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Purpose Fixed structures in prosthetic dentistry are highly customized products, manufactured individually for patients who have missing teeth. When choosing the technology for fixed dental structure manufacturing, three viable options are available (precise casting, milling and selective laser melting [SLM]). All these technologies can be used to produce a dental structure from CoCr alloy. Besides materials and availability of technologies, economic efficiency is an important factor when choosing a production method. The purpose of this study is to develop an estimation model for achievable productivity of selective laser melting and compare the results with the productivity of conventional manufacturing. Design/methodology/approach Results presented in this paper are based on manufacturing time analysis of an individual case with each of the technologies mentioned above. Because of the efficiency of SLM is highly dependent on how efficiently the work space of the machine is used, this issue was also included in the research. Data used for research were acquired from practical use of each technology in dental applications. Findings Analysis of achievable SLM manufacturing speeds is based on the previous research into manufacturing speeds of additive manufacturing technologies. The presented results present a model that can be used to estimate the productivity of the SLM technology. Research limitations/implications Research was limited to a specific SLM machine type with a fixed workspace volume. Nevertheless, the results show that any SLM machine has to be used as efficiently as possible to be able to be competitive regarding the conventional manufacturing technologies. Practical implications The presented results show clearly at least a rough estimation of what kind of parts and in what volume will be manufactured with an SLM machine prior to buying one. Social implications Results can help to widen the economically efficient way of running SLM machines, replacing conventional manufacturing for medical applications especially with complicated cases. Originality/value A method is presented to adapt the estimation model to a particular real-life production scenario. This method can be used to establish how efficiently selective laser sintering can be used and if using SLM machine instead of conventional manufacturing would be economically viable.

In this work single-objective, multi-objective and multi-parameter optimization models of a widely used modern machining process namely abrasive waterjet machining process are solved using a newly proposed optimization algorithm named Jaya algorithm. In order to solve the multi-objective optimization models, a posteriori version of Jaya algorithm named as “Multi-objective Jaya (MO-Jaya) algorithm” is used. Two optimization case studies of abrasive waterjet machining process are considered and the results of Jaya and MO-Jaya algorithms are found to be better than the results of well-known optimization algorithms such as simulated annealing (SA), particle swam optimization (PSO), firefly algorithm (FA), cuckoo search (CS) algorithm, blackhole (BH) algorithm, bio-geography based optimization (BBO) algorithm, non-dominated sorting genetic algorithm (NSGA), non-dominated sorting teaching-learning-based optimization (NSTLBO) algorithm and sequential approximation optimization (SAQ). A set of Pareto-efficient solutions is obtained for each of theconsidered multi-objective optimization problems using MO-Jaya algorithm and the same is reported in this work. Hypervolume performance metric is used to compare the quality of the Pareto-front provided by MO-Jaya algorithm to the Pareto-front provided by NSGA and NSTLBO algorithms.

In this work, the process parameters optimization problems of abrasive waterjet machining process are solved using a recently proposed metaheuristic optimization algorithm named as Jaya algorithm and its posteriori version named as multi-objective Jaya (MO-Jaya) algorithm. The results of Jaya and MO-Jaya algorithms are compared with the results obtained by other well-known optimization algorithms such as simulated annealing, particle swam optimization, firefly algorithm, cuckoo search algorithm, blackhole algorithm and bio-geography based optimization. A hypervolume performance metric is used to compare the results of MO-Jaya algorithm with the results of non-dominated sorting genetic algorithm and non-dominated sorting teaching–learning-based optimization algorithm. The results of Jaya and MO-Jaya algorithms are found to be better as compared to the other optimization algorithms. In addition, a multi-objective decision making method named PROMETHEE method is applied in this work in order to select a particular solution out-of the multiple Pareto-optimal solutions provided by MO-Jaya algorithm which best suits the requirements of the process planer.

