This article presents a computer system for the identification of casting defects using the methodology of Case-Based Reasoning. The system is a decision support tool in the diagnosis of defects in castings and is designed for small and medium-sized plants, where it is not possible to take advantage of multi-criteria data. Without access to complete process data, the diagnosis of casting defects requires the use of methods which process the information based on the experience and observations of a technologist responsible for the inspection of ready castings. The problem, known and studied for a long time, was decided to be solved with a computer system using a CBR (CaseBased Reasoning) methodology. The CBR methodology not only allows using expert knowledge accumulated in the implementation phase, but also provides the system with an opportunity to "learn" by collecting new cases solved earlier by this system. The authors present a solution to the system of inference based on the accumulated cases, in which the main principle of operation is searching for similarities between the cases observed and cases stored in the knowledge base.
The main scope of the article is the development of a computer system, which should give advices at problem of cooper alloys manufacturing. This problem relates with choosing of an appropriate type of bronze (e.g. the BA 1044 bronze) with possible modification (e.g. calcium carbide modifications: Ca + C or CaC2) and possible heat treatment operations (quenching, tempering) in order to obtain desired mechanical properties of manufactured material described by tensile strength - Rm, yield strength - Rp0.2 and elongation - A5. By construction of the computer system being the goal of presented here work Case-based Reasoning is proposed to be used. Case-based Reasoning is the methodology within Artificial Intelligence techniques, which enables solving new problems basing on experiences that are solutions obtained in the past. Case-based Reasoning also enables incremental learning, because every new experience is retained each time in order to be available for future processes of problem solving. Proposed by the developed system solution can be used by a technologist as a rough solution for cooper alloys manufacturing problem, which requires further tests in order to confirm it correctness.
This work presents the project of the application of Case-based reasoning (CBR) methodology to an advisory system. This system should give an assistance by selection of proper alloying additives in order to obtain a material with predetermined mechanical properties. The considered material is silumin EN AC-46000 (hypoeutectic Al-Si alloy) that is modified by the addition of Cr, Mo, V and W elements in the range from 0% to 0.5% in the modified alloy. The projected system should indicate to the user the content of particular additives so that the obtained material is in the chosen range of parameters: tensile strength Rm, yield strength Rp0.2, elongation A and hardness HB. The CBR methodology solves new problems basing on the solutions of similar problems resolved in the past. The advantage of the CBR application is that the advisory system increases knowledge base as the subsequent use of the system. The presented design of the advisory system also considers issues related to the ergonomics of its operation.
This article presents a practical solution in the form of implementation of agent-based platform for the management of contracts in a network of foundries. The described implementation is a continuation of earlier scientific work in the field of design and theoretical system specification for cooperating companies . The implementation addresses key design assumptions - the system is implemented using multi-agent technology, which offers the possibility of decentralisation and distributed processing of specified contracts and tenders. The implemented system enables the joint management of orders for a network of small and medium-sized metallurgical plants, while providing them with greater competitiveness and the ability to carry out large procurements. The article presents the functional aspects of the system - the user interface and the principle of operation of individual agents that represent businesses seeking potential suppliers or recipients of services and products. Additionally, the system is equipped with a bi-directional agent translating standards based on ontologies, which aims to automate the decision-making process during tender specifications as a response to the request.
The article discusses the development of an approximation model of selected plastic and mechanical properties obtained from compression tests of model materials used in physical modeling. The use of physical modeling with the use of soft model materials such as a synthetic wax branch with various modifiers is a popular tool used as an alternative or verification of numerical modeling of bulk metal forming processes. In order to develop an algorithm to facilitate the choice of material model to simulate the behavior of real-metallic materials used in industrial production processes the induction of decision trees was used. First of all, the Statistica program was used for data mining, which made it possible to determine / find the relationship between the percentage of particular constituents of the model material (base material and modifiers) and yield strength, critical and maximum strain, and provide the opportunity to indicate the most important variables determining the shape of the stress – strain curve. Next, using the induction of decision trees, an approximation model was developed, which allowed to create an algorithm facilitating the selection of individual modifying components. The last stage of the research was verification of the correctness of the developed algorithm. The obtained research results indicate the possibility of using decision tree induction to approximate selected properties of modeling materials simulating the behavior of real materials, thus eliminating the need for costly and time-consuming experiments carried out on metallic material.
One way to ensure the required technical characteristics of castings is the strict control of production parameters affecting the quality of the finished products. If the production process is improperly configured, the resulting defects in castings lead to huge losses. Therefore, from the point of view of economics, it is advisable to use the methods of computational intelligence in the field of quality assurance and adjustment of parameters of future production. At the same time, the development of knowledge in the field of metallurgy, aimed to raise the technical level and efficiency of the manufacture of foundry products, should be followed by the development of information systems to support production processes in order to improve their effectiveness and compliance with the increasingly more stringent requirements of ergonomics, occupational safety, environmental protection and quality. This article is a presentation of artificial intelligence methods used in practical applications related to quality assurance. The problem of control of the production process involves the use of tools such as the induction of decision trees, fuzzy logic, rough set theory, artificial neural networks or case-based reasoning.
