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Study of Bending Fatigue Properties of Al-Si Cast Alloy

Tillová E., Závodská D., Kuchariková L., Chalupová M., Belan J.

Archives of Metallurgy and Materials

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2017

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Vol. 62, iss. 3

1591--1596

EN

Fatigue properties of casting Al-alloys are very sensitive to the microstructural features of the alloy (e.g. size and morphology of the eutectic Si, secondary dendrite arm spacing – SDAS, intermetallics, grain size) and casting defects (porosity and oxides). Experimental study of bending fatigue properties of secondary cast alloys have shown that: fatigue tests up to 106-107cycles show mean fatigue limits of approx. 30-49 MPa (AlSi9Cu3 alloy – as cast state), approx. 65-76 MPa (AlSi9Cu3 alloy after solution treatment) and 60-70 MPa (self-hardened AlZn10Si8Mg alloy) in the tested casting condition; whenever large pore is present at or near the specimen’s surface, it will be the dominant cause of fatigue crack initiation; in the absence of large casting defects, the influence of microstructural features (Si morphology; Fe-rich phases) on the fatigue performance becomes more pronounced.

2

Methodology of automatic quality control of aluminium castings

Dobrzański L. A., Krupiński M., Sokolowski J. H.

Journal of Achievements in Materials and Manufacturing Engineering

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2007

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Vol. 20, nr 1-2

69-78

EN

Purpose: Employment of the artificial intelligence tools for development of the methodology of the automated assessment of quality and structural defects in the Al and Mg alloys and the custom made computer software will make it possible to determine the quality of the manufactured element based on the digital images registered in the X-ray flow detection examinations. The possibility to correlate the frequency and morphology of defects with the technological process parameters will make it also possible to identify and classify these defects and control the process to minimise and eliminate them. Design/methodology/approach: The developed design methodologies both the material and technological ones will make it possible to improve shortly the quality of materials from the light alloys in the technological process, and the automatic process flow correction will make the production cost reduction possible, and - first of all - to reduce the amount of the waste products. Findings: The merit of the project consists in the interdisciplinary joining of the knowledge in the area of light metal alloys, including Al and/or Mg, in the area of materials processing connected with the entire scope of problems connected with manufacturing of products and their elements, in the area of the automated low-pressure die casting, and also in the methodology of structure and properties assessment of the engineering materials with, among others, the X-ray flaw detection and computer image analysis methods. Practical implications: The developed methodology of the automated assessment of quality and properties of the light Al and Mg based alloys may used by manufacturers of subassemblies and elements of engines (e.g., car engine bodies made from the light alloys with the low-pressure casting in the sand moulds). Originality/value: The project's effects will be shortening the time needed for analyses and elimination of manu subjective evaluation errors made by humans.

3

Computer aided method for quality control of automotive Al-Si-Cu cast components

Dobrzański L. A., Krupiński M., Maniara R., Sokolowski J. H.

Journal of Achievements in Materials and Manufacturing Engineering

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2007

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Vol. 24, nr 2

151-154

EN

Purpose: The technological progress in material engineering causes the continuous need to develop product testing methods providing comprehensive quality evaluation. In material engineering it is the images obtained by various methods that have become the source of information about materials. Design/methodology/approach: The presented methodology, making it possible to determine the types and classes of defects developed during casting the elements from aluminium alloys, making use photos obtained with the flaw detection method with the X-ray radiation. The tests indicate to the applicability of neural networks for this task. It is very important to prepare the neural network data in the appropriate way, including their standardization, carrying out the proper image analysis and correct selection and calculation of the geometrical coefficients of flaws in the X-ray images. Findings: In classical computer algorithms even a slight rotation or change in lighting can binder the proper interpretation and alternation of variable input data. To eliminate this hindrance the programming can be converted by specifying such features of the structure element that remain most significant and effect the similarities of the analysed images. In neural networks this particular feature needs not to be specified - if necessary, the neural network spots it automatically. Practical implications: The computer aided methodology of the quality control of the light Al and Mg based alloys may be used by manufacturers of subassemblies and elements of car engines. Originality/value: The value of the applied methodology was to correct identify the casting effects that occurred during the casting process.

4

Methodology of analysis of casting defects

Dobrzański L. A., Krupiński M., Sokolowski J. H., Zarychta P., Włodarczyk-Fligier A.