Multi-objective optimization aspects of four modern machining processes namely wire-electro discharge machining process, laser cutting process, electrochemical machining process and focused ion beam micro-milling process are considered in this work. In WEDM process cutting velocity and surface quality are important objectives which are mutually conflicting in nature. Minimization of kerf taper is vital in the laser cutting process which increases with the increase in material removal rate. The ECM process is characterized by high material removal rate, but poor dimensional accuracy, high tool wear rate and high over cut. FIB micro-milling process is useful in applications where a nano-level surface finish is desired but this process is characterized by a very low material removal rate. All the above mentioned objectives are vital as they closely govern the performance of the machining processes considered in this work. Therefore, the aim of this work is to achieve these objectives through process parameter optimization. In order to handle multiple objectives simultaneously a new posteriori multi-objective optimization algorithm named as multi-objective Jaya (MO-Jaya) algorithm is proposed which can provide multiple optimal solutions in a single simulation run. The regression models for the above mentioned machining processes which were developed by previous researchers are used as fitness function for MO-Jaya algorithm.In the case of WEDM process the optimization problem is an unconstrained, linear and parameter bounded. In the case of laser cutting process the optimization problem is a non-linear, unconstrained, quadratic and parameter bounded. In the ECM process the optimization problem is a non-linear, unconstrained, quadratic and parameter bounded. The second case study of ECM process the optimization problem is a non-linear, constrained, non-quadratic and parameter bounded. In the case of FIB micro-milling process, the optimization problem is a non-linear, unconstrained, quadratic and parameter bounded. In addition, the performance of MO-Jaya algorithm is also tested on a non-linear, non-quadratic unconstrained multi-objective benchmark function of CEC2009. In order to handle the constraints effectively a heuristic approach for handling constraints known as the constrained-dominance concept is used in MO-Jaya algorithm. In order to ensure that the newly generated solutions are within the parameter bounds a parameter-bounding strategy is used in MO-Jaya algorithm. The results of MO-Jaya algorithm are compared with the results of GA, NSGA, NSGA-II, BBO, NSTLBO, PSO, SQP and Monte Carlo simulations. The results have shown the better performance of the proposed algorithm. Flowchart for the MO-Jaya algorithm.Display Omitted A new multi-objective optimization algorithm named as MO-Jaya algorithm is proposed.Multi-objective optimization aspects of modern machining processes are considered.A Pareto optimal set of solutions along with a Pareto front is obtained for each of the considered machining processes.The MO-Jaya algorithm may also be applied to multi-objective optimization problems of other manufacturing processes.

Material removal rate and surface roughness are the most important performance measures in nano-finishing processes and these are largely influenced by the process parameters. The optimum combination of process parameters for nano-finishing processes is determined in this paper using a recently proposed optimization algorithm, named as Jaya algorithm. The results show the better performance of the Jaya algorithm over the other approaches attempted by the previous researchers such as genetic algorithm and desirability function approach for the same nano-finishing processes. The results obtained by the Jaya algorithm are useful for the real production systems.

Purpose This paper aims to present a comparison between selective laser sintering and injection moulding technology for the production of small batches of plastic products. Design/methodology/approach The comparison is based on analysing the time–cost efficiencies of each manufacturing process regarding the size of the series for the selected product sample. Both technologies are described and the times and costs of those individual processes needed to create a final product are assessed when using each of the manufacturing processes. Findings The study shows that the time-cost efficiency of the selected laser sintering technology increases according to the complexity of the product and decreases with increasing series size and product volume. Research limitations/implications The study and absolute values of the presented results are limited to a selected plastic product, but the series size-focused efficiency analysis could be expanded to general cases. Originality/value The presented analysis could be used as a general guideline for a decision-making process regarding the more efficient manufacturing method. In addition, the results show the viability of using selective laser sintering during the early stages of production when fast product availability is required, regardless of the series size. Also, some complementary effects of using both technologies in the serial production of the same part are discussed.

With the technology of Additive manufacturing (AM) still developing and more and more AM machines and technologies becoming available, it is very important to know accurate data about every machine to make the right choice for your product. This paper will focus on selective laser sintering (SLS) and more specifically the influence of the laser speed on the average manufacturing speed.

