Optimization of Machine System Designs Based on Decomposition and Hierarchical Ordering of Criteria and Design Variables

2000 ◽  
Author(s):  
Masataka Yoshimura ◽  
Ryousuke Nomura
Keyword(s):  
Design Optimization ◽  
Optimization Methods ◽  
Optimization Method ◽  
Hierarchical Optimization ◽  
Local Optimum ◽  
Optimization Strategy ◽  
Cooperative Relationships ◽  
Design Variables ◽  
Hierarchical Ordering ◽  
Basic Characteristics

Abstract Designs of machine products routinely have so many characteristics to be evaluated that usual design optimization methods often result in an unsatisfactory local optimum solution. In order to overcome this problem, this paper proposes a design optimization method based on decomposition by substructuralization and subsequent hierarchical ordering, considering the both conflicting and cooperative relationships between the characteristics under evaluation. First of all, each characteristic is divided into simpler basic characteristics. The pool of design variables is also divided into smaller groups, according to specific design features. Next, the relationships between the basic characteristics and the divided design variables, as well as the relationships among the characteristics themselves, are systematically identified and clarified. Then, based on this clarification, and after setting a core characteristic derived from the primary performance characteristic for the product under consideration, an optimization strategy and detailed hierarchical optimization procedures are constructed. In this paper, the proposed method is applied to machine tool structures and transportation products.

Optimization of Machine System Designs Using Hierarchical Decomposition Based on Criteria Influence

2002 ◽  
Author(s):  
Masataka Yoshimura ◽  
Kazuhiro Izui ◽  
Shigeaki Komori
Keyword(s):  
Design Optimization ◽  
Structural Model ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Hierarchical Level ◽  
Hierarchical Optimization ◽  
Pareto Optimum ◽  
Local Optimum ◽  
Optimization Strategy ◽  
Basic Characteristics

Machine product designs routinely have so many mutually related characteristics that common design optimization methods often result in an unsatisfactory local optimum solution. In order to overcome this problem, this paper proposes a design optimization method based on the clarification of the conflicting and cooperative relationships among the characteristics. First of all, each performance characteristic is divided into simpler basic characteristics according to its structure. Next, the relationships among the basic characteristics are systematically identified and clarified. Then, based on this clarification, the optimization problem is expressed using hierarchical constructions of these basic characteristics and design variables related to the most basic characteristics. Finally, an optimization strategy and detailed hierarchical optimization procedures are constructed, after clarifying the influence levels of each basic characteristic upon the objective functions and setting a core characteristic for the product under consideration. Here, optimizations are sequentially repeated starting with the basic optimal unit group at the bottom hierarchical level and proceeding to higher levels by the hierarchical genetic algorithms. Then, the Pareto optimum solutions at the top hierarchical level are obtained. With the proposed optimization methods, optimization can be more easily applied after the optimization problems have been simplified by decomposition. In doing so, the volume of design spaces for each optimization is reduced, while useful and unique rules and laws may be uncovered. The optimization strategy expressed by the hierarchical structures can be used for the optimization of similar product designs, which realize these breakthroughs, yielding improved product performances. The proposed method is applied to a machine-tool structural model.

Design Optimization of Machine-Tool Dynamics Based on Clarification of Competitive-Cooperative Relationships Between Characteristics

1987 ◽  
Vol 109 (1) ◽  
pp. 143-150 ◽  
Author(s):  
Masataka Yoshimura
Keyword(s):  
Machine Tool ◽  
Design Optimization ◽  
Structural Model ◽  
Optimization Methods ◽  
Optimization Method ◽  
Design Decision ◽  
Cooperative Relationships ◽  
Machine Tool Dynamics ◽  
Design Variables ◽  
Tool Dynamics

This paper proposes a design optimization method of machine-tool dynamics based on clarification of competitive and cooperative relationships between characteristics. Clarification of competitive and cooperative relationships between characteristics results in division of design variables into three groups. The design variables of each group are determined in each of the three-phase design optimization procedures. The design decision problem in each procedure is far simpler and easier than that in usual design optimization methods, in which all design variables are determined at the same time. The competitive and cooperative relations between characteristics are first clarified. Next, algorithmic procedures of the design optimization method are constructed. The method is demonstrated on a structural model of a milling machine.

