Iskanje po katalogu digitalne knjižnice

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: 163; Prenosov: 0
Celotno besedilo (1,90 MB)

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: 1813; Prenosov: 55
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Opis: This paper proposes new approach for modeling of various processes in metal-forming industry. As an example, we demonstrate the use of genetic programming (GP) for modeling of forming efficiency. The forming efficiency is a basis for determination of yield stress which is the fundamental characteristic of metallic materials. Several different genetically evolved models for forming efficiency on the basis of experimental data for learning were discovered. The obtained models (equations) differ in size, shape, complexity and precision of solutions. In one run out of many runs of our GP system the well-known equation of Siebel was obtained. This fact leads us to opinion that GP is a very powerful evolutionary optimization method appropriate not only for modeling of forming efficiency but also for modeling of many other processes in metal-forming industry.
Ključne besede: metal forming, yield stress, forming efficiency, mathematical modeling, adaptation, genetic methods, genetic algorithm, genetic programming, artificial intelligence, process optimisation
Objavljeno v DKUM: 01.06.2012; Ogledov: 1777; Prenosov: 114
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Opis: Process planning is one of the key activities for product design and manufacturing. The impact of process plans on all phases of product design and manufacture requires high level of interaction of different activities and close integration of them into a coherent system. This paper presents a process model of product development with manufacturing approach based on intelligent process planning techniques with focus on optimal selection of manufacturing parameters. Some derivations of the computing model for analysis of machining conditions by optimal determination of the cutting parameters in multi-pass NC machining activities are made with implementation of new evolutionary computation techniques. Genetic Algorithm (GA) based optimization method and deterministic optimization method (DO) are developed and then implementations into real manufacturing process planning for new product developed are analyzed. The results showed that both the developed optimization methods (GA and DO), especially GA, are effective methods for solving multi-objective optimization problems during the manufacturing process planning and cutting parameters selection.
Ključne besede: genetic algorithm, intelligent manufacturing systems, process planning
Objavljeno v DKUM: 31.05.2012; Ogledov: 1737; Prenosov: 36
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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: 2167; Prenosov: 115
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