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1.
Sustainable design of circular reinforced concrete column sections via multi-objective optimization
Primož Jelušič, Tomaž Žula, 2023, original scientific article

Abstract: 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.
Keywords: reinforced concrete columns, circular cross section, costs, CO2 emissions, multi-objective optimization, genetic algorithm
Published in DKUM: 15.04.2024; Views: 173; Downloads: 198
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2.
Optimization of chaboche material parameters with a genetic algorithm
Nejc Dvoršek, Iztok Stopeinig, Simon Klančnik, 2023, original scientific article

Keywords: Chaboche material model, parameter optimization, genetic algorithm, finite element method
Published in DKUM: 04.04.2024; Views: 72; Downloads: 6
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3.
Optimizacija Chaboche materialnih parametrov z genetskim algoritmom : magistrsko delo
Nejc Dvoršek, 2022, master's thesis

Abstract: 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.
Keywords: Chaboche material model, parameter optimization, genetic algorithm, finite element method
Published in DKUM: 16.12.2022; Views: 739; Downloads: 0
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4.
Automatic compiler/interpreter generation from programs for domain-specific languages using semantic inference : doktorska disertacija
Željko Kovačević, 2022, doctoral dissertation

Abstract: Presented doctoral dissertation describes a research work on Semantic Inference, which can be regarded as an extension of Grammar Inference. The main task of Grammar Inference is to induce a grammatical structure from a set of positive samples (programs), which can sometimes also be accompanied by a set of negative samples. Successfully applying Grammar Inference can result only in identifying the correct syntax of a language. But, when valid syntactical structures are additionally constrained with context-sensitive information the Grammar Inference needs to be extended to the Semantic Inference. With the Semantic Inference a further step is realised, namely, towards inducing language semantics. In this doctoral dissertation it is shown that a complete compiler/interpreter for small Domain-Specific Languages (DSLs) can be generated automatically solely from given programs and their associated meanings using Semantic Inference. For the purpose of this research work the tool LISA.SI has been developed on the top of the compiler/interpreter generator tool LISA that uses Evolutionary Computations to explore and exploit the enormous search space that appears in Semantic Inference. A wide class of Attribute Grammars has been learned. Using Genetic Programming approach S-attributed and L-attributed have been inferred successfully, while inferring Absolutely Non-Circular Attribute Grammars (ANC-AG) with complex dependencies among attributes has been achieved by integrating a Memetic Algorithm (MA) into the LISA.SI tool.
Keywords: Grammatical Inference, Semantic Inference, Genetic Programming, Attribute Grammars, Memetic Algorithm, Domain-Specific Languages
Published in DKUM: 17.02.2022; Views: 1114; Downloads: 115
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5.
Parameter optimization of tube hydroforming
Edina Karabegović, Miran Brezočnik, 2012, original scientific article

Abstract: 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.
Keywords: hydroforming, tube, modelling, optimization, parameter, genetic algorithm, T-shape
Published in DKUM: 10.07.2015; Views: 2450; Downloads: 60
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6.
Automated and intelligent programming of cnc machine tools : doctoral thesis
Afrim Gjelaj, 2014, doctoral dissertation

