Prediction of the ultraviolet protection of cotton woven fabrics dyed with reactive dystuffs

Textile materials provide a simple and convenient protection against UV radiation. To assign the degree of UV radiation protection of textile materials, the Ultraviolet Protection Factor (UPF) is commonly used. This paper reports the effect of woven fabric construction (yarn fineness, type of weave, relative fabric density), the colour of bi-functional reactive dyestuffs, and Cibacron dyed fabrics on the ultraviolet protection of light summer woven fabrics. A predictive model, determined by genetic programming, was derived to describe the influence of fabric construction. Warp and weft densities, weave factor and CIELab colour components were taken into account by developing the prediction model for UPF. The results show very good agreement between the experimental and predicted values

Study of crosslinking efficiency of cotton cellulose by different physical-chemical methods and genetic programming

We have investigated the crosslinking effect of unmercerized and mercerized cotton celluose crosslinked with different BTCA mass fractions in the impregnation bath. Crosslinking efficiency was analyzed using FT-IR spectroscopy, water retention capacity method, tensiometry and the methylene blue method. On the basis of the experimental data which was obtained with theseparate physical-chemical methods, different prediction models for crosslinking efficiency was developed. Modelling was taken out with the genetic programming method. Research shows good accordance of the experimentaldata with the genetic models

Genetic modeling of electrical conductivity of formed material

V prispevku smo predstavili metodo genetskega programiranja za uspešno določitev natančnih modelov spremembe električne prevodnosti hladno preoblikovane zlitine CuCrZr. Glavna značilnost metode genetskega programiranja, ki spada med evolucijske metode modeliranja, je, da rešitev ne iščemo po vnaprej določenih poteh ter da sočasno obravnavamo množico enostavnih objektov. Čedalje natančnejšim rešitvam smo se približevali postopoma, med postopkom simulirane evolucije. V prispevku smo predstavili le nekatere najuspešnejše oziroma najprimernejše genetske modele. Natančnost genetskih modelov je bila preverjena na množici preskusnih točk. Primerjali smo tudi natančnost genetsko dobljenih modelov in modela, dobljenega po deterministični metodi regresije. Primerjava je pokazala, da se genetski modeli dosti manj odmikajo od eksperimentalnih rezultatov in da so bolj raznoliki. Prav raznolikost nam omogoča, da se, glede na zahteve, odločimo za optimalen model, s katerim lahko matematično opišemo ali napovedujemo spremembo električne prevodnosti zlitine v okviru eksperimentalnega okolja.In the paper a genetic programming method for efficient determination of accurate models for the change of electrical conductivity of cold formed alloy CuCrZr was presented. The main characteristic of genetic programming method, which is one of evolutionary methods for modeling, is its non- deterministic way of computing. No assumptions about the form and size of expressions were made in advance, but they were left to the self organization and intelligence of evolutionary process. Only the best models, gained by genetic programming were presented in the paper. Accuracy of the best models was proved with the testing data set. The comparison between deviation of genetic models results and regression models results concerning the experimental results has showed that genetic models are much more precise and more varied then regression model. The variety of genetic models allows us, concerning the demands, to decide for an optimal genetic model for mathematical description and prediction of change of electrical conductivity in the frame of experimental environment

Modeling of impact toughness of cold formed material by genetic programming

In the paper, an approach completely different from the conventional methods for determination of accurate models for the change of properties of cold formed material, is presented. This approach is genetic programming (GP) method which is based on imitation of natural evolution of living organisms. The main characteristic of GP is its non-deterministic way of computing. No assumptions about the form and size of expressions were made in advance, but they were left to the self organization and intelligence of evolutionary process. First, copper alloy rods were cold drawn under different conditions and then impact toughness of cold drawn specimens was determined by Charpy tests. The values of independent variables (effective strain, coefficient of friction) influence the value of the dependent variable, impact toughness. On the basis of training data, different prediction models for impact toughness were developed by GP. Only the best models, gained by genetic programming were presented in the paper. Accuracy of the best models was proved with the testing data set. The comparison between deviation of genetic model results and regression model results concerning the experimental results has showed that genetic models are more precise and more varied then regression models

Optimizacija procesa istiskivanja primjenom genetskog algoritma i konvencionalnih tehnika