Multi Objective Design Optimization of Two Bar Truss Using NSGA II and TOPSIS

2014 ◽  
Vol 984-985 ◽  
pp. 419-424
Author(s):  
P. Sabarinath ◽  
M.R. Thansekhar ◽  
R. Saravanan
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Nsga Ii ◽  
Multi Objective ◽  
Total Displacement ◽  
Design Variables ◽  
Conflicting Objectives

Arriving optimal solutions is one of the important tasks in engineering design. Many real-world design optimization problems involve multiple conflicting objectives. The design variables are of continuous or discrete in nature. In general, for solving Multi Objective Optimization methods weight method is preferred. In this method, all the objective functions are converted into a single objective function by assigning suitable weights to each objective functions. The main drawback lies in the selection of proper weights. Recently, evolutionary algorithms are used to find the nondominated optimal solutions called as Pareto optimal front in a single run. In recent years, Non-dominated Sorting Genetic Algorithm II (NSGA-II) finds increasing applications in solving multi objective problems comprising of conflicting objectives because of low computational requirements, elitism and parameter-less sharing approach. In this work, we propose a methodology which integrates NSGA-II and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for solving a two bar truss problem. NSGA-II searches for the Pareto set where two bar truss is evaluated in terms of minimizing the weight of the truss and minimizing the total displacement of the joint under the given load. Subsequently, TOPSIS selects the best compromise solution.

Smart Optimization of Machine Systems Using Hierarchical Genotype Representations

1999 ◽  
Author(s):  
Masataka Yoshimura ◽  
Kazuhiro Izui
Keyword(s):  
Structural Design ◽  
Hierarchical Structures ◽  
Optimization Methods ◽  
Optimization Method ◽  
Design Problems ◽  
Design Variables ◽  
The Hierarchical Structure ◽  
Machine Systems ◽  
System Designs ◽  
Crossover And Mutation

Abstract Design problems for machine products are generally hierarchically expressed. With conventional product optimization methods, it is difficult to concurrently optimize all design variables of portions within the hierarchical structure. This paper proposes a design optimization method using genetic algorithms containing hierarchical genotype representations, so that the hierarchical structures of machine system designs are exactly expressed through genotype coding, and optimization can be concurrently conducted for all of the hierarchical structures. Crossover and mutation operations for manipulating the hierarchical genotype representations are also developed. The proposed method is applied to a machine-tool structural design to demonstrate its effectiveness.

The Effect of Different Optimization Methods on Robustness for Robust Optimization Design

2014 ◽  
Vol 721 ◽  
pp. 464-467
Author(s):  
Tao Fu ◽  
Qin Zhong Gong ◽  
Da Zhen Wang
Keyword(s):  
Robust Optimization ◽  
Objective Function ◽  
Robust Design ◽  
Optimization Design ◽  
Optimization Methods ◽  
Optimization Method ◽  
Design Parameters ◽  
Hybrid Particle Swarm Optimization ◽  
Design Variables ◽  
Robust Optimization Design

In view of robustness of objective function and constraints in robust design, the method of maximum variation analysis is adopted to improve the robust design. In this method, firstly, we analyses the effect of uncertain factors in design variables and design parameters on the objective function and constraints, then calculate maximum variations of objective function and constraints. A two-level optimum mathematical model is constructed by adding the maximum variations to the original constraints. Different solving methods are used to solve the model to study the influence to robustness. As a demonstration, we apply our robust optimization method to an engineering example, the design of a machine tool spindle. The results show that, compared with other methods, this method of HPSO(hybrid particle swarm optimization) algorithm is superior on solving efficiency and solving results, and the constraint robustness and the objective robustness completely satisfy the requirement, revealing that excellent solving method can improve robustness.

scholarly journals Application of evolutionary algorithms to optimize cooling channels

