Application of Artificial Neural Networks to Multiple Criteria Inventory Classification

Inventory classification is a very important part of inventory control which represents the technique of operational research discipline. A systematicapproach to the inventory control and classification may have a significant influence on company competitiveness. The paper describes the results obtained by investigating the application of neural networks in multiple criteria inventory classification. Various structures of a back-propagation neural network have been analysed and the optimal one with the minimum Root Mean Square error selected. The predicted results are compared to those obtained by the multiple criteria classification using the analytical hierarchy process

Estimation of CNC Grinding Process Parameters Using Different Neural Networks

Continuation of research on solving the problem of estimation of CNC grinding process parameters of multi-layer ceramics is presented in the paper. Heuristic analysis of the process was used to define the attributes of influence on the grinding process and the research model was set. For the problem of prediction - estimation of the grinding process parameters the following networks were used in experimental work: Modular Neural Network (MNN), Radial Basis Function Neural Network (RBFNN), General Regression Neural Network (GRNN) and Self-Organizing Map Neural Network (SOMNN). The experimental work, based on real data from the technological process was performed for the purpose of training and testing various architectures and algorithms of neural networks. In the architectures design process different rules of learning and transfer functions and other attributes were used. RMS error was used as a criterion for value evaluation and comparison of the realised neural networks and was compared with previous results obtained by Back-Propagation Neural Network (BPNN). In the validation phase the best results were obtained by Back-Propagation Neural Network (RMSE 12,43 %), Radial Basis Function Neural Network (RMSE 13,24 %,), Self-Organizing Map Neural Network (RMSE 13,38 %) and Modular Neural Network (RMSE 14,45 %). General Regression Neural Network (RMSE 21,78 %) gave the worst results

Primjena ekspertnih sustava kod određivanja najpovoljnijeg dobavljača u pojedinačnoj proizvodnji

Today\u27s business life tempo is faster than ever, so the management of production systems has to make the proper decisions as soon as possible. Relevance of time by and reliable decisions cannot supervene from inadequate models of production management based on unreliable and unfulfilled data. Experiences from single production often show lateness, because of complex production conditions that are caused by numbers of factors, which lead to significant deviations in due dates and engagement of unplanned extra work with increased production costs. The paper aimed to show the application of expert system for determination of the most beneficial suppliers in the single and the small scale production. The proposed model which is based on expert system takes into consideration different attribute types (e.g. price, due date, discount etc.) and their values which increase the model reliability and complexity. Through the applying of expert system for determination of the most beneficial suppliers the contribution is made especially to the purchase and operational preparation departments in the single production enterprises.U današnje vrijeme kada je tempo života u poslovnom svijetu brži nego ikad, od rukovodstva proizvodnih sustava traži se da u što kraćem roku donese što pouzdanije odluke. Važnost donošenja pravovremenih i pravovaljanih odluka ne može proizići iz neadekvatnih modela upravljanja proizvodnjom koji se temelje na nepouzdanim i nepotpunim podacima. Iskustva iz pojedinačne proizvodnje pokazala su česta kašnjenja zbog složenih uvjeta proizvodnje na koju utječe veliki broj faktora koji dovode do značajnih odstupanja rokova isporuke, što dovodi do potrebe za neplanskim angažiranjem dodatnog rada s povećanim troškovima proizvodnje. U radu se daje mogućnost primjene ekspertnog sustava kod određivanja najpovoljnijeg dobavljača u pojedinačnoj proizvodnji. Predloženi model određivanja najpovoljnijeg dobavljača utemeljen je na ekspertnom sustavu koji uzima u obzir različite vrste atributa (npr. cijenu, rok isporuke, popust itd. i njihovih vrijednosti koje povećavaju pouzdanost i složenost modela. Primjenom ekspertnog sustava kod određivanja najpovoljnijeg dobavljača dan je prilog kako funkciji nabave tako i operativnoj pripremi u poduzećima u pojedinačne proizvodnje

Researching and designing of hardware and software supports for an educational flexible cell

U radu je istražena mogućnost povezivanja postojeće opreme iz Laboratorija za računalom integriranu proizvodnju s ciljem dobivanja nove kvalitete u edukaciji studenata. Analizirani su problemi i postavljeni ciljevi u realizaciji edukacijske fleksibilne stanice kroz integraciju CNC tokarilice i robotskog sustava. Predložen je način oblikovanja sklopovske i programske podrške i pojašnjene su prednosti i nedostaci danog rješenja: stezanja, prilagodbe prstiju prihvatnice robota, povezivanja upravljačkih jedinica robota i CNC tokarilice, te izrade programskih rješenja za postavljeni tehnološki problem. U eksperimentalnom dijelu rada testirano je predloženo sklopovlje. Po prihvaćenom sklopovskom rješenju provedena je simulacija programskog rješenja na razini upravljačkog sustava robota, a potom i testiranje predložene integrirane cjeline, odnosno sinkroniziran rad edukacijske fleksibilne stanice kod izrade predloženog radnog predmeta.The paper shows the possible ways of using existing laboratory equipment with the main goal to increase the quality of education and training of students in the work with the flexible cell. Problems were analyzed and goals were set especially in a way of mutual communication, necessary links and integration problems that were taken into consideration between CNC lathe and the robot system. Advantages and disadvantages of the proposed solution will be shown in a way of tightening, adjustments of robot fingers, connections between robot and CNC lathe control units and software solutions. The testing of proposed hardware has been done through the experimental part. According to accepted and realised hardware solution, the robot simulation was done first and it was followed by the testing and synchronisation of the overall system

