1 -100 -500~0
Stroke 12 10~15
Time (s)
Die2
✓ Optimal design
Time (s)
Optimized die velocity profile Optimal MFL
6. 단류선 최적 설계 적용 사례 – Cutting Index
Upsetting FinisherBlocker
✓ Double taper bearing forging
✓ MFL cutting on the sliding surface
MFL cutting
✓ Design variable
𝑃2𝑥
𝑃3𝑥
𝑃2𝑥, 𝑃3𝑥, 𝑑𝑦
𝑑𝑦
-blocker punch
-blocker die
6. 단류선 최적 설계 적용 사례 - Cutting Index
✓ Object function
A
B
Product shape cutting
Minimization of cutting index in A and B
✓ Optimization
Initial design Optimal design
Blocker process
✓ Optimization result
23.4 4.1
Param. Initial Optimal value
𝑃2𝑥 45.0 50.0
𝑃3𝑥 54.0 65.0
𝑑2 0.0 0.0
Object function
7. 결론 및 향후 계획
✓ The way to quantify MFL quality was studied.
✓ Several applications to optimize MFL were introduced.
✓ Development of the parameterization technique of 3D die shape
will be carried out.
✓ Additional practical objective functions in terms of product quality
and cost will be developed.
✓ Template for the process optimal design will be developed.
☞ Enhancement of process optimization capability for
the forging process designers.
