Iskanje po katalogu digitalne knjižnice

Opis: An optimization model for reinforced concrete circular columns based on the Eurocodes is presented. With the developed optimization model, which takes into account the exact distribution of the steel reinforcement, which is not the case when designing with conventional column design charts, an optimal design for the reinforced concrete cross section is determined. The optimization model uses discrete variables, which makes the results more suitable for actual construction practice and fully exploits the structural capacity of the structure. A parametric study of the applied axial load and bending moment was performed for material cost and CO2 emissions. The results based on a single objective function show that the optimal design of the reinforced concrete column cross section obtained for the material cost objective function contains a larger cross-sectional area of concrete and a smaller area of steel compared with the optimization results when CO2 emissions are determined as the objective function. However, the optimal solution in the case where the material cost was assigned as the objective function has much more reserve in axial load capacity than in the optimal design where CO2 was chosen as the objective function. In addition, the multi-objective optimization was performed to find a set of solutions that provide the best trade-offs between the material cost and CO2 emission objectives.
Ključne besede: reinforced concrete columns, circular cross section, costs, CO2 emissions, multi-objective optimization, genetic algorithm
Objavljeno v DKUM: 15.04.2024; Ogledov: 338; Prenosov: 207
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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: 872; 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: 2651; Prenosov: 62
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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: 2212; Prenosov: 121
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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: 2083; Prenosov: 42
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