Multi-objective Optimization of WEDM Process Parameters Using NSGA-II Algorithm

2020 ◽  
pp. 405-411
Author(s):  
A. Tamilarasan ◽  
G. Sriram ◽  
S. Arumugam ◽  
D. Vijayan ◽  
D. Rajamani ◽  
...  
Keyword(s):  
Process Parameters ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Wedm Process

scholarly journals Multi-Objective Optimization of Process Parameters of Longitudinal Axial Threshing Cylinder for Frozen Corn Using RSM and NSGA-II

2020 ◽  
Vol 10 (5) ◽  
pp. 1646 ◽  
Author(s):  
Jun Fu ◽  
Haikuo Yuan ◽  
Depeng Zhang ◽  
Zhi Chen ◽  
Luquan Ren
Keyword(s):  
Process Parameters ◽  
Feed Rate ◽  
Loss Rate ◽  
Optimization Method ◽  
Operational Performance ◽  
Multi Objective Optimization ◽  
Optimal Process ◽  
Nsga Ii ◽  
Multi Objective ◽  
Damage Rate

Corn was frozen at harvest time in high-latitude areas, when corn kernel is wetter and more easily broken. When frozen corn was threshed and separated by the longitudinal axial threshing cylinder of a combine harvester, it caused a significantly high kernel damage rate and loss rate. The process parameters of threshing cylinder were optimized using RSM (response surface method) and NSGA-II (Non-Dominated Sorted Genetic Algorithm-II). The drum speed (Ds), feed rate (Fr) and concave clearance (Cc) were determined as the optimized process parameters. The loss rate (Lr) and damage rate (Dr) were indicators of operational performance. The RSM was used to establish a mathematical model between process parameters and indicators. With an elite strategy, NSGA-II was used for multi-objective optimization to obtain the optimal operational performance of the threshing cylinder. Overall, when the drum speed was selected as 384.1 rpm, the feed rate as 8.6 kg/s and the concave clearance as 40.5 mm, according to the requirement of corn harvest, the best operational performance of the longitudinal axial threshing cylinder on frozen corn was obtained. The Lr was 1.98% and the Dr was 3.49%. This result indicated that the applicability of the optimal process parameters and the optimization method of combining NSGA-II and RSM was effective for determining the optimal process parameters. This will provide an optimization method for synchronously reducing the loss rate and damage rate of grain harvesters.

scholarly journals Numerical Simulation and Multi-Objective Optimization of Partition Cooling in Hot Stamping of the Automotive B-Pillar Based on RSM and NSGA-II

Metals ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1264 ◽  
Author(s):  
Maomao Cui ◽  
Zhao Wang ◽  
Leigang Wang ◽  
Yao Huang
Keyword(s):  
Tensile Strength ◽  
Process Parameters ◽  
Hot Stamping ◽  
Cooling Zone ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Slow Cooling ◽  
Rapid Cooling ◽  
Multi Objective ◽  
Thinning Rate

In this study, the simulation and optimization of the partition cooling in the hot stamping process was carried out for an automotive B-pillar through minimizing the maximum thickening rate and the maximum thinning rate located in the rapid and slow cooling zones. The optimization was implemented by investigating the process parameters such as friction coefficient, sheet austenitizing temperature, holding time, heating zone temperature, the upper binder force and the lower binder force. The optimal Latin hypercube design (OLHD), the response surface methodology (RSM) and the non-dominated sorting genetic algorithm (NSGA-II) were combined to establish the relationship between process parameters and form quality objectives. After multi-objective optimization, the maximum thickening rate and the maximum thinning rate of the slow cooling zone and rapid cooling zone were 11.1% and 12.4%, 4.7% and 7.1%, respectively. Afterwards, the simulation was performed according to the optimized parameter combinations to analyze the temperature field, microstructure, tensile strength, hardness, thickening rate and thinning rate, and forming quality. Moreover, the hot stamping test and experimental results showed that the microstructure of the ferrite and pearlite structure was uniformly distributed in the slow cooling zone, and its tensile strength reached 680 MPa, the elongation was 11.4% and the hardness was 230.56 HV, while the lath martensite structure was obtained in the rapid cooling zone, with tensile strength of up to 1390 MPa, elongation of about 7.0% and hardness reaching 478.78 HV. The results of thickness, microstructure, tensile strength and the hardness test correspond well with the simulation results.

