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1
Content available remote Forecasting Models of Tool use in Different Intervals of Time
100%
Rojek I.
Management and Production Engineering Review
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2010
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tom Vol. 1, No. 1
31-39
EN
In the paper the forecasting models of tool use in dii erent intervals of time were presented. The models were worked out by the use of hybrid neural networks in the form of: linear neural network (L) - multi-layer networks with error backpropagation (MLP), L network - Radial Basis Function network (RBF), MLP network - RBF network and L network - MLP network - RBF network. The comparison of these models was executed. The effectiveness of forecasting of tool use in different time intervals is the measure of model evaluation. These models are used at the design stage of manufacturing process with the aim to plan production and prevent standstill due to lack of tools, and special tools in particular. The created models were tested on real data from an enterprise.
2
Metody sztucznej inteligencji w rozwoju systemów informatycznych dla przedsiębiorstw produkcyjnych i wodociągowych
100%
Rojek I.
Metody Informatyki Stosowanej
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2009
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tom nr 2 (19)
101-111
PL
W artykule przedstawiono metody sztucznej inteligencji w rozwoju systemów informatycznych dla przedsiębiorstw produkcyjnych i wodociągowych. Obie dziedziny są ważne dla rozwoju gospodarki kraju oraz aktualne na świecie. Interesujące są efekty usystematyzowania tych dziedzin oraz tworzenie narzędzi wspomagania, które prowadzą do opracowania zbioru modeli z udziałem metod sztucznej inteligencji.
EN
In the paper artificial methods in development of information systems for manufacturing and water-supply enterprises were introduced. The classic approaches and the author's approach with use of these methods were presented. The application of artificial intelligence methods is particularly important in critical situation and pronouncement of average in complex technical systems. The improvement of the manufacturing processes and the water-supply systems was made possible through the use of the artificial intelligence methods, new in this area of practice.
3
Content available remote Neural networks as performance improvement models in intelligent CAPP systems
100%
Rojek I.
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tom Vol. 39, no 1
54-68
EN
The paper presents neural networks as performance improvement models in intelligent computer aided process planning systems (CAPP systems). For construction of these models three types of neural networks were used: linear network, multi-layer network with error backpropagation, and the Radial Basis Function network (RBF). The models were compared. Due to the comparison, we can say which type of neural network is the best for selection of tools for manufacturing operations. Tool selection for manufacturing operation is a classification problem. Hence, neural networks were built as classification models, meant to improve tool selection for manufacturing. The study was done for selected manufacturing operations: turning, milling and grinding. Models for the milling operation were presented in detail.
4
Content available remote Komputerowe metody wspomagania projektowania procesu technologicznego
100%
Rojek I.
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2017
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tom R. 90, nr 8-9
805--807
PL
Przedstawiono komputerowe metody wspomagania projektowania procesu technologicznego – od najprostszych do bardzo zaawansowanych. Ideą przeprowadzonych przez autorkę badań było opracowanie metody, modeli oraz systemu ekspertowego, którego funkcjonowanie przypominałoby sposób działania człowieka będącego ekspertem w danej dziedzinie. Cel ten osiągnięto dzięki zastosowaniu sieci neuronowych.
EN
The article presents the computer aided design methods as applied for arrangement of production processes in the range from the simplest to the most advanced ones. The idea behind the research procedure as conducted by the author was to develop a method, models and expert system that would resemble a human expert in the field. This goal was achieved using neural networks.
5
Content available MLP neural nets in design of technological process
100%
Rojek I.
Advances in Manufacturing Science and Technology
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2015
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tom Vol. 39, nr 1
87--95
EN
This paper proposes MLP neural nets to improve technological process design. The first stage of research concerned the creation of models to selection of machine tools, the second stage pertained the creation of models to selection of tools and the third stage concerned the creation of models to selection of machining parameters. In addition, use of tools is forecasted at various time intervals. The models were created using Statsoft STATISTICA Data Miner. These models were compared in order to obtain the best selection. Based on the models, it is possible to create different scenarios of the design of technological process.
PL
W artykule przedstawiono opracowanie sieci neuronowych MLP w celu poprawy projektowania procesu technologicznego. Pierwszy etap dotyczył tworzenia modeli wyboru obrabiarek, drugi modeli wyboru narzędzi i trzeci tworzenia modeli do wyboru parametrów obróbki skrawaniem. Dodatkowo w opracowanych modelach uwzględniono prognozowanie użycia narzędzi w różnych przedziałach czasowych. Stosowano program Statsoft STATISTICA Data Miner. Prowadzono analizy wyników dla poszczególnych modeli i opracowano kryteria doboru. Stwierdzono, że wprowadzenie sieci neronowych umożliwia tworzenie różnych scenariuszy projektowania procesu technologicznego.
