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Reduction of surface defects by optimization of casting speed using genetic programming : an industrial case study
Miha Kovačič, Uroš Župerl, Leo Gusel, Miran Brezočnik, 2023, original scientific article

Abstract: Štore Steel Ltd. produces more than 200 different types of steel with a continuous caster installed in 2016. Several defects, mostly related to thermomechanical behaviour in the mould, originate from the continuous casting process. The same casting speed of 1.6 m/min was used for all steel grades. In May 2023, a project was launched to adjust the casting speed according to the casting temperature. This adjustment included the steel grades with the highest number of surface defects and different carbon content: 16MnCrS5, C22, 30MnVS5, and 46MnVS5. For every 10 °C deviation from the prescribed casting temperature, the speed was changed by 0.02 m/min. During the 2-month period, the ratio of rolled bars with detected surface defects (inspected by an automatic control line) decreased for the mentioned steel grades. The decreases were from 11.27 % to 7.93 %, from 12.73 % to 4.11 %, from 16.28 % to 13.40 %, and from 25.52 % to 16.99 % for 16MnCrS5, C22, 30MnVS5, and 46MnVS5, respectively. Based on the collected chemical composition and casting parameters from these two months, models were obtained using linear regression and genetic programming. These models predict the ratio of rolled bars with detected surface defects and the length of detected surface defects. According to the modelling results, the ratio of rolled bars with detected surface defects and the length of detected surface defects could be minimally reduced by 14 % and 189 %, respectively, using casting speed adjustments. A similar result was achieved from July to November 2023 by adjusting the casting speed for the other 27 types of steel. The same was predicted with the already obtained models. Genetic programming outperformed linear regression.
Keywords: continuous casting of steel, surface defects, automatic control, machine learning, modelling, optimisation, prediction, linear regression, genetic programming
Published in DKUM: 25.03.2024; Views: 221; Downloads: 10
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3.
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: 1191; Downloads: 121
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4.
New computational models for better predictions of the soil-compression index
Ahmet Demir, 2015, original scientific article

Abstract: The compression index is one of the important soil parameters that are essential for geotechnical designs. Because laboratory and in-situ tests for determining the compression index (Cc) value are laborious, time consuming and costly, empirical formulas based on soil parameters are commonly used. Over the years a number of empirical formulas have been proposed to relate the compressibility to other soil parameters, such as the natural water content, the liquid limit, the plasticity index, the specific gravity. These empirical formulas provide good results for a specific test set, but cannot accurately or reliably predict the compression index from various test sets. The other disadvantage is that they tend to use a single parameter to estimate the compression index (Cc), even though Cc exhibits spatial characteristics depending on several soil parameters. This study presents the potential for Genetic Expression Programming (GEP) and the Adaptive Neuro-Fuzzy (ANFIS) computing paradigm to predict the compression index from soil parameters such as the natural water content, the liquid limit, the plastic index, the specific gravity and the void ratio. A total of 299 data sets collected from the literature were used to develop the models. The performance of the models was comprehensively evaluated using several statistical verification tools. The predicted results showed that the GEP and ANFIS models provided fairly promising approaches to the prediction of the compression index of soils and could provide a better performance than the empirical formulas.
Keywords: compression index, statistical analysis, genetic expression programming, adaptive neuro-fuzzy, empirical equations
Published in DKUM: 14.06.2018; Views: 1422; Downloads: 82
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5.
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: 3193; Downloads: 404
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6.
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: 2161; Downloads: 120
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7.
Predicting defibrillation success by "genetic" programming in patients with out-of-hospital cardiac arrest
Matej Podbregar, Miha Kovačič, Aleksandra Podbregar-Marš, Miran Brezočnik, 2003, original scientific article