Abstract: Nowadays, many scientists focus on increasing the level of automation, respectively flexibility in manufacturing systems. In addition, automated programming of CNC machine tools has reached a high level of machining operations. However, it is still impossible for a machine to manipulate completely in an autonomous way. Special attention in this doctoral thesis is focused on the automated programming of CNC machine tools regarding artificial intelligence. The purpose of automated programming is to improve quality and to fulfil the requirements of manufacturing industry and provide commercial solutions. This thesis also provides a description of artificial intelligence usage in order to solve optimal tool path-length and tool selection, as well as the preparation of planned technology. Firstly, the automated programming of CNC machine tools enjoys great success when applying artificial intelligence in regard to the machining processes. Choices of path length and tool selection are analysed in great detail in order to ascertain the optimal problems of tool path- length and tool selection. However, in order to achieve automated and intelligent CNC programming of machine tools, their flexibilities are of major importance. Automation today tends to improve and implement manufacturing flexibility at a strategic level. This means increasing the degree of flexibility whilst at the same time increasing the degree of automation regarding CNC machine tools. In addition to the above-mentioned investigated problems, the influences of cutting force (Fc), power cutting (Pc), tool life (T) and surface roughness (Ra) as functions of tool path- length are also analysed. Analytical and mathematical models are optimised using a multi-objective genetic algorithm (MOGA). MOGA enables optimisation by employing two or more equations simultaneously. Another problem for the automated and intelligent CNC programming of machine tools focuses on the application of Discrete Systems (DS). The discrete system in our work focuses on analysing cutting force (Fc) in regard to the turning operation.
Keywords: inteligent CNC programming, intelligent manufacturing, discrete system, automated programming, multiobjective genetic algorithm MOGA
Published in DKUM: 23.01.2015; Views: 3148; Downloads: 402
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7.
Modeling of forming efficiency using genetic programming
Miran Brezočnik, Jože Balič, Zlatko Kampuš, 2001, original scientific article

Abstract: 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.
Keywords: metal forming, yield stress, forming efficiency, mathematical modeling, adaptation, genetic methods, genetic algorithm, genetic programming, artificial intelligence, process optimisation
Published in DKUM: 01.06.2012; Views: 2114; Downloads: 118
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8.
Use of genetic algorithm for fitting Sovova's mass transfer model
Dejan Hrnčič, Marjan Mernik, Maša Knez Marevci, 2010, original scientific article

Abstract: A genetic algorithm with resizable population has been applied to the estimation of parameters for Sovovaćs mass transfer model. The comparison of results between a genetic algorithm and a global optimizer from the literatureshows that a genetic algorithm performs as good as or better than a global optimizer on a given set of problems. Other benefits of the genetic algorithm, for mass transfer modeling, are simplicity, robustness and efficiency.
Keywords: Sovova's mass transfer model, genetic algorithm, parameter estimation
Published in DKUM: 31.05.2012; Views: 1826; Downloads: 80
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9.
Intelligent process planning for competitive engineering
Valentina Gečevska, Franc Čuš, 2010, original scientific article

Abstract: 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.
Keywords: genetic algorithm, intelligent manufacturing systems, process planning
Published in DKUM: 31.05.2012; Views: 1999; Downloads: 40
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10.
Intelligent programming of CNC turning operations using genetic algorithm
Jože Balič, Miha Kovačič, Boštjan Vaupotič, 2006, original scientific article

Abstract: CAD/CAM systems are nowadays tightly connected to ensure that CAD data can be used for optimal tool path determination and generation of CNC programs for machine tools. The aim of our research is the design of a computer-aided, intelligent and genetic algorithm(GA) based programming system for CNC cutting tools selection, tool sequences planning and optimisation of cutting conditions. The first step is geometrical feature recognition and classification. On the basis of recognised features the module for GA-based determination of technological data determine cutting tools, cutting parameters (according to work piece material and cutting tool material) and detailed tool sequence planning. Material, which will be removed, is split into several cuts, each consisting of a number of basic tool movements. In thenext step, GA operations such as reproduction, crossover and mutation are applied. The process of GA-based optimisation runs in cycles in which new generations of individuals are created with increased average fitness of a population. During the evaluation of calculated results (generated NC programmes) several rules and constraints like rapid and cutting tool movement, collision, clamping and minimum machining time, which represent the fitness function, were taken into account. A case study was made for the turning operation of a rotational part. The results show that the GA-based programming has a higher efficiency. The total machining time was reduced by 16%. The demand for a high skilled worker on CAD/CAM systems and CNC machine tools was also reduced.
Keywords: CNC programming, genetic algorithm, intelligent CAM, turning, tool path generation
Published in DKUM: 30.05.2012; Views: 2935; Downloads: 96
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