The aim of this study is to design and implement a computer system, which will allow the semantic cataloging and data retrieval in the field of cast iron processing. The intention is to let the system architecture allow for consideration of data on various processing techniques based on the information available or searched by a potential user. This is achieved by separating the system code from the knowledge of the processing operations or from the chemical composition of the material being processed. This is made possible by the creation and subsequent use of formal knowledge representation in the form of ontology. So, any use of the system is associated with the use of ontologies, either as an aid for the cataloging of new data, or as an indication of restrictions imposed on the data which draw user attention. The use of formal knowledge representation also allows consideration of semantic meaning, a consequence of which may be, for example, returning all elements in subclasses of the searched process class or material grade.
Article present various forms of transfer of information available on the Internet. An attempt was made to show the possibility of such a selection of the knowledge sources that, taking into account user preferences, would arouse his interest, showing in parallel the intended substantive content. This commitment is shown in the context of the current assumptions of building a platform dedicated to support the needs of production processes in foundry and metallurgy.
This article presents the methodology for exploratory analysis of data from microstructural studies of compacted graphite iron to gain knowledge about the factors favouring the formation of ausferrite. The studies led to the development of rules to evaluate the content of ausferrite based on the chemical composition. Data mining methods have been used to generate regression models such as boosted trees, random forest, and piecewise regression models. The development of a stepwise regression modelling process on the iteratively limited sets enabled, on the one hand, the improvement of forecasting precision and, on the other, acquisition of deeper knowledge about the ausferrite formation. Repeated examination of the significance of the effect of various factors in different regression models has allowed identification of the most important variables influencing the ausferrite content in different ranges of the parameters variability.
The problem of materials selection in terms of their mechanical properties during the design of new products is a key issue of design. The complexity of this process is mainly due to a multitude of variants in the previously produced materials and the possibility of their further processing improving the properties. In everyday practice, the problem is solved basing on expert or designer knowledge. The paper is the proposition of a solution using computer-aided analysis of material experimental data, which may be acquired from external data sources. In both cases, taking into account the rapid growth of data, additional tools become increasingly important, mainly those which offer support for adding, viewing, and simple comparison of different experiments. In this paper, the use of formal knowledge representation in the form of an ontology is proposed as a bridge between physical repositories of data in the form of files and user queries, which are usually formulated in natural language. The number and the sophisticated internal structure of attributes or parameters that could be the criteria of the search for the user are an important issue in the traditional data search tools. Ontology, as a formal representation of knowledge, enables taking into account the known relationships between concepts in the field of cast iron, materials used and processing techniques. This allows the user to receive support by searching the results of experiments that relate to a specific material or processing treatment. Automatic presentation of the results which relate to similar materials or similar processing treatments is also possible, which should make the conducted analysis of the selection of materials or processing treatments more comprehensive by including a wider range of possible solutions.
The article describes the problem of selection of heat treatment parameters to obtain the required mechanical properties in heat- treated bronzes. A methodology for the construction of a classification model based on rough set theory is presented. A model of this type allows the construction of inference rules also in the case when our knowledge of the existing phenomena is incomplete, and this is situation commonly encountered when new materials enter the market. In the case of new test materials, such as the grade of bronze described in this article, we still lack full knowledge and the choice of heat treatment parameters is based on a fragmentary knowledge resulting from experimental studies. The measurement results can be useful in building of a model, this model, however, cannot be deterministic, but can only approximate the stochastic nature of phenomena. The use of rough set theory allows for efficient inference also in areas that are not yet fully explored.
The objective of studies presented in this publication was structuring of research knowledge about the ADI functional properties and changes in these properties due to material treatment. The results obtained were an outcome of research on the selection of a format of knowledge representation that would be useful in further work aiming at the design, application and implementation of an effective system supporting the decisions of a technologist concerning the choice of a suitable material (ADI in this case) and appropriate treatment process (if necessary). ALSV(FD) logic allows easy modelling of knowledge, which should let addressees of the target system carry out knowledge modelling by themselves. The expressiveness of ALSV (FD) logic allows recording the values of attributes from the scope of the modelled domain regarding ADI, which is undoubtedly an advantage in the context of further use of the logic. Yet, although the logic by itself does not allow creating the rules of knowledge, it may form a basis for the XTT format that is rule-based notation. The difficulty in the use of XTT format for knowledge modelling is acceptable, but formalism is not suitable for the discovery of rules, and therefore the knowledge of technologist is required to determine the impact of process parameters on values that are functional properties of ADI. The characteristics of ALSV(FD) logic and XTT formalism, described in this article, cover the most important aspects of a broadly discussed, full evaluation of the applicability of these solutions in the construction of a system supporting the decisions of a technologist.