Abstract: In some patients with ventricular fibrillation (VF) there may be a better chance of successful defibrillation after a period of chest compression and ventilation before the defibrillation attempt. It is therefore important to know whether a defibrillation attempt will be successful. The predictive powerof a model developed by "genetic" programming (GP) to predict defibrillation success was studied. Methods and Results: 203 defibrillations were administered in 47 patients with out-of-hospital cardiac arrest due to a cardiac cause. Maximal amplitude, a total energy of power spectral density, and the Hurst exponent of the VF electrocardiogram (ECG) signal were included in the model developed by GP. Positive and negative likelihood ratios of the model for testing data were 35.5 and 0.00, respectively. Using a model developed by GP on the complete database, 120 of the 124 unsuccessful defibrillations would have been avoided, whereas all of the 79 successful defibrillations would have been administered. Conclusion: The VF ECG contains information predictive of defibrillation success. The model developed by GP, including data from the time-domain, frequency-domain and nonlinear dynamics, could reduce the incidence of unsuccessful defibrillations.
Keywords: optimisation methods, evolutionary optimisation methods, genetic algorithms, genetic programming, defibrillation, cardiac arrest prediction
Published in DKUM: 01.06.2012; Views: 1991; Downloads: 96
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8.
A model of data flow in lower CIM levels
Igor Drstvenšek, Ivo Pahole, Jože Balič, 2004, original scientific article

Abstract: After years of work in fields of computer-integrated manufacturing (CIM), flexible manufacturing systems (FMS), and evolutionary optimisation techniques, several models of production automation were developed in our laboratories. The last model pools the discoveries that proved their effectiveness in the past models. It is based on the idea of five levels CIM hierarchy where the technological database (TDB) represents a backbone of the system. Further on the idea of work operation determination by an analyse of the production system is taken out of a model for FMS control system, and finally the approach to the optimisation of production is supported by the results of evolutionary based techniques such as genetic algorithms and genetic programming.
Keywords: computer integrated manufacturing, flexible manufacturing systems, evolutionary optimisation techniques, production automation, CIM hierarchy, technological databases, production optimisation, genetic algorithms, genetic programming
Published in DKUM: 01.06.2012; Views: 2205; Downloads: 102
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9.
Prediction of surface roughness with genetic programming
Miran Brezočnik, Miha Kovačič, Mirko Ficko, 2004, original scientific article

Abstract: In this paper we propose genetic programming to predict surface roughness in end-milling. Two independent data sets were obtained on the basis of measurement: training data set and testing data set. Spindle speed, feed rate,depth of cut, and vibrations are used as independent input variables (parameters), while surface roughness as dependent output variable. On the basis of training data set, different models for surface roughness were developed by genetic programming. Accuracy of the best model was proved with the testing data. It was established that the surface roughness is most influenced by the feed rate, whereas the vibrations increase the prediction accuracy.
Keywords: end milling, surface roughness, prediction of surface roughness, genetic programming
Published in DKUM: 01.06.2012; Views: 2112; Downloads: 131
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10.
Evolutionary approach for cutting forces prediction in milling
Miha Kovačič, Jože Balič, Miran Brezočnik, 2004, original scientific article

Abstract: Knowing cutting forces is important for choosing cutting parameters for milling. Traditionally, cutting forces are calculated by equation which includes empirically measured specific cutting forces. In the article modelling of cutting forces with genetic programming is proposed, which imitates principles of living beings. Measurements have been made for two materials (aluminium alloy AlMgSi1 and steel 1.2343) and two different types of milling (conventional milling and STEP milling). For each material and type of milling parameters, tensile strength and hardness of workpiece, tool diameter, cutting depth, spindle speed, feeding and type of milling were monitored, and for each combination of milling parameters cutting forces were measured. On the basis of the experimental data, different models for cutting forces prediction were obtained by genetic programming. Research shows that genetically developed models fit the experimental data.
Keywords: milling, simulation, milling cutting forces prediction, genetic programming
Published in DKUM: 01.06.2012; Views: 1511; Downloads: 79
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