1. Metallurgical and geometric properties controlling of additively manufactured products using artificial intelligenceSnehashis Pal, Igor Drstvenšek, 2021, original scientific article Abstract: This article has presented a technical concept for producing precisely desired Additive
Manufactured (AM) metallic products using Artificial Intelligence (AI). Due to the stochastic
nature of the metallic AM process, which causes a greater variance in product properties
compared to traditional manufacturing processes, significant inaccuracies in metallurgical
properties, as well as geometry, occur. The physics behind these phenomena are related to
the melting process, bonding, cooling rate, shrinkage, support condition, part orientation.
However, by controlling these phenomena, a wide range of product features can be achieved
using the fabricating parameters. A variety of fabricating parameters are involved in the
metal AM process, but an appropriate combination of these parameters for a given material
is required to obtain an accurate and desired product. Zero defect product can be achieved
by controlling these parameters by implementing Knowledge-Based System (KBS). A suitable
combination of manufacturing parameters can be determined using mathematical tools with
AI, considering the manufacturing time and cost. The knowledge required to integrate AM
manufacturing characteristics and constraints into the design and fabricating process is beyond
the capabilities of any single engineer. Concurrent Engineering enables the integration of design
and manufacturing to enable trades based not only on product performance, but also on other
criteria that are not easily evaluated, such as production capability and support. A decision
support system or KBS that can guide manufacturing issues during the preliminary design
process would be an invaluable tool for system designers. The main objective of this paper is to
clearly describe the metal AM manufacturing process problem and show how to develop a KBS
for manufacturing process determination.
Keywords: metallurgical properties, geometry, additive manufacturing, artificial intelligence, knowledge-based system
Published in DKUM: 25.09.2024; Views: 0; Downloads: 9
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3. Mathematical model for the selection of processing parameters in selective laser sintering of polymer productsAna Pilipović, Igor Drstvenšek, Mladen Šercer, 2014, original scientific article Abstract: Additive manufacturing (AM) is increasingly applied in the development projects from the initial idea to the finished product. The reasons are multiple, but what should be emphasised is the possibility of relatively rapid manufacturing of the products of complicated geometry based on the computer 3D model of the product. There are numerous limitations primarily in the number of available materials and their properties, which may be quite different from the properties of the material of the finished product. Therefore, it is necessary to know the properties of the product materials. In AM procedures the mechanical properties of materials are affected by the manufacturing procedure and the production parameters. During SLS procedures it is possible to adjust various manufacturing parameters which are used to influence the improvement of various mechanical and other properties of the products. The paper sets a new mathematical model to determine the influence of individual manufacturing parameters on the polymer product made by selective laser sintering. Old mathematical model is checked by statistical method with central composite plan and it is established that old mathematical model must be expanded with new parameter beam overlay ratio. Verification of new mathematical model and optimization of the processing parameters are made on SLS machine.
Keywords: manufacturing system, inteligent methods, 3D scanning
Published in DKUM: 12.07.2017; Views: 1204; Downloads: 395
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4. Automated and intelligent programming of cnc machine tools : doctoral thesisAfrim 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: 3247; Downloads: 418
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5. Relational database as a cogitative part of an intelligent manufacturing systemIgor Drstvenšek, Mirko Ficko, Jože Balič, 2004, original scientific article Abstract: An intelligent manufacturing system is intended to produce one or more subjects that it is designed for in an optimal way. This means that it has to find a proper production process to produce the subject in an optimal way. The manufacturing system can be called "intelligent" when it is able to find applicable optimisation criteria upon its past experiences thus improving its performance in future. Therefore, an intelligent manufacturing system needs capabilities to store data and make decisions upon them. Such a "brain" can be established by a proper design of a technological database and its database management system (DBMS). Examining all constitutive parameters of a work operation a model of a production process organization can be made, which can serve as a basis for a suitable database design. In addition, an application programme that will check the existence and availability of work operations in the database has to be added to the DBMS. What remains are some optimisation criteria upon which we will choose an operation among suitable and available work operations. This task is fulfilled by a genetic algorithm optimisation technique that would consider work operations' data as parameters of optimisation and on this basis search the optimal one out of the set of available operation.
Keywords: production processes, optimisation, relational database, database management systems, production automation, manufacturing system
Published in DKUM: 01.06.2012; Views: 2034; Downloads: 135
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6. Databases for technological information systemsFranc Čuš, Bogomir Muršec, 2004, original scientific article Abstract: Organization of tool management for mixed production includes today, in particular, the computer-supported management and organization of the flow of tools and data on them. The system supports the entire flow of tools in a production process including the tool store management, commissioning, mounting, dismantling and pre-setting of tools. The system contains the management of the tool database with all vital data on tools and ensures adaption of production requirements for meeting the needs for tools. The integral model for the selection of optimal cutting conditions in the computer aided tool management system (TOMS) is proposed. The integration of technological databases and tool management systems is urgently necessary. The target function for the OPTIS programme, worked out by the programme package Microsoft Visual Basic, is selection of optimal cutting conditions from commercial databases with respect to the lowest costs of machining by taking into account the technological limitations of the metal removal process. The newly developed OPTIS programme selects optimal cutting conditions with respect to the tool maker, workpiece material, type of machining, cutting machine, smallest and greatest cutting conditions, tool, data on series, type of clamping and workpiece geometry.
Keywords: machining processes, tool system, manufacturing systems, technological information systems, databases, tool management, machining systems, cutting conditions
Published in DKUM: 01.06.2012; Views: 4096; Downloads: 100
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7. Design of row-based flexible manufacturing system with evolutionary computationMirko Ficko, Jože Balič, 2008, published scientific conference contribution Abstract: This paper discusses design of flexible manufacturing systems (FMSs) in one or multiple rows. Evolutionary computation, particularly genetic algorithms (GAs) proved to be successful in search of optimal solution for this type of problems. The model of solution, the most suitable way of coding the solutions into the organisms and the selected evolutionary and genetic operators are presented. 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 (GAs). In the end the test results of the application made and the analysis are discussed.
Keywords: flexible manufacturing system design, genetic algorithms, evolutionary computation, intelligent manufacturing systems, artificial intelligence
Published in DKUM: 31.05.2012; Views: 2248; Downloads: 102
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