Diploma thesis deal with definnig the casuses of stell castings defects in the DSB EURO s.r.o Foundry. The main target is to optimise technological parameters for avoiding the surface casting defects. The first stage is the identification of the defects, describing of the main reasons and mechanic. Defect quantification is make from measuring of area and volume of the mended defects. In association with surface defects, the number of the crackings was observed. Finally there are suggested correction actions based on the previous investigation.

Within industrial product development processes there is an increasing demand towards reliable predictions of the material behavior, which aims to promote a property driven development that can reduce the lead times. The implementation of simulation based product development with integrated casting simulation may enable the design engineers to gain an early understanding of the products with relation to castability, and orient the subsequent design refinement so as to achieve the desired mechanical properties. This work investigates the suitability of three commercial casting simulation softwares –MAGMA 5.2, NovaFlow & Solid 4.7.5 (NFS) and Click2Cast 3.0 (C2C)–, with respect to the needs of design engineers, such as prediction of shrinkage porosity and mechanical properties with relation to the design. Simplified solidification simulations suitable for this stage were thus performed for three high pressure die cast components with different geometrical constraints. The comparability between the solidification and cooling behaviour predicted by the three softwares was studied, and showed that a reasonably good agreement between predicted solidification times by MAGMA and NFS could be obtained, albeit not between predictions by MAGMA and C2C. Predictions by the three softwares of the hot spot/porosity areas showed to have a good agreement. The calculation times by each software were compared, and MAGMA was seen to have the best performance, yielding significantly shorter times than NFS and C2C. The results obtained were also compared to experimental investigations of porosity, microstructural coarseness, and mechanical properties. There was a good agreement between the predicted hot spot areas –i.e. areas in the geometry that solidify last– and the findings of porosities in the actual castings, meaning that solidification simulations might be able to provide important information for the prediction of most of shrinkage related porosity locations that are related to the casting geometry. However, the lack of a detailed knowledge at the design stage of the casting process limits the possibilities to predict all porosities. The predicted microstructure and mechanical properties by MAGMA non-ferrous were seen to have a good agreement in trend with the experimental data, albeit the predicted values showed large differences in magnitude with the experimental data. Although, the MAGMA non-ferrous module was not developed for HPDC components, it was interesting to study if it could be applied in this context. However, the models seem to need adoption to the HPDC process and alloys. In conclusion, with a limited knowledge of the manufacturing parameters, simplified solidification simulations may still be able to provide reasonably reliable and useful information during early development stages in order to optimise the design of castings.

The central goal of this research is to quantitatively characterize the relationships between processing, microstructure, and mechanical properties of important high-pressure die-cast (HPDC) Mg-alloys. For this purpose, a new digital image processing technique for automatic detection and segmentation of gas and shrinkage pores in the cast microstructure is developed and it is applied to quantitatively characterize the effects of HPDC process parameters on the size distribution and spatial arrangement of porosity. To get better insights into detailed geometry and distribution of porosity and other microstructural features, an efficient and unbiased montage based serial sectioning technique is applied for reconstruction of three-dimensional microstructures. The quantitative microstructural data have been correlated to the HPDC process parameters and the mechanical properties. The analysis has led to hypothesis of formation of new type of shrinkage porosity called, gas induced shrinkage porosity that has been substantiated via simple heat transfer simulations. The presence of inverse surface macrosegregation has been also shown for the first time in the HPDC Mg-alloys. An image analysis based technique has been proposed for simulations of realistic virtual microstructures that have realistic complex pore morphologies. These virtual microstructures can be implemented in the object oriented finite elements framework to model the variability in the fracture sensitive mechanical properties of the HPDC alloys.

No description available.

Oxide inclusions are among the most commonly reported defects in ferrous and non-ferrous castings. They affect the surface quality, machinability, and mechanical performance of a cast part. Air entrainment during mold filling is the main source of the oxygen that is consumed in inclusion formation. A quantitative understanding of the formation mechanisms or the prediction of final amounts and locations of oxide inclusions in metal castings is not available. Ductile iron experiments are conducted to study the formation of oxide inclusions during pouring. Oxide inclusions are measured by serial sectioning of the solidified castings. The effect of different gating systems, section thicknesses, and surface orientations on the inclusion formation and final distribution is studied. In addition, a computational model is developed for predicting the formation, motion and final location of oxide inclusions during pouring of metal castings, with the focus on the important mechanism of generation of oxide inclusions due to air entrainment during mold filling. The developed model calculates the local air entrainment rate as a function of the turbulent kinetic energy and the magnitude of the normal velocity gradient of the liquid metal at the liquid-air interface. The turbulent kinetic energy is estimated from the sum of the squares of the fluctuating velocity components relative to a spatially averaged mean velocity. The air entrainment model is implemented in a casting simulation software and validated by comparing its predictions to experimental air entrainment measurements for a circular water jet plunging into a quiescent pool. The liquid velocity, diameter and the turbulence intensity dependence is determined by a single entrainment coefficient. Oxide inclusions are then generated at the liquid-air interface, transported with the melt flow under the combined influences of drag and buoyancy, and captured by the solidifying casting surface. The developed model provides a powerful technique for predicting the oxide inclusion formation and final location.

The thesis deals with the fatigue resistance of material MAR-M 247 at temperatures of 650 °C, 800 °C and 900 °C. Based on the results obtained, curves of durability ("S/N curves") were drawn and the effect of temperature on the fatigue behaviour of nickel-based superalloys MAR-M 247 and IN713 LC was evaluated. Fractographic analysis was used to examine the failure mechanism and the effect of casting defects on the fatigue crack initiation.

