Expert system for analysis of casting defects - ESVOD

The powerful tool for defect analysis is an expert system. It is a computer programme based on the knowledge of experts for solving the quality of castings. We present the expert system developed in the VSB-Technical University of Ostrava called 'ESWOD'. The ESWOD programme consists of three separate modules: identification, diagnosis / causes and prevention / remedy. The identification of casting defects in the actual form of the system is based on their visual aspect.Web of Science151201

Effects of surface roughness on the reliability of magnetic particle inspection for the detection of subsurface indications in steel castings

The objective of this research is to quantify the effects of surface roughness on the reliability of magnetic particle inspection (MPI) when detecting sub-surface indications. Indications in this study refer to possible defects. The reliability of MPI can be influenced by factors such as process control, part and indication characteristics, and human factors [1], [2]. Surface roughness is known to influence the effectiveness of wet MPI as rougher surfaces tend to result in particles collecting in the valleys of the surface textures which likely result in false positives [3], [4]. The surface roughness of the steel castings poses a challenge as it could increase the collection of particles when performing wet MPI. The lack of research into the influence of surface roughness on wet MPI has led to the need for this research. Three sets of experimental designs were developed. Firstly, particle collection due to surface roughness was tested using samples containing three levels of surface textures where a metric for the accumulation of fluorescent particles was developed by obtaining a value to represent the average green intensity. Next, the noise area percentage caused by four levels of surface roughness with a common sub-surface indication was tested. Noise area percentage in this study was determined by the percentage of pixels surrounding the indication which have higher green intensity compared to the average green intensity of the indication. This experiment was conducted to evaluate the relationship between noise area percentage and surface roughness when testing for a fixed discontinuity. Noise area percentage is a metric to determine the level of difficulty in identifying an indication. The higher noise area percentage, the harder it is to identify an indication. Lastly, the effect of surface roughness compared to depth and diameter with regards to the influence it has on the response variable (noise area percentage) was evaluated. This research will provide a quantifiable method for the effects caused by factors that were not available prior to this study. Additionally, a better understanding of the impacts surface roughness have on the effectiveness of wet MPI was achieved through this investigation

Notes and laboratory reports on “Technology of Structural materials and Material Science” Part 2

“Technology of Structural materials and Material Science” is one of the basic technical disciplines in the syllabus for “Engineering mechanics” field of study. During the implementation of laboratory work considerable attention is given to the educational and experimental work for the study of materials that are used in different branches of an industry; alloy’s properties dependance on the chemical composition; structure, methods of treatment and external environments. The study of the theory and practice of different methods of materials strengthening is to provide a high reliability and longevity of the machine’s details, devices, tools etc. After every practical class in the laboratory, students will fill the laboratory report. The content of the laboratory class corresponds with the syllabus of the course “Material Science” for students of the “Engineering mechanics” field of study. The purpose of this manual is to provide guidelines for the students in preparation for independent laboratory work and to project its results in the laboratory reports

Development and application of titanium alloy casting technology in China

The development and research of casting titanium alloy and its casting technology, especially its application in aeronautical industry in China are presented. The technology of moulding, melting and casting of titanium alloy, casting quality control are introduced. The existing problem and development trend in titanium alloy casting technology are also discussed

Métodos machine learning para la predicción de inclusiones no metálicas en alambres de acero para refuerzo de neumáticos

