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1.
Optimization of chaboche material parameters with a genetic algorithm
Nejc Dvoršek, Iztok Stopeinig, Simon Klančnik, 2023, izvirni znanstveni članek

Ključne besede: Chaboche material model, parameter optimization, genetic algorithm, finite element method
Objavljeno v DKUM: 04.04.2024; Ogledov: 33; Prenosov: 1
.pdf Celotno besedilo (3,62 MB)
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2.
Optimizacija Chaboche materialnih parametrov z genetskim algoritmom : magistrsko delo
Nejc Dvoršek, 2022, magistrsko delo

Opis: The basis of this thesis is research and development of a genetic algorithm for material parameters optimization. It is written in collaboration with AVL, which already has a solution for this problem, but is looking into better alternatives. Chaboche material model is a nonlinear isotropic and kinematic hardening model which can describe elasto-viscoplastic constitutive relations. Parameters of such complex nature do not have a physical interpretation in the real-world and must be defined with inverse analysis. Genetic algorithms (GA) are a promising tool to help with such tasks. They have been widely used and recognized for various optimization problems. Material data available are low cycle fatigue (LCF), creep, and tensile experiments. For each experiment a corresponding finite element model in Abaqus is prepared. Comparing experimental and simulation data is the objective function GA will try to minimize. For this reason, a corresponding fitness function was developed to score each individual. It makes use of similarity measure algorithm proposed in this paper [10]. GA was implemented in Python with Pygad library. Instead of bits, genes are represented with real-valued numbers with defined limits. Performance of developed GA was tested based on various population sizes, mutation probabilities, and crossover operators. The main parameter that impacts algorithms performance is population size. Paired with right mutation probability the algorithm can find a global minimum of described optimization problem. Making it a viable alternative to existing approach used at AVL.
Ključne besede: Chaboche material model, parameter optimization, genetic algorithm, finite element method
Objavljeno v DKUM: 16.12.2022; Ogledov: 672; Prenosov: 0
.pdf Celotno besedilo (1,90 MB)

3.
Organization in finance prepared by stohastic differential equations with additive and nonlinear models and continuous optimization
Pakize Taylan, Gerhard-Wilhelm Weber, 2008, izvirni znanstveni članek

Opis: A central element in organization of financal means by a person, a company or societal group consists in the constitution, analysis and optimization of portfolios. This requests the time-depending modeling of processes. Likewise many processes in nature, technology and economy, financial processes suffer from stochastic fluctuations. Therefore, we consider stochastic differential equations (Kloeden, Platen and Schurz, 1994) since in reality, especially, in the financial sector, many processes are affected with noise. As a drawback, these equations are hard to represent by a computer and hard to resolve. In our paper, we express them in simplified manner of approximation by both a discretization and additive models based on splines. Our parameter estimation refers to the linearly involved spline coefficients as prepared in (Taylan and Weber, 2007) and the partially nonlinearly involved probabilistic parameters. We construct a penalized residual sum of square for this model and face occuring nonlinearities by Gauss-Newton's and Levenberg-Marquardt's method on determining the iteration step. We also investigate when the related minimization program can be written as a Tikhonov regularization problem (sometimes called ridge regression), and we treat it using continuous optimization techniques. In particular, we prepare access to the elegant framework of conic quadratic programming. These convex optimation problems are very well-structured, herewith resembling linear programs and, hence, permitting the use of interior point methods (Nesterov and Nemirovskii, 1993).
Ključne besede: stochastic differential equations, regression, statistical learning, parameter estimation, splines, Gauss-Newton method, Levenberg-Marquardt's method, smoothing, stability, penalty methods, Tikhonov regularization, continuous optimization, conic quadratic programming
Objavljeno v DKUM: 10.01.2018; Ogledov: 1283; Prenosov: 137
.pdf Celotno besedilo (364,34 KB)
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4.
Parameter optimization of tube hydroforming
Edina Karabegović, Miran Brezočnik, 2012, izvirni znanstveni članek

