Miran Brezocnik

Integrated Genetic Programming and Genetic Algorithm Approach to Predict Surface Roughness

Abstract In this article we propose a new integrated genetic programming and genetic algorithm approach to predict surface roughness in end-milling. Four independent variables, spindle speed, feed rate, depth of cut, and vibrations, were measured. Those variables influence the dependent variable (i.e., surface roughness). On the basis of training data set, different models for surface roughness were developed by genetic programming. The floating-point constants of the best model were additionally optimized by a genetic algorithm. Accuracy of the model was proved on the testing data set. By using the proposed approach, more accurate prediction of surface roughness was reached than if only modeling by genetic programming had been carried out. It was also established that the surface roughness is most influenced by the feed rate, whereas the vibrations increase the prediction accuracy.

Using Genetic Programming to Predict the Macroporosity of Woven Cotton Fabrics

This paper reports the effect of woven fabric construction on macroporosity properties. The area of a macropores cross section, equivalent, maximum, and minimum pore diameters, pore density, and open porosity are observed in this research involving woven fabric construction parameters—yarn linear density, fabric tightness, weave type, and denting. Predictive models, determined by genetic programming, are derived to describe the influence of fabric construction. The results show very good agreement between the experimental and predicted values. This work provides guidelines for engineering staple- yarn cotton fabrics in a grey state in terms of macroporosity properties.

Prediction of the bending capability of rolled metal sheet by genetic programming

The paper proposes genetic programming (GP) to predict the bending capability of rolled titanzinc metal sheet. In this study ZnTiCu alloy with ∼ 0.1% Cu and ∼ 0.1% Ti was used for production of metal sheet. Three groups of independent input variables were measured: (1) chemical composition of the ZnTiCu alloy during casting (percentage of Cu, Ti, and Fe), (2) parameters of hot rolling (temperature of ingot before rolling, time of rolling, temperature of plate after rolling, time of cooling), and (3) parameters of cold rolling (temperature of plate before rolling, temperature of sheet after rolling). Therefore, nine input variables (parameters) influence the bending capability of the sheet metal. On the basis of the experimental data, several models for prediction of the bending capability of titanzinc metal sheet were developed by the simulated evolution. The influence of individual input variables on bending capability was also studied. The most accurate model was verified with an independent testing data set. The results showed that GP is a powerful tool for predicting the bending capability of metal sheet.

A genetic-based approach to simulation of self-organizing assembly

Abstract The paper proposes a new and innovative biologically oriented idea in conceiving intelligent systems in modern factories of the future. The intelligent system is treated as an autonomous organization structure efficiently adapting itself to the dynamic changes in the production environment and the environment in a wider sense. Simulation of self-organizing assembly of mechanical parts (basic components) into the product is presented as an example of the intelligent system. The genetic programming method is used. The genetic-based assembly takes place on the basis of the genetic content in the basic components and the influence of the environment. The evolution of solutions happens in a distributed way, nondeterministically, bottom-up, and in a self-organizing manner. The paper is also a contribution to the international research and development program intelligent manufacturing systems, which is one of the biggest projects ever introduced.

Comparison Between Genetic Algorithm and Genetic Programming Approach for Modeling the Stress Distribution

ABSTRACT This article compares genetic algorithm (GA) and genetic programming (GP) for system modeling in metal forming. As an example, the radial stress distribution in a cold-formed specimen (steel X6Cr13) was predicted by GA and GP. First, cylindrical workpieces were forward extruded and analyzed by the visioplasticity method. After each extrusion, the values of independent variables (radial position of measured stress node, axial position of measured stress node, and coefficient of friction) were collected. These variables influence the value of the dependent variable, radial stress. On the basis of training data, different prediction models for radial stress distribution were developed independently by GA and GP. The obtained models were tested with the testing data. The research has shown that both approaches are suitable for system modeling. However, if the relations between input and output variables are complex, the models developed by the GP approach are much more accurate.

Modeling and Design of Experiments of Laser Cladding Process by Genetic Programming and Nondominated Sorting

Laser deposition of materials represents a modern additive technology that has a number of advantages over remaining technologies for depositing metallic materials. Besides a low-energy input, a quality bond, and minimal heat-affected zone, this technology is also characterized by the good mechanical properties of the deposited material that is a result of rapid cooling. Despite the prospects, this technology is still at the developing phase. New materials and techniques for determining optimal process parameters are being introduced. In this article, we developed a system for modeling (predicting) the properties of the deposited material and used design of experiments (DOE) for the laser cladding process parameter selection. Based on the experimental data obtained during cladding process, models were made for predicting the volume and roughness of the deposited material. Genetic programming was used for modeling the process. Then, a set of several thousand possible combinations (settings) of the machine parameters was produced on the basis of the obtained model. The most appropriate machine (process) parameters were selected in terms of deposition speed, powder efficiency, and surface roughness. These parameters were determined by nondominated sorting. The results offer the operator of the machine a set of appropriate process parameters that enable the production of high-quality products.

Programming of CNC Milling Machines Using Particle Swarm Optimization

This article proposes asystem for theautomatic programming of a CNC milling machine by particle swarm optimization (PSO). In the presented research, each individual swarm particle presents a possible numerical control (NC) program. Voxel representation of machining area was used. Bresenhams algorithm was implemented, for the rasterization of the cuts. Optimisation with PSO was carried out within a voxelized machining area. The system automatically finds the NC program for optimal machining. The NC program guarantees an optimal selection of tools, the shortest possible work and rapid motions, and minimization of the manufacturing time, thus achieving a reduction in machining costs and increased productivity. Testing using test workpieces and 2.5 D milling confirmed the efficiency of the proposed approach. The proposed intelligent system is easily adaptable for programming other types of CNC machines by PSO.

Modeling of forming efficiency using genetic programming

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.

Genetic programming approach to determining of metal materials properties

The paper deals with determining metal material properties by the use of genetic programming (GP). As an example, the determination of the flow stress in bulk forming is presented. The flow stress can be calculated on the basis of known forming efficiency. The experimental data obtained during pressure test serve as an environment to which models for forming efficiency have to be adapted during simulated evolution as much as possible. By performing four experiments, several different models for forming efficiency are genetically developed. The models are not a result of the human intelligence but of intelligent evolutionary process. With regard to their precision, the successful models are more or less equivalent; they differ mainly in size, shape, and complexity of solutions. The influence of selection of different initial model components (genes) on the probability of successful solution is studied in detail. In one especially successful run of the GP system the Siebels expression was genetically developed. In addition, redundancy of the knowledge hidden in the experimental data was detected and eliminated without the influence of human intelligence. Researches showed excellent agreement between the experimental data, existing analytical solutions, and models obtained genetically.

Genetic Algorithm Rolling Mill Layout Optimization

Štore Steel Ltd. is a small flexible steel plant in Slovenia. In 2010, the new continuous rolling mill, which has a technical capacity of 250,000 tons per year, was installed. The new continuous rolling mill, which entailed a corresponding reduction in space, required an urgent relocation of machinery. The genetic algorithm was used for the optimal rearranging of the machinery. Two-dimensional or three-dimensional representation of the machines without any kind of geometrical restrictions can be used in the proposed genetic algorithm. The layout efficiency after machinery relocation could be increased by 58.1%, but due to spatial, financial, and practical constraints, the layout efficiency is only 13.58% higher.