6
Learning system of manufacturing knowledge acquisition
100%
Rojek I.
Image Processing & Communications
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2006
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tom Vol. 11, no 1
19-23
EN
In the paper, learning system of manufacturing knowledge acquisition in the range of tool selection to machining operation was presented. Currently often and often research concerning machine learning is developed. Machine learning includes issues of system designing which improves its operations, along with analysis of experience represented by file of learning examples. Recently, machine learning methods have been applied successfully in many practical problems and are becoming a part of advanced information systems - in particular concerning knowledge discovery in databases, and the so-called data mining. Learning system acquires knowledge by using the method of decision trees induction. This method allows approximation of classification functions of discrete output values relating to certain terms, decision classes. The system learns to select tools on the basis of the ones chosen out of the database by process engineering experts for machining operations. In the method of decision trees induction a decision tree is created. This tree allows classification of the whole learning examples file into homogeneous classes. On the basis of the decision tree, decision rules are created. Next, these rules are used in expert system for selection of tools from outside of the file of learning examples.
7
Miejsce baz danych i baz wiedzy w systemie wspomagania decyzji
100%
Rojek I.
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tom Vol. 30, nr 2B
35-47
PL
W artykule przedstawiono miejsce baz danych i baz wiedzy w systemie wspomagania decyzji. Zaprezentowano algorytm działania systemu DSS. System DSS korzystając z baz danych, metod, modeli i wiedzy wspomaga podejmowanie decyzji. Praktycznie został przedstawiony system wspomagania doboru narzędzi do opeŹracji technologicznych.
EN
The position of databases and knowledge bases in decision support system was presented in article. The working algorithm of DSS system was submitted too. The DSS system using database, the methods base, the models base and knowledge base supports decision making process. The system aided selection tools to manufacturing operations was introduced practically.
8
Content available Tooling selection in technological processes using neural networks
100%
Rojek I.
Archives of Mechanical Technology and Materials
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2015
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tom Vol. 35
41--50
EN
The idea of the author’s research is to develop a system aiding the design of a technological process (a CAPP system), namely a system for creation of a technological process plan, in which the sequence of technological operations is defined and for each operation in the technological process, the appropriate machine, tools, tooling and machining parameters are selected. The article discusses accessory selection in technological processes using neural networks. Tooling selection is a necessary stage in the design of technological processes if a tool that has been selected does not fit the machine. Tooling selection models were prepared using unidirectional multilayer neural networks with back propagation of error (MLP) and a self-organizing Kohonen network. Two completely different neural networks were selected for the selection of the tooling. MLP network represents a network with learning supervision, and network Kohonen network learning without supervision. The training data for the neural networks was prepared at a manufacturing company. The neural networks were made using the Statsoft STATISTICA Data Miner software.
PL
Ideą badań autorki jest opracowanie systemu wspomagania projektowania procesu technologicznego (systemu CAPP ), czyli systemu , w którym kolejność operacji technologicznych jest zdefiniowana , a dla każdej operacji następuje odpowiedni dobór obrabiarek, narzędzi, oprzyrządowania oraz parametrów obróbki. W artykule przedstawiono dobór oprzyrządowania narzędziowego przy użyciu sieci neuronowych. Dobór ten jest niezbędnym etapem projektowania procesu w przypadku, gdy dobrane narzędzie nie pasuje na obrabiarkę. Zostały wykonane modele doboru oprzyrządowania przy zastosowaniu sieci neuronowych jednokierunkowych wielowarstwowych ze wsteczną propagacją błędu (MLP) oraz samoorganizującej się sieci Kohonena. Dane do nauczenia sieci neuronowych zostały przygotowane w przedsiębiorstwie produkcyjnym. Sieci neuronowe zostały wykonane przy użyciu oprogramowania Statsoft STATISTICA Data Miner.
9
Content available Machine learning methods for optimal compatibility of materials in ecodesign
71%
Rojek I. , Dostatni E. , Rojek I. , Dostatni E.
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10
Content available remote Sieci neuronowe MLP w badaniu chropowatości Ra i Rz
63%
Rojek I. , Twardowski P.