The purpose of this research is the determination of the optimal cold forward extrusion parameters with the minimization of tool load as objective. This paper deals with different optimization approaches in order to determine optimal values of logarithmic strain, die angle and friction factor with the purpose to find minimal tool loading obtained by cold forward extrusion process. Two experimental plans based on factorial design of experiment and orthogonal array have been carried out. Classical optimization, according to the response model of extrusion forming force, and the Taguchi approach are presented. The obtained extrusion force model as the fitness function was used to carry out genetic algorithm optimization. Experimental verification of optimal forming parameters with their influences on the forming forces was also performed. The experimental results show an improvement in the minimization of tool loading. The results of optimal forming parameters obtained with different optimization approaches have been compared and based on that the characteristics analysis (features and limitations) of presented techniques.Svrha ovoga rada je određivanje optimalnih parametara procesa hladnog istosmjernog istiskivanja s ciljem minimizacije opterećenja alata. U radu su predstavljeni različite optimizacijski pristupi u cilju definiranja optimalnih vrijednosti logaritamske deformacije, kuta matrice i faktora trenja s ciljem određivanja minimalnog opterećenja alata procesa hladnog istosmjernog istiskivanja. U tomu cilju izvedena su dva eksperimentalna plana temeljena na faktornom ortogonalnom planu i ortogonalnom nizu. Predstavljena je klasična optimizacija dobivenog modela sile istiskivanja kao iTaguchi pristup. Dobiveni model sile istiskivanja korišten je kao funkcija cilja u optimizaciji pomoću genetskog algoritma. Eksperimentalna provjera optimalnih parametara procesa i njihov utjecaj na silu istiskivanja je također prikazan u radu. Eksperimentalni rezultati prikazuju poboljšanja u procesu minimizacije opterećenja alata. Izvršena je usporedba rezultata dobivenih različitim optimizacijskim tehnikama, kao i analiza prezentiranih tehnika s njihovim mogućnostima i ograničenjima u praktičnoj primjeni

Predicting stress distribution in cold-formed material with genetic programming

In this paper we propose a genetic programming approach to predict radial stress distribution in cold-formed material. As an example, cylindrical specimens of copper alloy were forward extruded and analysed by the visioplasticity method. They were extruded with different coefficients of friction. The values of three independent variables (i.e., radial and axial position of measured stress node, and coefficient of friction) were collected after each extrusion. These variables influence the value of the dependent variable, i.e., radial stress. On the basis of training data set, various different prediction models for radial stress distribution were developed during simulated evolution. Accuracy of the best models was proved with the testing data set. The research showed that by proposed approach the precise prediction models can be developedtherefore, it is widely used also in other areas in metal-forming industry, where the experimental data on the process are known

Parameter Optimization of Tube Hydroforming

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 engine ering 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

Numerične simulacije brizganja kovinskih prašnatih materialov

Metal injection moulding (MIM) is already a well-established and promising technology for the mass production of small, complex, near-net-shape products. The dimensions and mechanical properties of MIM products are influenced by the feedstock characteristics, the process parameters of the injection moulding, as well as the debinding and the sintering. Numerical simulations are a very important feature of the beginning of any product or technology development. In the article two different techniques for measuring the rheological properties of MIM feedstocks are presented and compared. It was established that capillary rheometers are more appropriate for MIM feed stocks, while on the other hand, parallel-plate rheometers are only suitable for shear rates lower than 10 s[sup]{-1}. Later on we used genetic algorithms to determine the model coefficients for some numerical simulation software. The results of the simulation of the filling phase and a comparison with the experimental results are presented in the article.Brizganje kovinskih prašnatih materialov (MIM) je uveljavljena tehnologija, primerna za izdelavo majhnih, kompleksnih izdelkov visoke natančnosti. Dimenzije in mehanske lastnosti MIM-izdelkov so odvisne od lastnosti mešanic, procesnih parametrov brizganja, odstranjevanja veziva in sintranja. Numerične simulacije so pri razvoju novih izdelkov oziroma tehnologij zelo pomembne. V prispevku je narejena primerjava in predstavitev dveh metod za merjenje reoloških lastnosti MIM mešanic. Ugotovljeno je bilo, da je kapilarni reometer primernejši za meritve pri višjih strižnih hitrostih, medtem ko je reometer z vzporedno ploščo primeren le za strižne hitrosti do 10 s[na]{-1}. V nadaljevanju je bila uporabljena metoda genetskih algoritmov za določitev koeficientov matematičnega modela, ki se uporablja v programski opremi za numerične simulacije brizganja. Na koncu so predstavljeni rezultati simulacije polnjenja orodne votline, narejena pa je tudi primerjava z eksperimentalnimi rezultati

A model for forming a flexible manufacturing system using genetic algorithms

Znano je, da je mogoče z dobro razmestitvijo naprav v prilagodljivem obdelovalnem sistemu zmanjšati spremenljive prenosne stroške in s tem skupne izdelovalne stroške in si tako zagotoviti konkurenčno prednost. Do sedaj so bile vpeljane različne metode razporejanja, vendar problem se ni zadovoljivo rešen. V prispevku je opisana metoda za razporejanje strojev in naprav v prilagodljivem obdelovalnem sistemu z metodo genetskih algoritmov. Predpostavljena je črtna razporeditev enot prilagodljivega obdelovalnega sistema. S predlagano metodo je mogoče poiskati optimalno razporeditev glede na postavljeno ciljno funkcijo, ki zajema spremenljive stroške prenosa.It is well-known that with a proper layout of devices in a flexible manufacturing system it is possible to reduce the variable transport costs and, consequently, the total manufacturing costs, and thus secure a competitive advantage. So far, a variety of methods of layout have been introduced, but the problem has not been satisfactorily solved. This paper describes a method of placing machines and devices in a FMS using a genetic algorithm method. The line layout of the units of the FMS system is assumed. With the proposed method it is possible to ensure optimum layout with respect to the set target function including variable transport costs