2019 ◽  
Vol 10 ◽  
pp. A4 ◽  
Author(s):  
Narasimha R. Nagaiah ◽  
Christopher D. Geiger
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Simulation Method ◽  
Optimization Method ◽  
Composite Function ◽  
Cooling Channels ◽  
Design Variables ◽  
Evolutionary Simulation ◽  
Conventional Methods ◽  
Cooling Passage

The design and development is a complex, repetitive, and more often difficult task, as design tasks comprising of restraining and conflicting relationships among design variables with more than one design objectives. Conventional methods for solving more than one objective optimization problems is to build one composite function by scalarizing the multiple objective functions into a single objective function with one solution. But, the disadvantages of conventional methods inspired scientists and engineers to look for different methods that result in more than one design solutions, also known as Pareto optimal solutions instead of one single solution. Furthermore, these methods not only involved in the optimization of more than one objectives concurrently but also optimize the objectives which are conflicting in nature, where optimizing one or more objective affects the outcome of other objectives negatively. This study demonstrates a nature-based and bio-inspired evolutionary simulation method that addresses the disadvantages of current methods in the application of design optimization. As an example, in this research, we chose to optimize the periodic segment of the cooling passage of an industrial gas turbine blade comprising of ribs (also known as turbulators) to enhance the cooling effectiveness. The outlined design optimization method provides a set of tradeoff designs to pick from depending on designer requirements.

Design Optimization of Machine-Tool Structures With Respect to Dynamic Characteristics

1983 ◽  
Vol 105 (1) ◽  
pp. 88-96 ◽  
Author(s):  
M. Yoshimura ◽  
T. Hamada ◽  
K. Yura ◽  
K. Hitomi
Keyword(s):  
Machine Tool ◽  
Design Optimization ◽  
Dynamic Characteristics ◽  
Machine Tools ◽  
Structural Model ◽  
Optimization Method ◽  
Practical Applications ◽  
Design Variables ◽  
Design Changes ◽  
Machine Tool Structures

This paper proposes a design optimization method in which simplified structural models and standard mathematical programming methods are employed in order to optimize the dynamic characteristics of machine-tool structures in practical applications. This method is composed of three phases: (1) simplification, (2) optimization, and (3) realization. As design variables employed in this optimization are greatly reduced, machine-tool structures are optimized effectively in practice. With large design changes being conducted through this multiphase procedure, dynamic characteristics of machine tools can be greatly improved. This method is demonstrated on a structural model of a vertical lathe.

Design Optimization of Valveless DTH Pneumatic Hammers by a Weighted Pseudo-Gradient Search Method

1998 ◽  
Vol 120 (4) ◽  
pp. 687-694 ◽  
Author(s):  
L. E. Chiang ◽  
E. B. Stamm
Keyword(s):  
Dynamic Model ◽  
Linear Equations ◽  
Optimization Methods ◽  
Optimization Method ◽  
Design Variables ◽  
Standard Procedures ◽  
Non Linear ◽  
Predicted Values ◽  
Gradient Search Method ◽  
Non Linear Equations

A design methodology for Down-The-Hole (DTH) pneumatic hammers used for rock drilling is proposed which renders an optimal design for a given set of constraints. A generic non-linear dynamic model developed by the authors is used to compute the hammer performance. This model consists of a set of six differential equations plus a set of twenty non-linear polynomial equations. In addition there are parameter range restrictions given by fabrication and operational standard procedures. In any given application, magnitudes such as power, impact energy, frequency, efficiency and mass flow may be sought for optimality. However these magnitudes must be computed by integration after solving the dynamic model over an entire cycle, thus traditional optimization methods for non-linear equations that are based in gradient information are not suitable. Hence a method that uses secant information is used to approximate the gradient of the space of design variables. Several prototypes using this optimization method have been designed and field tested. The results are in agreement with predicted values.