Journal of Achievements in Materials and Manufacturing Engineering

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2006

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Vol. 18, nr 1-2

267--270

EN

Purpose: The goal of this publication is to present the methodology of the automatic supervision and control of the technological process of manufacturing the elements from aluminium alloys and of the methodology of the automatic quality assessment of these elements basing on analysis of images obtained with the X-ray defect detection, employing the artificial intelligence tools. The methodologies developed will make identification and classification of defects possible and the appropriate process control will make it possible to reduce them and to eliminate them - at least in part. Design/methodology/approach: The methodology is presented in the paper, making it possible to determine the types and classes of defects developed during casting the elements from aluminium alloys, making use photos obtained with the flaw detection method with the X-ray radiation. It is very important to prepare the neural network data in the appropriate way, including their standardization, carrying out the proper image analysis and correct selection and calculation of the geometrical coefficients of flaws in the X-ray images. The computer software was developed for this task. Findings: Combining of all methods making use of image analysis, geometrical shape coefficients, and neural networks will make it possible to achieve the better efficiency of class recognition of flaws developed in the material. Practical implications: The presented issues may be essential, among others, for manufacturers of car subassemblies from light alloys, where meeting the stringent quality requirements ensures the demanded service life of the manufactured products. Originality/value: The correctly specified number of products enables such technological process control that the number of castings defects can be reduced by means of the proper correction of the process.

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Komputerowe wspomaganie klasyfikacji wad identyfikowanych metodami radiograficznymi w odlewach ze stopów aluminium

Dobrzański L. A., Krupiński M., Maniara R., Kasprzak W., Sokołowski J.

Rudy i Metale Nieżelazne

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2005

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R. 50, nr 5

229-236

PL

W artykule przedstawiono komputerową metodykę klasyfikacji wad (tabl. 2), powstałych w stopach Al w trakcie wykonywania z nich elementów silników samochodowych produkowanych metodą niskociśnieniowego odlewania. Identyfikacji wad dokonano na podstawie danych uzyskanych z cyfrowych obrazów rejestrowanych metodami rentgenowskiej analizy defektoskopowej (rys. 1). Do rozwiązania tego zagadnienia wykorzystano opracowaną metodykę i związane z nią programy komputerowe do analizy obrazów rentgenowskich (rys. 2÷3), przygotowania danych wejściowych do sieci neuronowych oraz samą kontrolę jakości odlewów. Z zastosowanych w badaniach sieci neuronowych, w pracy przedstawiono wyniki klasyfikacji wad dla najlepszej sieci każdego typu. Parametry sieci o najlepszych wynikach klasyfikacji przedstawiono w tablicy 3. Zagadnienia klasyfikacyjne oceniano analizując, wyznaczoną dla danych testowych, liczbą poprawnych klasyfikacji (rys. 6÷7).

EN

In the paper a computer aided methodology of classification of defects (tabl. 2), which are formed in aluminium alloys during production of elements for car engines with the low-pressure casting method is presented. The defect identification was performed on the basis of digital recorded data registered by the use of X-ray image analysis method (fig. 1). In order to solve this problem an elaborated methodology and related computer programmes for X-ray images analysis (fig. 2÷ 3), as well as the preparation of entrance data for neuronal networks and also the quality cast control were used. The classification results of the best type of every network investigated are presented. The network parameters with the best classification results are showed in table 3. Analysing a number of correct classifications of pointed out test data (fig. 6÷7), the classifying problems are evaluated.

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Wpływ odlewania odśrodkowego na jakość kompozytów wzmacnianych cząstkami ceramicznymi i fazami międzymetalicznymi - aspekt porowatości

Dolata-Grosz A., Śleziona J.

Archiwum Odlewnictwa

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2004

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R. 4, nr 11/1

114--121

PL

Porowatość jest jedną z wysoce niepożądanych cech wszelkich odlewów, w tym także kompozytowych. Jednym z powodów porowatości, występującej w kompozytach odlewanych jest zagazowanie suspensji kompozytowej. Pęcherze gazowe, powstałe w czasie mechanicznego mieszania, znacząco obniżają jakość wyrobów. Ich obecność wpływa niekorzystnie między innymi na właściwości mechaniczne, a także obniża odporność korozyjną odlewów. Ponadto, zagazowanie zawiesiny, powstałe wskutek mechanicznego mieszania powoduje powstawanie aglomeratów cząstek ceramicznych. Dlatego też, dla wytworzenia odlewu kompozytowego o optymalnych właściwościach, konieczna jest jego minimalna porowatość. W pracy przebadano wpływ odlewania odśrodkowego zawiesin kompozytowych na porowatość odlewów.

EN

Porosity is one of the highly unwanted characteristics in any castings, included of casts composite, too. One of the reasons occurring in cast composites is suspension gassing. Gas porosity is probably one of the biggest problems in composites production with the liquid metal matrix, especially in slurry casting method. During the course of mixing the gases cavity can be introduced into the melt. Gaseous inclusions present in the over volume matrix, independently of the reason there are formation, decrease products quality significantly. Its presence, among other things, can be detrimental to the mechanical properties and corrosion resistance of the casts. Moreover, gassing of the suspended matter, being an effect of mechanical mixing, causes the formation of ceramic particles agglomerates. Porosity levels must therefore be kept to a minimum in order to produce sound composite castings with optimum properties. In this work the influence of centrifugal casting method on porosity in composite casts have been investigations.