Surface quality prediction by artificial-neural-networks

Površinska hrapavost se često uzima kao pokazatelj kvalitete obrađivanih radnih predmeta. Postizanje željene kvalitete površine od velike je važnosti za obavljanje funkcije proizvoda. U radu je promatran utjecaj obrađivanog materijala, alata, dubine rezanja, posmaka i brzine rezanja na hrapavost obrađivane površine. Prikupljeni rezultati eksperimentalnih istraživanja, na radnom predmetu „razvodnik uređaja za ronjenje“, poslužili su za procjenjivanje površinske hrapavosti primjenom neuronskih mreža. Analizirane su različite neuronske mreže širenja unazad, te je izabrana mreža s najmanjom razinom RMS (eng. Root Mean Square) greške. Procjena površinske hrapavosti koju daje model, može olakšati rad manje iskusnim tehnolozima i na taj način skratiti vrijeme tehnološke pripreme proizvodnje.Surface roughness is often taken as an important indicator of the quality of machined parts. Achieving the desired surface quality is of great importance for the product function. In this paper, influence of material, type of tool, cutting depth, feed rate and cutting speed on surface roughness is observed. Collected results of experimental research are utilized for surface roughness prediction using neural networks. Various structures of a back-propagation neural network have been analyzed and the network with the minimum RMS error has been selected. Evaluation of surface roughness obtained by neural networks model can help to less experienced technologists and therefore production preparation technological time can be shorter

Cefpodoxime proxetil as a therapeutic option in switching therapy for infective endocarditis in children: case reports and literature review

Infective endocarditis (IE) is uncommon in children, affecting predominantly subjects with congenital heart disease (CHD) and patients with indwelling central lines. The principles of antibiotic treatment in paediatric population are similar to those in adults. Prolonged intravenous administration of bactericidal rather than bacteriostatic agents is preferred. Outpatient intravenous therapy after initial treatment in the hospital may be considered only in selected patients. Partial oral treatment has been described in cases of left-sided, uncomplicated IE caused by common pathogens in adult patients. There are no guidelines or trials in paediatric population regarding switching therapy from intravenous to oral route. We present two cases of IE in children caused by uncommon pathogenic bacteria (Abiotrophia defectiva and Haemophilus parainfluenzae) successfully treated with oral third-generation cephalosporin - cefpodoxime proxetil after initial intravenous therapy. This paper provides observations on different therapeutic approach for IE in children as well as another potential use of cefpodoxime proxetil

Prilagodljivi neuro-fazi model za predviđanje tehnoloških parametara

The main goal of each technologist is the prediction of technological parameters by fulfilling the set design and technological demands. The work of the technologist is made easier by acquired knowledge and previous experience. A plan of input-output data was made by using the hybrid system of modelling ANFIS (Adaptive Neuro-Fuzzy Inference System) based on the results of seam tube production. This plan is the prerequisite for generating the system of fuzzy logic. The generated system can be used to estimate the output (speed of polishing) based on the given input (external tube diameter, oval shaping of the tube after the first phase of production, gradation of belts for grinding or polishing, condition of belts - time of usage, pressure of belts).The more precise predictions of technological time provided by the model supplement the previously defined manufacturing operations, replace the predictions based on the technologists\u27 experience and form the basis on which to plan production and control delivery times. The work of technologists is thus made easier and the production preparation technological time shorter.Procijeniti tehnološke parametre na način da se ispune postavljeni konstrukcijski i tehnološki zahtjevi cilj je i želja svakog tehnologa. Procjenu tehnologu mogu olakšati prikupljena znanja i ranije stečena iskustva. Na temelju sustavno prikupljenih podataka iz proizvodnje šavnih cijevi u radu je primjenom hibridnog sustava za modeliranje ANFIS (Adaptive Neuro-Fuzzy Inference System) oblikovan plan ulazno/izlaznih podataka. Taj je plan pretpostavka za generiranje sustava neizrazitog zaključivanja. Generirani sustav ima mogućnost procjene izlaza (brzine poliranja) na temelju danih ulaza (vanjski promjer cijevi, ovalnost cijevi nakon prve faze proizvodnje, gradacija remenja za brušenje ili poliranje, stanje remenja - vrijeme uporabe remenja, pritisak remenja). Točnije procjene tehnološkog vremena koje daje model upotpunjavaju prethodno definirane tehnološke operacije, zamjenjuje iskustvene procjene tehnologa i predstavljaju osnovu za planiranje proizvodnje i kontrolu rokova isporuke. Na ovaj se način olakšava rad tehnologa i skraćuje vrijeme tehnološke pripreme proizvodnje