Opis: Tube hydroforming is mostly applied in automotive industry. In this respect, necessity for the procedure improvement of fluid forming is constant. One of the reasons of its improvement is the procedure performance in optimal conditions. The process parameters have the direct influence on quality and optimal of forming procedure. This paper provides an example of the fluid pressure optimization in T-shape tube hydroforming. Three types of material have been analysed, with three wall thickness and three course levels of axial printers. For the optimization, the evolutional method with applied genetic algorithm (GA) was utilized. The application of GA is significant in solving of many problems in engineering practice. The simplicity and adaptability of the genetic algorithm to the engineering problem results with the increasing volume of applications in a research work. In this paper we investigated interactions of the internal parameters of the T tube hydroforming process, towards achieving the GA model for the optimal internal pressure, necessary for hydroforming.
Ključne besede: hydroforming, tube, modelling, optimization, parameter, genetic algorithm, T-shape
Objavljeno v DKUM: 10.07.2015; Ogledov: 2374; Prenosov: 60
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5.
Performance comparison of self-adaptive and adaptive differential evolution algorithms
Janez Brest, Borko Bošković, Sašo Greiner, Viljem Žumer, Mirjam Sepesy Maučec, 2007, izvirni znanstveni članek

Opis: Differential evolution (DE) has been shown to be a simple, yet powerful, evolutionary algorithm for global optimization. for many real problems. Adaptation, especially self-adaptation, has been found to be highly beneficial for adjusting control parameters, especially when done without any user interaction. This paper presents differential evolution algorithms, whichuse different adaptive or self-adaptive mechanisms applied to the control parameters. Detailed performance comparisons of these algorithms on the benchmark functions are outlined.
Ključne besede: differential evolution, control parameter, fitness function, optimization, self-adaption
Objavljeno v DKUM: 01.06.2012; Ogledov: 2096; Prenosov: 87
URL Povezava na celotno besedilo

6.
Population size reduction for the differential evolution algorithm
Janez Brest, Mirjam Sepesy Maučec, 2008, izvirni znanstveni članek

Opis: This paper studies the efficiency of a recently defined population-based direct global optimization method called Differential Evolution with self-adaptive control parameters. The original version uses fixed population size but a method for gradually reducing population size is proposed in this paper. It improves the efficiency and robustness of the algorithm and can be applied to any variant of a Differential Evolution algorithm. The proposed modification is tested on commonly used benchmark problems for unconstrained optimization and compared with other optimization methods such as Evolutionary Algorithms and Evolution Strategies.
Ključne besede: differential evolution, control parameter, fitness function, global function optimization, self-adaptation, population size
Objavljeno v DKUM: 01.06.2012; Ogledov: 2421; Prenosov: 128
URL Povezava na celotno besedilo

7.
Approach to optimization of cutting conditions by using artificial neural networks
Franc Čuš, Uroš Župerl, 2006, izvirni znanstveni članek

Opis: Optimum selection of cutting conditions importantly contribute to the increase of productivity and the reduction of costs, therefore utmost attention is paid to this problem in this contribution. In this paper, a neural network-based approach to complex optimization of cutting parameters is proposed. It describes the multi-objective technique of optimization of cutting conditions by means of the neural networks taking into consideration the technological, economic and organizational limitations. To reach higher precision of the predicted results, a neural optimization algorithm is developed and presented to ensure simple, fast and efficient optimization of all important turning parameters. The approach is suitable for fast determination of optimum cutting parameters during machining, where there is not enough time for deep analysis. To demonstrate the procedure and performance of the neural network approach, an illustrative example is discussed in detail.
Ključne besede: optimization, cutting parameter optimization, genetic algorithm, cutting parameters, neural network algorithm, machining, metal cutting
Objavljeno v DKUM: 30.05.2012; Ogledov: 2467; Prenosov: 120
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