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2016
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tom R. 89, nr 8-9
1036--1037
PL
Omówiono modele sieci neuronowych jednokierunkowych wielowarstwowych ze wsteczną propagacją błędu (MLP). Modele te zastosowano do oceny chropowatości Ra i Rz. Badania wykonano na danych rzeczywistych wybranego przedsiębiorstwa. Dane te zostały zebrane podczas procesu obróbki rowków pod pierścienie w tłokach silników samochodowych.
EN
The article discusses the models of one-directional multilayer neural networks with error backpropagation (MLP). These models were used to evaluate the surface roughness of Ra and Rz. The study was performed on real data of the selected enterprise. These data were gathered during the process of machining grooves under the rings of the pistons in automobile engines.
11
Content available Failures location within water-supply systems by means of neural networks
63%
Rojek I. , Studziński J.
Journal of Automation Mobile Robotics and Intelligent Systems
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2014
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tom Vol. 8, No. 2
24--28
EN
In the article the neural networks used for failures location for water supply networks are presented. To do this a hydraulic model of the water net, as well as an appropriate developed monitoring system have to be used. The current applications of monitoring systems installed in the waterworks do not realize their possibilities. The monitoring systems provided as autonomic programs to collect and record the information about flows and pressures of water in source pumping stations, in the pump stations bringing up the water pressure inside the water net and in the pipes of water supply network give a general knowledge about state of its work, but if they would be used as elements of IT systems supporting the water network management, they could help to solve the tasks concerning detection and localization of water leaks. The models of failures location in water nets described in the paper are created by means of neural networks in the form of MLP nets.
12
Content available Comparison of different types of neuronal nets for failures location within water-supply networks
63%
Rojek I. , Studziński J.
Eksploatacja i Niezawodność
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2014
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tom Vol. 16, no. 1
42--47
PL
W artykule prezentowane są różne typy sieci neuronowych do lokalizacji awarii w sieci wodociągowej. Obecne wykorzystanie systemów monitorowania nie odpowiada ich możliwościom. Współcześnie systemy monitoringu służą jako autonomiczne programy do zbierania informacji o przepływach i ciśnieniach wody w pompowniach źródłowych, hydroforniach strefowych i końcówkach sieci wodociągowej, dając ogólną wiedzę o stanie jej pracy, gdy jednocześnie mogą i powinny być wykorzystane jako elementy IT systemów zarządzania siecią, w tym w szczególności w zakresie wykrywania i lokalizacji wycieków wody. Modele lokalizacji awarii sieci zostały utworzone przy wykorzystaniu jednokierunkowych sieci neuronowych ze wsteczną propagacją błędu typu MLP i sieci Kohonena.
EN
The different types of neuronal nets for failures location within a water-supply network are presented in the paper. The present utilization of the monitoring systems does not exhaust their possibilities. The monitoring systems operated as autonomic programs gather the information about flows and pressures of water in the source pumping stations, in the zones of hydrophore stations and also in some selected pipes of water network, giving general knowledge about state of its work, when simultaneously they could and should be used as elements of IT systems for network management, and particularly regarding detection and location of hidden water leaks. The models of network failures location are created by means of neuronal nets in the form of MLP and Kohonen nets.
13
Content available Evaluating the reliability of groove turning for piston rings in combustion engines with the use of neural networks
51%
Lisiak P. , Rojek I. , Twardowski P.
Archives of Mechanical Technology and Materials
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2017
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tom Vol. 37
35--40
EN
The article describes a method of evaluating the reliability of groove turning for piston rings in combustion engines. Parameters representing the roughness of a machined surface, Ra and Rz, were selected for use in evaluation. At present, evaluation of surface roughness is performed manually by operators and recorded on measurement sheets. The authors studied a method for evaluation of the surface roughness parameters Ra and Rz using multi-layered perceptron with error back-propagation (MLP) and Kohonen neural networks. Many neural network models were developed, and the best of them were chosen on the basis of the effectiveness of measurement evaluation. Experiments were carried out on real data from a production company, obtained from several machine tools. In this way it becomes possible to assess machines in terms of the reliability evaluation of turning.
14
Content available Computational Intelligence and Optimization Techniques in Communications and Control
45%
Szczepański J. , Klamka J. , Węgrzyn-Wolska K. M. , Rojek I. , Prokopowicz P. , Szczepański J. , Klamka J. , Węgrzyn-Wolska K. M. , Rojek I. , Prokopowicz P.
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