A multi-hierarchical successive optimization method for reduction of spring-back in autoclave forming

2018 ◽  
Vol 188 ◽  
pp. 143-158 ◽  
Author(s):  
Hu Wang ◽  
Lei Chen ◽  
Fan Ye ◽  
Jian Wang
Keyword(s):  
Optimization Method ◽  
Spring Back ◽  
Successive Optimization

Optimization of Fixture and Joint Positions in Sheet Metal Assembly: The Effect of Fixture Numbers and Constraints

2011 ◽  
Author(s):  
Kambiz Haji Hajikolaei ◽  
G. Gary Wang
Keyword(s):  
Sheet Metal ◽  
Metal Forming ◽  
Optimization Method ◽  
Stochastic Methods ◽  
Objective Functions ◽  
Variation Analysis ◽  
Spring Back ◽  
Final Assembly ◽  
Deterministic Methods ◽  
Sheet Metal Assembly

Assembly process is widely used in the manufacturing processes. Fabrication processes such as machining, casting and metal forming are not perfect and introduce variation in the components. Variations of components and tools accumulate and cause the assembly variation. In this paper, after reviewing the literature and presenting sheet metal assembly variation analysis, an optimization method is used to minimize the assembly variation by optimizing the location of joints and fixtures. The model is constructed in ANSYS with three fixtures and two joints. When a black-box function calculated numerically in software is used as the objective function, using deterministic methods for optimization is not suitable because the deterministic methods need knowledge of the objective functions. Also, using stochastic methods such as genetic algorithm is not suitable because of the large number of function evaluations they normally need. In this paper, an optimization algorithm based on mode-pursuing sampling (MPS) method is used to minimize the assembly variation. The optimization method is explained and after implementing the method, results are presented. It is learned that, in addition to the number of fixtures, the constraints on neighboring fixture locations also affect the optimal fixture layout, as well as the final assembly stiffness and spring-back.

Application of the successive optimization method in tuning the natural vibration frequency of GTE compressor blades

1982 ◽  
Vol 14 (7) ◽  
pp. 957-960
Author(s):  
A. B. Roitman ◽  
V. P. Afanas'ev ◽  
T. F. Mikhailova ◽  
S. P. Omel'chenko
Keyword(s):  
Vibration Frequency ◽  
Natural Vibration ◽  
Optimization Method ◽  
Natural Vibration Frequency ◽  
Compressor Blades ◽  
Successive Optimization

Optimization Method of Superelevation Transition for Oval Racing Tracks

CICTP 2019 ◽  
2019 ◽  
Author(s):  
Yuchen Wang ◽  
Tao Lu ◽  
Hongxing Zhao ◽  
Zhiying Bao
Keyword(s):  
Optimization Method

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Dimensioning a recovery boiler furnace using mathematical optimization

TAPPI Journal ◽  
2015 ◽  
Vol 14 (2) ◽  
pp. 119-129 ◽  
Author(s):  
VILJAMI MAAKALA ◽  
PASI MIIKKULAINEN
Keyword(s):  
Radial Basis Function Network ◽  
High Capacity ◽  
Mathematical Optimization ◽  
Optimization Procedure ◽  
Optimization Method ◽  
Performance Criteria ◽  
Boiler Furnace ◽  
Starting Point ◽  
Recovery Boiler ◽  
Base Design

Capacities of the largest new recovery boilers are steadily rising, and there is every reason to expect this trend to continue. However, the furnace designs for these large boilers have not been optimized and, in general, are based on semiheuristic rules and experience with smaller boilers. We present a multiobjective optimization code suitable for diverse optimization tasks and use it to dimension a high-capacity recovery boiler furnace. The objective was to find the furnace dimensions (width, depth, and height) that optimize eight performance criteria while satisfying additional inequality constraints. The optimization procedure was carried out in a fully automatic manner by means of the code, which is based on a genetic algorithm optimization method and a radial basis function network surrogate model. The code was coupled with a recovery boiler furnace computational fluid dynamics model that was used to obtain performance information on the individual furnace designs considered. The optimization code found numerous furnace geometries that deliver better performance than the base design, which was taken as a starting point. We propose one of these as a better design for the high-capacity recovery boiler. In particular, the proposed design reduces the number of liquor particles landing on the walls by 37%, the average carbon monoxide (CO) content at nose level by 81%, and the regions of high CO content at nose level by 78% from the values obtained with the base design. We show that optimizing the furnace design can significantly improve recovery boiler performance.

A Power Optimization Method in Large-Scale Wireless Sensor Networks Using a Two-Stage Meta-Heuristic Algorithm

2014 ◽  
Vol 2 ◽  
pp. 302-305
Author(s):  
Ryohei Sato ◽  
Hidehiro Nakano ◽  
Arata Miyauchi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Heuristic Algorithm ◽  
Large Scale ◽  
Power Optimization ◽  
Optimization Method ◽  
Wireless Sensor ◽  
Two Stage

Multi-point Combinatorial Optimization Method Based on Hierarchical Structure in Solution Space

2014 ◽  
Vol 134 (7) ◽  
pp. 999-1000 ◽  
Author(s):  
Hiroki Ochiai ◽  
Kenichi Tamura ◽  
Keiichiro Yasuda
Keyword(s):  
Combinatorial Optimization ◽  
Hierarchical Structure ◽  
Solution Space ◽  
Optimization Method ◽  
Structure In Solution
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