The Response Surface Single Loop Reliability-Based Design Optimization Method With Reliability Requirement on System Failure

2016 ◽  
Author(s):  
Rami Mansour ◽  
Mårten Olsson
Keyword(s):  
Design Optimization ◽  
Response Surface ◽  
Optimization Problem ◽  
Optimization Method ◽  
Reliability Based Design Optimization ◽  
Single Loop ◽  
Reliability Based Design ◽  
Design Variables ◽  
Reliability Method ◽  
Deterministic Solution

In reliability-based design optimization (RBDO), an optimal design which minimizes an objective function while satisfying a number of probabilistic constraints is found. As opposed to deterministic optimization, statistical uncertainties in design variables and design parameters have to be taken into account in the design process in order to achieve a reliable design. In the most widely used RBDO approaches, the First-Order Reliability Method (FORM) is used in the probability assessment. This involves locating the Most Probable Point (MPP) of failure, or the inverse MPP, either exactly or approximately. If exact methods are used, an optimization problem has to be solved, typically resulting in computationally expensive double loop or decoupled loop RBDO methods. On the other hand, locating the MPP approximately typically results in highly efficient single loop RBDO methods since the optimization problem is not necessary in the probability assessment. However, since all these methods are based on FORM, which in turn is based on a linearization of the deterministic constraints at the MPP, they may suffer inaccuracies associated with neglecting the nonlinearity of deterministic constraints. In a previous paper presented by the authors, the Response Surface Single Loop (RSSL) Reliability-based design optimization method was proposed. The RSSL-method takes into account the non-linearity of the deterministic constraints in the computation of the probability of failure and was therefore shown to have higher accuracy than existing RBDO methods. The RSSL-method was also shown to have high efficiency since it bypasses the concept of an MPP. In RSSL, the deterministic solution is first found by neglecting uncertainties in design variables and parameters. Thereafter quadratic response surface models are fitted to the deterministic constraints around the deterministic solution using a single set of design of experiments. The RBDO problem is thereafter solved in a single loop using a closed-form second order reliability method (SORM) which takes into account all elements of the Hessian of the quadratic constraints. In this paper, the RSSL method is used to solve the more challenging system RBDO problems where all constraints are replaced by one constraint on the system probability of failure. The probabilities of failure for the constraints are assumed independent of each other. In general, system reliability problems may be more challenging to solve since replacing all constraints by one constraint may strongly increase the non-linearity in the optimization problem. The extensively studied reliability-based design for vehicle crash-worthiness, where the provided deterministic constraints are general quadratic models describing the system in the whole region of interest, is used to demonstrate the capabilities of the RSSL method for problems with system reliability constraints.

DBD Plasma Actuator Multi-Objective Design Optimization at Reynolds Number 63,000: Baseline Case

2013 ◽  
Author(s):  
Taufik Sulaiman ◽  
Satoshi Sekimoto ◽  
Tomoaki Tatsukawa ◽  
Taku Nonomura ◽  
Akira Oyama ◽  
...  
Keyword(s):  
Reynolds Number ◽  
Design Optimization ◽  
Linear Trend ◽  
Lift Coefficient ◽  
Optimization Method ◽  
Input Power ◽  
Plasma Actuator ◽  
Dbd Plasma ◽  
Multi Objective ◽  
Design Variables

The working parameters of the dielectric barrier discharge (DBD) plasma actuator were optimized to gain an understanding of the flow control mechanism. Experiments were conducted at a Reynolds number of 63,000 using a NACA 0015 airfoil which was fixed to the stall angle of 12 degrees. The two objective functions are: 1) power consumption (P) and 2) lift coefficient (Cl). The goal of the optimization is to decrease P while maximizing Cl. The design variables consist of input power parameters. The algorithm was run for 10 generations with a total population of 260 solutions. Although the number of generations and population size was limited due to experimental constraints, the algorithm was able to converge and the approximate Pareto-front was obtained. From the objective function space, we observe a relatively linear trend where Cl increases with P and after a certain threshold, the value of Cl seems to saturate. We discuss the results obtained in the objective space in addition to scatter plot matrix and color maps. This article, with its experiment-based approach, demonstrates the robustness of a Multi-Objective Design Optimization method and its feasibility for wind tunnel experiments.

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