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 ...

This paper proposes the modelling of a turning process using particle swarm optimization (PSO). The independent input machining parameters for the modelling were cutting speed, feed rate, and cutting depth. The input parameters affected three dependent output parameters that were the main cutting force, surface roughness, and tool life. The values of the independent and dependent parameters were acquired by experimental work and served as knowledge base for the PSO process. By utilizing the knowledge base and the PSO approach, various models could be acquired for describing the cutting process. In our case, three different polynomial models were obtained: models a) for the main cutting force, b) for surface roughness, and c) for tool life. All the models had exactly the same basic polynomial form which was chosen similarly to that in the conventional regression analysis method. The PSO approach was used for optimization of the polynomials' coefficients. Several different randomly-selected data sets were used for the learning and testing phases. The accuracies of the developed models were analysed. It was discovered that the accuracies of the models for different learning and testing data sets were very good, having almost the same deviations. The least deviation was noted for the cutting force, whilst the most deviation, as expected was for tool life. The obtained models could then be used for later optimization of the turning process.

This paper presents reverse engineering of car volume button. The purpose of article is to introduce reverse engineering procedure, what we need to do this kind of procedure and how we can remanufacture car's volume button. The purpose of reverse engineering is to manufacture another object based on a physic and existing object for which 3D CAD is not available. The first we need digital version of object. Because our car's volume button has free formed surfaces we decided to use 3D scanning technology to obtain the point cloud of existing object. With the help of point cloud we can developed 3D CAD model which will be used for manufacturing of button pair. We used for manufacturing of pair of buttons machine for selective laser sintering Formiga P 100. In the paper are also described costs of making of one pair of buttons and whole workspace.

Paper is presenting computer aided machining of models for moulding out of synthetic materials. Goal of the project was to produce a manikin (dummy) from aluminium alloy for testing protective clothes for intervention services. Usage of state-of-the-art software for CAD modeling and CAM machining has been applied. At planning of the machining strategies, the software package UGS NX 7 has been used. Machining of certain models has been done on CNC machining centre Heller BEA 1. Due to dimensional restrictions on the machine workspace we had to part up the models on smaller segments. The model pieces have been made out of synthetic material Sikablock® M700. Producing of the models was pretentious and extensive. In project were successfully captured demands for aluminium casting, with these basics we produced objects and produced final casts.

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. Bresenham's 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.

Članak prikazuje savijanje lima u dvije faze i predviđanje konačnog kuta savijanja pomoću usmjerene neuronske mreže. Glavni cilj je bio istražiti tehnološke parametre savijanja lima u dvije faze i razviti inteligentan način, koji će omogućiti predviđanje tih tehnoloških parametara. Prikazan je proces savijanja lima u dvije faze, gdje se prikazuju i razni tehnološki parametri i ispitni alati sa kojima su provedena ispitivanja i mjerenja. Rezultati ispitivanja i mjerenja su bili ključ u donošenju procjene pojedinih tehnoloških parametara. Opisano je predviđanje konačnog kuta savijanja lima korištenjem usmjerene neuronske mreže, koja prima signale na ulazu. Ti signali tada prolaze kroz skrivenu razinu do izlaza, gdje dobiju odgovor na ulazne signale. Za ulaz u neuronsku mrežu upotrebljavaju se podaci koji utječu na odabir kuta konačnog savijanja. Za neuronsku mrežu se koristi pet različitih inputa. Odabirom željenog kuta savijanja pomoću neuronske mreže, može se doprinijeti optimizaciji savijanja lima u dvije faze., This paper describes sheet metal bending in two stages as well as predicting and testing of the final bend angle by means of a feed-forward neural network. The primary objective was to research the technological parameters of bending sheet metal in two stages and to develop an intelligent method that would enable the predicting of those technological parameters. The process of bending sheet metal in two stages is presented by demonstrating the various technological parameters and the test tool used to carry out tests and measurements. The results of the tests and measurements were of decisive guidance in the evaluation of individual technological parameters. Developed method for prediction of the final bend angle is based on a feed-forward neural network that receives signals at the input level. These signals then travel through the hidden level to the output level, where the responses to input signals are received. The input to the neural network is composed of data that affect the selection of the final bend angle. Only five different inputs are used for the total neural network. By choosing the desired final bend angle by means of the trained neural network, bending sheet metal in two stages is optimised and made more efficient.

