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    Wyświetlanie 1-3 z 3
    Tytuł:
    The deformation of wax patterns and castings in investment casting technology
    Autorzy:
    Herman, A.
    Česal, M.
    Mikeš, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/382400.pdf
    Data publikacji:
    2012
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    technologia informatyczna
    przemysł odlewniczy
    innowacyjne technologie odlewnicze
    innowacyjne materiały odlewnicze
    odlewanie precyzyjne
    model odlewniczy
    information technology
    foundry industry
    innovative foundry technologies
    innovative foundry material
    investment casting
    wax patterns
    Opis:
    The dimensional accuracy of the final casting of Inconel alloy 738 LC is affected by many aspects. One of them is the choice of method and time of cooling wax model for precision investment casting. The main objective was to study the initial deformation of the complex shape of the casting of the rotor blades. Various approaches have been tested for cooling wax pattern. When wax models are cooling on the air, without clamping in jig for cooling, deviations from the ideal shape of the casting are very noticeable (up to 8 mm) and most are in extreme positions of the model. When blade is cooled in fixing jig in water environment, the resulting deviations compared with cooling in air are significantly larger, sometimes up to 10 mm. This itself does not mean that the final shape of the casting is dimensionally more accurate with usage of wax models, which have deviations from the ideal position smaller. Another deformation occurs when shell mould is produced around wax pattern and furthermore deformations emerge while casting of blade is cooling. This paper demonstrates first steps in describing complex process of deformations of Inconel alloy blades produced with investment casting technology by comparing results from thermal imagery, simulations in foundry simulation software ProCAST 2010 and measurements from CNC scanning system Carl Zeiss MC 850. Conclusions are so far not groundbreaking, but it seems deformations of wax pattern and deformations of castings do in some cases cancel each other by having opposite directions. Describing entirely whole process of deformations will help increase precision of blade castings so that models at the beginning and blades in the end are the same.
    Źródło:
    Archives of Foundry Engineering; 2012, 12, 1; 37-42
    1897-3310
    2299-2944
    Pojawia się w:
    Archives of Foundry Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Methodology of Fault Diagnosis in Ductile Iron Melting Process
    Autorzy:
    Perzyk, M.
    Kozlowski, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/382169.pdf
    Data publikacji:
    2016
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    quality management
    information technology
    foundry industry
    process fault diagnosis
    ductile iron
    data driven model
    zarządzanie jakością
    technologia informatyczna
    przemysł odlewniczy
    diagnostyka uszkodzeń
    żeliwo ADI
    model danych
    Opis:
    Statistical Process Control (SPC) based on the Shewhart’s type control charts, is widely used in contemporary manufacturing industry, including many foundries. The main steps include process monitoring, detection the out-of-control signals, identification and removal of their causes. Finding the root causes of the process faults is often a difficult task and can be supported by various tools, including data-driven mathematical models. In the present paper a novel approach to statistical control of ductile iron melting process is proposed. It is aimed at development of methodologies suitable for effective finding the causes of the out-of-control signals in the process outputs, defined as ultimate tensile strength (Rm) and elongation (A5), based mainly on chemical composition of the alloy. The methodologies are tested and presented using several real foundry data sets. First, correlations between standard abnormal output patterns (i.e. out-of-control signals) and corresponding inputs patterns are found, basing on the detection of similar patterns and similar shapes of the run charts of the chemical elements contents. It was found that in a significant number of cases there was no clear indication of the correlation, which can be attributed either to the complex, simultaneous action of several chemical elements or to the causes related to other process variables, including melting, inoculation, spheroidization and pouring parameters as well as the human errors. A conception of the methodology based on simulation of the process using advanced input - output regression modelling is presented. The preliminary tests have showed that it can be a useful tool in the process control and is worth further development. The results obtained in the present study may not only be applied to the ductile iron process but they can be also utilized in statistical quality control of a wide range of different discrete processes.
    Źródło:
    Archives of Foundry Engineering; 2016, 16, 4; 101-108
    1897-3310
    2299-2944
    Pojawia się w:
    Archives of Foundry Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Diagnosis of Missed Ductile Iron Melts with Process Modelling
    Autorzy:
    Perzyk, M.
    Werlaty, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/382916.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    quality management
    information technology
    foundry industry
    process fault diagnosis
    ductile iron melting
    data driven model
    zarządzanie jakością
    technologia informacyjna
    przemysł odlewniczy
    diagnostyka uszkodzeń
    topienie żeliwa
    Opis:
    The paper presents an application of advanced data-driven (soft) models in finding the most probable particular causes of missed ductile iron melts. The proposed methodology was tested using real foundry data set containing 1020 records with contents of 9 chemical elements in the iron as the process input variables and the ductile iron grade as the output. This dependent variable was of discrete (nominal) type with four possible values: ‘400/18’, ‘500/07’, ‘500/07 special’ and ‘non-classified’, i.e. the missed melt. Several types of classification models were built and tested: MLP-type Artificial Neural Network, Support Vector Machine and two versions of Classification Trees. The best accuracy of predictions was achieved by one of the Classification Tree model, which was then used in the simulations leading to conversion of the missed melts to the expected grades. Two strategies of changing the input values (chemical composition) were tried: content of a single element at a time and simultaneous changes of a selected pair of elements. It was found that in the vast majority of the missed melts the changes of single elements concentrations have led to the change from the non-classified iron to its expected grade. In the case of the three remaining melts the simultaneous changes of pairs of the elements’ concentrations appeared to be successful and that those cases were in agreement with foundry staff expertise. It is concluded that utilizing an advanced data-driven process model can significantly facilitate diagnosis of defective products and out-of-control foundry processes.
    Źródło:
    Archives of Foundry Engineering; 2017, 17, 4; 123-126
    1897-3310
    2299-2944
    Pojawia się w:
    Archives of Foundry Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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