Prediction of both big and small decisions is something most companies have to make on a daily basis. The importance of having a highly accurate technique for different decision-making is not something that is new. However, even though the importance of prediction is a fact to most people, current techniques for estimation are still often highly inaccurate. The consequences of an inaccurate prediction can be huge in the differences between the misclassifications. Not just in the industry but for many different areas. Machine learning have in the recent couple of years improved significantly and are now considered a reliable method to use for prediction. The main goal of this research is to predict casting defects with the help of a machine-learning algorithm based on process parameters. In order to achieve the main goal, some sub-objectives have been identified to successfully reach those goals. A problem when dealing with machine learning is an unbalanced dataset. When training a network, it is essential that the dataset is balanced. In this research we have successfully balanced the dataset. Undersampling was the method used in our research to establish our balanced dataset. The research compares and evaluates a couple of different undersample methods in order to see which undersampling is best suited for this project. Three different machine models, “random forest”, “artificial neural network”, and “k-nearest neighbor”, are also compared to each other to see what model performs best. The conlcusion reached was that the best method for both undersampling and machine learning model varied due to many different reasons. So, in order to find the best model with the best method for a specific job, all the models and methods need to be tested. However, the undersampling method that provided best performances most times in our research was the NearMiss version 2 model. Artificial Neural Network was the machine learning model that had most success in our research. It performed best in two out of three evaluations and comparisons.

In its first part the work deals with the frequent defects of heavy steel castings. Experiments were carried out in very heavy bearing rings casting of cement kilns in order to reduce consumption of liquid metal and to increase the internal quality of castings. The progress was achieved by introducing new technologies; this thesis describes the technologies used and subsequently performs their comparison.

Le laminage est un procédé de mise en forme à chaud permettant d’obtenir des barres de différents diamètres en partant de blooms issus de la coulée continue. Dans les bruts de coulée se répartissent des inclusions qui sont de natures, de formes et de tailles différentes. Le laminage va permettre de réduire le diamètre de la barre et d’agir sur la microstructure du matériau, notamment, en fragmentant et dispersant les inclusions. L’objectif des travaux de thèse est de contribuer à la compréhension des effets du chargement thermomécanique sur l’évolution des défauts de coulée en laminage. Vu la dimension des installations, les investigations expérimentales directes sur les moyens industriels ne sont pas envisageables. L’idée développée dans les travaux de thèse concerne la mise en place et la validation d’un essai de caractérisation à échelle réduite reproduisant le chemin thermomécanique subi par la matière au cours du laminage. Pour ce faire la ligne de laminage industrielle est modélisé afin d’obtenir le chargement thermomécanique de la barre au cours du laminage. Seules les sollicitations ayant un rôle majeur sur le comportement des défauts sont reproduites. Un essai dit de « forgeage libre » a ainsi été conçu, réalisé, mis en place sur les moyens de mise en forme de la plateforme VULCAIN de l’ENSAM. L’essai a été utilisé avec des défauts artificiels présentant des similarités comme la malléabilité avec les défauts réels. Une campagne expérimentale paramétrique a été menée sur les différents types de défauts. Les échantillons forgés ont été analysés par des méthodes non destructives comme les ultrasons, la radiographie et la tomographie X. Ces analyses ont permis de suivre le comportement du défaut et d’observer certains phénomènes mis en évidence dans la littérature comme l’apparition de cavité à l’interface défaut-matrice. / Rolling is a hot forming process dedicated to manufacture bars with different diameters. The initial product is a bloom from the continuous casting. The bloom contains inclusions which have different forms, sizes and distributions. The rolling reduces the diameter of the bar and acts on the material microstructure by fragmenting and dispersing the inclusions. The aim of the thesis work is to understand the behavior of the inclusions with the rolling thermo mechanical loading effects. The direct investigations on the rolling mill are not possible because of the size of the installations. To address this problem, the solution is to develop and validate a small scale characterization test reproducing the thermo mechanical loading of the rolled bar. To achieve this, the rolling mill is modeled. Only the solicitations which have a major role on the behavior of the defects are reproduced. An open-die forging test is designed, manufactured and implemented on the VULCAIN installation of the ENSAM. The artificial defects which have a similar malleability to the real defects are integrated into the sample. A parametric experimental campaign has been conducted on the different defects. The forged samples have been analyzed with non-destructive methods such as ultrasound, radiography and tomography. These analyses allowed to follow the behavior of the defects and to observe certain phenomena illustrated in the literature such as the emergence of a cavity on the defect-matrix interface.

Als Ergebnis der vorliegenden Arbeit wurde ein nützliches Hilfsmittel auf der Basis der kognitiven Herangehensweise zur Verbesserung der Effizienz der Managemententscheidungen für die Gussausschussverringerung und Qualitätsverbesserung in den Gießereien entwickelt. Das Werkzeug hilft dem Technologen, den Mechanismus des Gussfehlerentstehungsprozesses aufzudecken, die Logik der Gussfehlerentstehung zu verstehen und die präventiven Maßnahmen zu testen. Man kann das Werkzeug täglich im Betrieb benutzen, um die strategischen und operativen Entscheidungen rasch und ohne Durchführung der kostspieligen und komplizierten Versuche zu treffen. Auf diese Weise können Kosten und Zeit eingespart werden.