In the tooling industry, there is often the need to repair damaged or worn out components, or to apply coatings on functional surfaces of tools. When depositing a material which properties are very different from the properties of the substrate material, difficulties, such as cracks, can occur. In this paper, we investigate the deposition of three different powders (Metco 15 E, Colmony 88 and VIM CRU 20) on cast irons and low carbon steel with the laser engineered net shaping (LENS™) technology. Coatings with a maximum of four layers were deposited with different process parameters. Although most bonds itself were of good quality, some coatings had numerous cracks. Preheating of the cast iron samples with the laser beam was used to reduce the number of cracks. In the preheating process, the surface of samples was partially melted in order to dissolve graphite nodules which were often the starting point of cracks.

PurposeThe purpose of this paper is to establish a general method for achievable speed and accuracy evaluation of additive manufacturing (AM) machines and an objective comparison among them.Design/methodology/approachFirst, a general schematic is defined that enables description of all currently available AM machines. This schematic is used to define two influential factors describing certain parts' properties regarding the machines' yield during manufacturing. A test part is defined, that will enable testing the influence of these factors on the speed and accuracy of manufacturing. A method for implementing and adapting test parts is established for individual machine's testing. This method was used to test four different machines that are predominantly used in Slovenia at the moment.FindingsResearch has proven that the machine's yield had a predominant influence on the achievable manufacturing speeds of all the tested machines. In addition, the results have shown different ranges of achievable manufacturing speeds for individually tested machines. Test parts' measurement results have shown comparable achievable accuracies for all the tested machines.Research limitations/implicationsSpeed evaluation is based on a 2k factorial design that assumes the linearity among individual points of the experiment. This design was chosen to keep the method as simple and quick as possible, in order to perform testing on those machines otherwise used in industrial environments. Accuracy evaluation was limited by a rather small sample size of ten fabricated test parts per machine.Practical implicationsThe presented evaluation method can be used on any existing or future type of AM machine, and their comparative placement regarding achievable manufacturing speed and accuracy.Originality/valueThe presented method can be used to evaluate a machine regardless of the AM technology on which it is based.

The presented research removes common constraints regarding the design of layout of flexible manufacturing system, and the subsequent search for a good solution is left solely to artificial intelligence. The proposed system is composed of a creative subsystem which can use different evolutionary optimization methods, and a subsystem for evaluating layouts. In the presented work the subsystem for creation uses a particle swarm optimization method for the creation/modification of solution sets. Evaluation of solution quality is made using intelligent search of the shortest travel paths within the layout. This system has proved to be innovative since it proposes very good solutions which are oriented to the main task of the system and are not simplified because of human limitations.

Optimization problem of an important traditional machining process namely surface grinding is considered in this work. The performance of machining processes in terms of cost, quality of the products and sustainability of the process is largely influenced by its process parameters. Thus, choice of the best (optimal) combination machining parameters is vital for any machining process. Hence, in present work a new algorithm is used for solving the considered optimization problem. The Jaya algorithm is a simple yet powerful algorithm and is a algorithm-specific parameter-less algorithm. The comparison of results of optimization show that the results of Jaya algorithm are better than the results reported by previous researchers using GA, SA, ABC, HS, PSO, ACO and TLBO.

