1. OPTIMAL DESIGN OF CLUTCH FRICTION PAD
MAE 598: DESIGN OPTIMIZATION
OPTIMAL DESIGN OF CLUTCH FRICTION PAD
Presented by:
VELUGUBANTLA SRINIVASA MURALIKRISHNA
ADARSH VENKITESWARAN
VARUN SUBRAMONIAM

2. PARAMETRIC OPTIMIZATION OF A CLUTCH SYSTEM
Problem statement
Design a rigid drive clutch system that meets multiple objectives such as Vibrational rigidity, Structural and Thermal strength. To demonstrate a systematic approach to solving multi-objective problems by approximating multiple Pareto optimal solutions by Response Surface Optimization.
Courtesy: google images
Modal Analysis1
Structural Analysis2
Thermal Analysis3
Minimize Temperature
Maximize Natural Frequency
Minimize Equivalent Stress
1 Velugubantla Srinivasa MuraliKrishna, 2 Adarsh, 3 Varun

3. SUB-SYSTEM OPTMIZATION METHODOLOGY
Parameter set
Constraint set
g1: 140 ≤ P1 ≤ 160
g2 : 2 ≤ P2 ≤ 10
g3: 0.5 ≤ P3 ≤ 3.5
Friction Lining Parameter set
P1- Inner Dia
P2- Pad thk
P3 – Pad Facing thk P1 P2 P3
Parametric Model
Modal Analysis
Structural Analysis
Thermal Analysis
Design Of Experiments
Latin Hyper Cube Sampling
Metamodeling
Standard Response surface-(Full 2nd order polynomial)
Response Surface Optimization
Non Linear Programming by Quadratic Lagrangian Method (NLPQL)
Maximize
Natural Frequency
Minimize
Equivalent Stress
Temperature

4. MODAL ANALYSIS Objective
Maximize the natural frequency of clutch assembly f = (P1,P2,P3) to avoid resonance with Engine and Transmission systems
Influence of parameter values
Initial design
Robustness of solution (Goodness of fit)
Final optimized design
31.5 % improvement
Parameter
Starting Point
Final Design
P1 (mm)
160
140 (active)
P2 (mm)
2.7
2 (active)
P3 (mm)
0.8
0.5 (active)
Output
Initial Value
Optimized Value
Simulated Value
Frequency (hz) 42
54.2
55.26

5. STRUCTURAL ANALYSIS Objective
Minimize the equivalent stress acting on the friction pad σeq = (P1,P2,P3) to avoid failure during dynamic loading
Influence of parameter values
Initial design
Robustness of solution (Goodness of fit)
Final optimized design
27.16 % improvement
Parameter
Starting Point
Final Design
P1 (mm)
160
160 (active)
P2 (mm)
2.7
10 (active)
P3 (mm)
0.8
2.52
Output
Initial Value
Optimized Value
Simulated Value
Stress(MPa)
0.254
0.18
0.185

6. THERMAL ANALYSIS Objective
Minimize the temperature generated on the friction pad T= (P1,P2,P3)
Influence of parameter values
Initial design
Final optimized design
Robustness of solution (Goodness of fit)
31.3 % improvement
Parameter
Starting Point
Final Design
P1 (mm)
160
150
P2 (mm)
2.7 6
P3 (mm)
0.8
3.5 (active)
Output
Initial Value
Optimized Value
Simulated Value
Temperature(ᵒC)
109.9
76.79
75.46

7. COMPLETE SYSTEM OPTMIZATION METHODOLOGY
Multi objective Genetic Algorithm Method(MOGA)
Single Objective optimization
(Other two objectives are constraints)
Non Linear Programming by Quadratic
Lagrangian Method (NLPQL)
Pareto Optimal Surface
Pareto Optimal Surface

8. RESULTS COMPARISON (Pareto Surface)
Multi-objective (MOGA)
Single-objective (NLPQL)
MOGA Algorithm
Hybrid variant of popular NSGA-II ( Non- dominant Sorted Genetic Algorithm)
Used for continuous problems
Constraint handling using same non-dominance principle. Hence, no penalty functions and Lagrange multipliers needed
NLPQL Algorithm
Generates a sequence of QP sub-problems obtained by quadratic approx. of Lagrangian function and Linearization of constraints.
Second order information is updated by Quasi-Newton formula method and stabilized by an Armijo line search

9. Lessons Learnt System based optimization
Different methods used for sampling in DOE
Comparison of various Optimization Algorithms
Response Surface metamodeling.
Active constraint identification and monotonicity
Pareto Surface and regression analysis