Synchronous R-NSGA-II: An Extended Preference-Based Evolutionary Algorithm for Multi-Objective Optimization

Informatica ◽  
2015 ◽  
Vol 26 (1) ◽  
pp. 33-50 ◽  
Author(s):  
Ernestas Filatovas ◽  
Olga Kurasova ◽  
Karthik Sindhya
Keyword(s):  
Evolutionary Algorithm ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective

scholarly journals Multi-Objective Lightweight Optimization of Parameterized Suspension Components Based on NSGA-II Algorithm Coupling with Surrogate Model

Machines ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 107
Author(s):  
Rongchao Jiang ◽  
Zhenchao Jin ◽  
Dawei Liu ◽  
Dengfeng Wang
Keyword(s):  
Ride Comfort ◽  
Lightweight Design ◽  
Dynamic Performance ◽  
Vehicle Dynamic ◽  
Multi Objective Optimization ◽  
Original Design ◽  
Dynamic Simulations ◽  
Nsga Ii ◽  
Torsion Beam ◽  
Multi Objective

In order to reduce the negative effect of lightweighting of suspension components on vehicle dynamic performance, the control arm and torsion beam widely used in front and rear suspensions were taken as research objects for studying the lightweight design method of suspension components. Mesh morphing technology was employed to define design variables. Meanwhile, the rigid–flexible coupling vehicle model with flexible control arm and torsion beam was built for vehicle dynamic simulations. The total weight of control arm and torsion beam was taken as optimization objective, as well as ride comfort and handling stability performance indexes. In addition, the fatigue life, stiffness, and modal frequency of control arm and torsion beam were taken as the constraints. Then, Kriging model and NSGA-II were adopted to perform the multi-objective optimization of control arm and torsion beam for determining the lightweight scheme. By comparing the optimized and original design, it indicates that the weight of the optimized control arm and torsion beam are reduced 0.505 kg and 1.189 kg, respectively, while structural performance and vehicle performance satisfy the design requirement. The proposed multi-objective optimization method achieves a remarkable mass reduction, and proves to be feasible and effective for lightweight design of suspension components.

scholarly journals Design and characterization of effective solar cells

2021 ◽  
Author(s):  
Varun Ojha ◽  
Giorgio Jansen ◽  
Andrea Patanè ◽  
Antonino La Magna ◽  
Vittorio Romano ◽  
...  
Keyword(s):  
Solar Cells ◽  
Quantum Efficiency ◽  
Cost Benefit Analysis ◽  
Cost Minimization ◽  
Cell Structure ◽  
Cost Benefit ◽  
Structure Design ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective

AbstractWe propose a two-stage multi-objective optimization framework for full scheme solar cell structure design and characterization, cost minimization and quantum efficiency maximization. We evaluated structures of 15 different cell designs simulated by varying material types and photodiode doping strategies. At first, non-dominated sorting genetic algorithm II (NSGA-II) produced Pareto-optimal-solutions sets for respective cell designs. Then, on investigating quantum efficiencies of all cell designs produced by NSGA-II, we applied a new multi-objective optimization algorithm II (OptIA-II) to discover the Pareto fronts of select (three) best cell designs. Our designed OptIA-II algorithm improved the quantum efficiencies of all select cell designs and reduced their fabrication costs. We observed that the cell design comprising an optimally doped zinc-oxide-based transparent conductive oxide (TCO) layer and rough silver back reflector (BR) offered a quantum efficiency ($$Q_e$$ Q e ) of 0.6031. Overall, this paper provides a full characterization of cell structure designs. It derives relationship between quantum efficiency, $$Q_e$$ Q e of a cell with its TCO layer’s doping methods and TCO and BR layer’s material types. Our solar cells design characterization enables us to perform a cost-benefit analysis of solar cells usage in real-world applications.

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