U radu se predstavlja razvijeni inteligentni sustav za predviđanje mehaničkih svojstava materijala na temelju metalografskih slika. Sustav se sastoji od dva modula. Prvi je modul algoritam za dobivanje karakteristika iz metalografskih slika. Prvi algoritam očitava metalografsku sliku dobivenu mikroskopom, zatim se dobivaju karakterisike razvijenim algoritmom, i na kraju algoritam izračunava omjere mikrostrukture materijala. U ovom istraživanju potrebno je što točnije odrediti omjere grafita, ferita i ausferita iz metalografskih slika. Drugi modul razvijenog sustava je sustav za predviđanje mehaničkih svojstava materijala. Predviđanje mehaničkih svojstava materijala izvršeno je pomoću feed-forward umjetne neuronske mreže. Kao ulazi u umjetnu neuronsku mrežu rabljeni su izračunati omjeri grafita, ferita i ausferita, dok su mehanička svojstva materijala upotrebljena kao ciljevi za uvježbavanje. Uvježbavanje umjetnih neuronskih mreža obavljeno je na prilično maloj bazi podataka, no mijenjajući parametre nama je to uspjelo. Umjetna neuronska mreža je naučila do te mjere da je greška bila prihvatljiva. S orijentiranom neuronskom mrežom uspješno smo predvidjeli mehanička svojstva izuzetog uzorka., This article presents developed intelligent system for prediction of mechanical properties of material based on metallographic images. The system is composed of two modules. The first module of the system is an algorithm for features extraction from metallographic images. The first algorithm reads metallographic image, which was obtained by microscope, followed by image features extraction with developed algorithm and in the end algorithm calculates proportions of the material microstructure. In this research we need to determine proportions of graphite, ferrite and ausferrite from metallographic images as accurately as possible. The second module of the developed system is a system for prediction of mechanical properties of material. Prediction of mechanical properties of material was performed by feed-forward artificial neural network. As inputs into artificial neural network calculated proportions of graphite, ferrite and ausferrite were used, as targets for training mechanical properties of material were used. Training of artificial neural network was performed on quite small database, but with parameters changing we succeeded. Artificial neural network learned to such extent that the error was acceptable. With the oriented neural network we successfully predicted mechanical properties for excluded sample.

Most CAD/CAM and computer-aided process planning systems manipulate all geometrical features on the part equally. In the area of free form machining, lack of efficient methodology for assessing the degree of manufacturing pretentiousness of free form features is still noticeable. Developing this methodology inside CAD/CAM systems brings the following benefits to the tool shop praxis: it minimizes the number of set-ups and tool changes and at the same time ensures the right sequence of machining strategies in order to achieve the best possible surface quality in the machining area. Based on this assessment, the CAD/CAM process will also be greatly simplified. When there are an increased number of non-prismatic and non-cylindrical features, this problem is even more exaggerated, and its solution cannot be found in the framework of analytical mathematics. This paper reports a neuro-fuzzy model that uses the concept of “feature manufacturability” to identify and recognize the degree of “pretentiousness—difficulty of machining”. The model is created by means of the construction of parametric fuzzy membership functions, based on neural networks learning process. This makes possible simultaneous evaluation of features complexity in a CAD model and manufacturing capability in an environment description model.

The paper presents a model of designing of the flexible manufacturing system (FMS) in one or multiple rows with genetic algorithms (GAs). First the reasons for studying the layout of devices in the FMS are discussed. After studying the properties of the FMS and perusing the methods of layout designing the genetic algorithms methods was selected as the most suitable method for designing the FMS. The genetic algorithm model, the most suitable way of coding the solutions into the organisms and the selected evolutionary and genetic operators are presented. In the model, the automated guided vehicles (AGVs) for transport between components of the FMS were used. In this connection, the most favourable number of rows and the sequence of devices in the individual row are established by means of genetic algorithms. In the end the test results of the application made and the analysis are discussed.

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.

The scope of the paper is to construct an autonomous, intelligent CAD/CAM programming system for the cutting device controller (for instance a CNC laser cutting machine tool) based on evolutionary methods. The CNC cutting device should be able to optimise paths autonomously between cutting trajectories, determined by the product's CAD model. An evolutionary GA was used for this purpose.