Application of Expert System for Determination of Work Order Priority Class in Piece Production

Analiziranjem stanja u proizvodnim poduzećima pokazalo se kako je u proizvodnji često prisutno neispunjenje obveza prema krajnjem kupcu, kašnjenje u rokovima isporuke, narušavanje ugleda firme i/ili plaćanje visokih penala. Uz neadekvatan model terminiranja koji često puta nema mogućnost izrade varijanti planova kao jedan od najvažnijih uzročnika kašnjenja navedeni su neadekvatni i neprimjenjivi modeli prioriteta. Definiran je pojam prioriteta i predložen novi model klasa prioriteta za pojedinačnu i maloserijsku proizvodnju, baziran na ekspertnim sustavima. Predloženi model u odnosu na postojeća pravila prioriteta uvodi niz novih parametara, ne samo iz službe tehnologije, već i iz službi prodaje i nabave. Klase prioriteta riješene su na razini radnih naloga i uzimaju u obzir različite vrste atributa ali i njihovih vrijednosti.The analysis of the state of affairs in manufacturing companies has shown that in a number of cases the obligations towards end customer remain unfulfilled, that due dates of delivery are not observed, that the image of the firm is damaged and that the penalties are high. Apart from an inadequate scheduling model often lacking the possibility for plan variants to be made, the inadequate and inapplicable priority models have often been stated as the most important reasons for running behind schedule. This paper defines the notion of priority and suggests a new class priority model for the piece and small scale production based on expert system. With regard to the existing priority rules the proposed model introduces a number of new parameters, not only for the technology department but also for the purchasing and sales departments. Priority classes are solved for work orders and consider not only various kinds of attributes but also their values

Usporedba modela za procjenu tehnološkog vremena

The paper sets out to describe the results obtained by investigating the prediction of technological parameters and, indirectly, of technological time needed for seam tube polishing. The results of experimental investigations were used to define, analyse and compare two models. One is a mathematical i.e. statistical model obtained by the application of the least squares method and the least absolute deviation method. The other is a model based on the application of neural networks. To define the model based on the application of neural networks various structures of the back- propagation neural network with one hidden layer were analysed and the optimal one with the minimum RMS error was selected. The more precise predictions of technological time provided by the models supplement the previously defined manufacturing operations, replace the predictions based on the technologists’ experience and form the basis on which to plan production and control delivery times. The work of technologists is thus made easier and the production preparation technological time shorter.U radu su opisani rezultati istraživanja vezani uz procjenjivanje tehnoloških parametara i, neizravno, tehnološkog vremena poliranja šavnih cijevi. Prikupljeni su rezultati eksperimentalnih istraživanja koji su poslužili za definiranje, analizu i usporedbu dvaju modela: matematičkog, odnosno statističkog modela, za čije je postavljanje primijenjena metoda najmanjih kvadrata i metoda najmanjih apsolutnih odstupanja, i modela temeljenog na primjeni neuronskih mreža. Za definiranje modela temeljenog na primjeni neuronskih mreža analizirane su različite strukture neuronske mreže širenja unazad s jednim skrivenim slojem, te je izabrana optimalna s najmanjom razinom RMS greške. Točnije procjene tehnološkog vremena koje daju modeli upotpunjavaju prethodno definirane tehnološke operacije, zamjenjuju iskustvene procjene tehnologa i predstavljaju osnovu za planiranje proizvodnje i kontrolu rokova isporuke. Na ovaj se način olakšava rad tehnologa i skraćuje vrijeme tehnološke pripreme proizvodnje

Influence of tool wear on the mechanism of chips segmentation and tool vibration

The tool wear has a significant impact on the cutting process and therefore tool wear monitoring is especially important for building intelligent machine tools which are capable of assessing their own states and reacting to important changes. This approach is based on the assumption that there exists a relationship between the spectrum of high-frequency vibrations measured on tool holder, in immediate vicinity of the cutting zone, and the tool wear degree. The wear causes changes in tool tip geometry, which has significant influence on the process of chip forming. At the same time, the erratic nature of chip forming process excites the cutting zone, generating a very broad spectrum of vibrations. Due to high input energy, these vibrations are very intensive, and spread through the entire machining system. In the paper, experimental results are shown which pertain to the relationship between the power spectral density (PSD) within 5 kHz to 50 kHz interval.Web of Science20111210