The paper proposes a new optimization technique based on genetic algorithms (GA) for the determination of the cutting parameters in machining operations. In metal cutting processes, cutting conditions have an influence on reducing the production cost and time and deciding the quality of a final product. This paper presents a new methodology for continual improvement of cutting conditions with GA. It performs the following: the modification of recommended cutting conditions obtained from a machining data, learning of obtained cutting conditions using neural networks and the substitution of better cutting conditions for those learned previously by a proposed GA. Experimental results show that the proposed genetic algorithm-based procedure for solving the optimization problem is both effective and efficient, and can be integrated into an intelligent manufacturing system for solving complex machining optimization problems.

In the paper we propose a fundamental shift from the present manufacturing concepts and problem solving approaches towards new manufacturing paradigms involving phenomena such as emergence, intelligence, non-determinism, complexity, self-organization, bottom-up organization, and coexistence with the ecosystem. In the first part of the paper we study the characteristics of the past and the present manufacturing concepts and the problems they caused. According to the analogy with the terms in cognitive psychology four types of problems occurring in complex manufacturing systems are identified. Then, appropriateness of various intelligent systems for solving of these four types of problems is analyzed. In the second part of the paper, we study two completely different problems. These two problems are (1) identification of system in metal forming industry and (2) autonomous robot system in manufacturing environment. A genetic-based approach that imitates integration of living cells into tissues, organs, and organisms is used. The paper clearly shows how the state of the stable global order (i.e., the intelligence) of the overall system gradually emerges as a result of low-level interactions between entities of which the system consists and the environment.

Dimensional stability of forming processes is becoming more and more important in the modern production world. Especially when mass production is concerned, the technological system has to be reliable and accurate. Growing market demands are forcing production engineers towards process optimisation in order to achieve high machinery efficiency and reduce the production costs. An important precondition for improving the process chain is the prediction of process behaviour in advance. The paper is presenting the use of genetic programming to predict the wire geometry after forming. The results can be used as the basis for later optimisation of forming processes.

Dimensional stability of forming processes is becoming very important in the modern manufacture. It is particularly in mass manufacture that technological systems have to be most reliable and accurate. Growing market demands are pushing the manufacturing engineers towards process optimization in order to achieve high machinery efficiency and reduce manufacturing costs. Predicting the process behavior is an important precondition for having it improved. The paper presents the use of genetic programming for forecasting the wire geometry after forming. The obtained results are the basis for later optimization of forming processes.

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 Siebel's 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.

The paper describes the design solution, operation and analysis of a new NC controller for a new step-by-step milling procedure. A step-by-step milling device ensures that the milling of workpieces by end or conical milling cutters, where the ratio between the depth of milling a (mm) and the milling cutter diameter D (mm) is greater than 1.5 (a/D > 1.5) results in the increased wear resistance of the cutting edge. Breaking of the milling cutter is minimised and is not frequent and the milling forces are reduced, which results in smaller deflections of the milling tool and higher accuracy of machining. The machine tool use is better.

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.

The expert system STATEXS is presented for dimensioning, optimization and manufacture of gears and gearings. The optimum dimensions of the gearing were determined using genetic algorithms, well suited to such problems especially because of their robustness and their ability to detect global extremes. After completion of the calculations and optimization of the gears or gear pairs, there follows one of the most difficult operations, the manufacture of the product with theoretically determined and optimized properties. To this end we have also started to use the genetic algorithm approach for the manufacture of various products with demanding shapes.

The presented chapter introduces intelligent methods, which can be used for designing and managing of modern warehouses. Because of the ever-increasing complexity of such systems, the traditional methods cannot assure optimal or near-optimal solutions in design and operation. Demands for high utilization, flexibility, and the capacity to work reliably, even in changeable environments, can be met by adding intelligence to artificial system. The most promising intelligent methods are evolutionary computation and swarm intelligence which are unique methods of non-deterministic solving and optimizing. They proved to be effective and robust for planning and management of real systems. Evolutionary computation and swarm intelligence are methods, which were obtained from the observation of nature. Nature has some of the best answers to the problem of design and management. Therefore, this chapter tries to present intelligent methods to wider audience, and especially to experts and students of warehousing design and management.