ABSTRACT: Non-metallic inclusions are unavoidably produced during steel casting resulting in lower mechanical strength and other detrimental effects. This study was aimed at developing a reliable Machine Learning algorithm to classify castings of steel for tire reinforcement depending on the number and properties of inclusions, experimentally determined. 855 observations were available for training, validation and testing the algorithms, obtained from the quality control of the steel. 140 parameters are monitored during fabrication, which are the features of the analysis; the output is 1 or 0 depending on whether the casting is rejected or not. The following algorithms have been employed: Logistic Regression, K-Nearest Neighbors, Support Vector Classifier (linear and RBF kernels), Random Forests, AdaBoost, Gradient Boosting and Artificial Neural Networks. The reduced value of the rejection rate implies that classification must be carried out on an imbalanced dataset. Resampling methods and specific scores for imbalanced datasets (Recall, Precision and AUC rather than Accuracy) were used. Random Forest was the most successful method providing an AUC in the test set of 0.85. No significant improvements were detected after resampling. The improvement derived from implementing this algorithm in the sampling procedure for quality control during steelmaking has been quantified. In this sense, it has been proved that this tool allows the samples with a higher probability of being rejected to be selected, thus improving the effectiveness of the quality control. In addition, the optimized Random Forest has enabled to identify the most important features, which have been satisfactorily interpreted on a metallurgical basis.RESUMEN: Las inclusiones no metálicas se producen inevitablemente durante la fabricación del acero, lo que resulta en una menor resistencia mecánica y otros efectos perjudiciales. El objetivo de este estudio fue desarrollar un algoritmo fiable para clasificar las coladas de acero de refuerzo de neumáticos en función del número y el tipo de las inclusiones, determinadas experimentalmente. Se dispuso de 855 observaciones para el entrenamiento, validación y test de los algoritmos, obtenidos a partir del control de calidad del acero. Durante la fabricación se controlan 140 parámetros, que son las características del análisis; el resultado es 1 ó 0 dependiendo de si la colada es rechazada o no. Se han empleado los siguientes algoritmos: Regresión Logística, Vecinos K-Cercanos, Clasificador de Vectores Soporte (kernels lineales y RBF), Bosques Aleatorios, AdaBoost, Gradient Boosting y Redes Neurales Artificiales. El bajo índice de rechazo implica que la clasificación debe llevarse a cabo en un set de datos desequilibrado. Se utilizaron métodos de remuestreo y métricas específicas para conjuntos de datos desequilibrados (Recall, Precision y AUC en lugar de Accuracy). Random Forest fue el algoritmo más exitoso que proporcionó un AUC en los datos de test de 0.83. No se detectaron mejoras significativas después del remuestreo. Se ha cuantificado la mejora derivada de la implementación de este algoritmo en el procedimiento de muestreo para el control de calidad durante la fabricación de acero. En este sentido, se ha comprobado que esta herramienta permite seleccionar las muestras con mayor probabilidad de ser rechazadas, mejorando así la eficacia del control de calidad. Además, el Random Forest optimizado ha permitido identificar las variables más importantes, que han sido interpretadas satisfactoriamente sobre una base metalúrgica.Máster en Ciencia de Dato

Quantitative surface inspection methods for metal castings

Inspecting castings to verify the quality of a part is critical for foundries to maintain a high level of customer confidence. Current methods employ qualitative methods, and the manufacturer must correctly interpret the inspection criteria set by the customer in order to meet the design specifications. The interpretation of what is acceptable often differs from the customer to manufacturer and even from inspector to inspector. In this thesis, the visual inspection of cast metal are explored in depth, and improvements to current methods are proposed. First, the risk of Type I and II errors from the inspection process were evaluated based off of varying states of environmental and human factors in the inspection process; however, it was discovered high variation among inspectors still exists due to the subjectivity of the standards. This signals a need for a more quantitative standard to evaluate the surface of a casting. In response a digital standard is proposed, which specifies three parameters to allow the customer to communicate their exact needs in regards to surface finish to the manufacturer. These parameters are calculated based off of a part’s true geometry post shrinkage in absence of surface roughness and abnormalities, or underlying geometry. Since the underlying geometry differs from the part’s intended geometry, methods will be explored to estimate the underlying geometry from a point cloud of the part’s surface. The proposed methods will be compared to identify which approach best estimates the ideal underlying geometry. Once an ideal method is identified, it will be used as a standard method to calculate the underlying geometry in order to create consistency among inspectors at both the customer and manufacturer. The work completed in this thesis will raise awareness of the risk associated with current visual inspection methods for cast metal surfaces. The new, digital standard will reduce the variation in this inspection process allowing greater confidence in the parts leaving the manufacturer. Additionally, the standard will allow the customer to improve communication with the manufacturer in order to achieve the quality of surface required for their specific needs

The effect of near-surface metallurgy on the machinability of cast iron

The increasing performance and durability of cutting tool inserts have created metallurgical challenges for production foundries to produce near-net shaped castings within strict dimensional tolerances. In order for foundries to take full advantage of the increased cutting speed capabilities, it becomes necessary to reduce machining allowances and produce much more stable casting surfaces. To accomplish this, a better understanding of the complex microstructures formed within the first 0.120 in. (3 mm) of the mold/metal interface (as-cast surface) is necessary. The goal of the work presented here was to examine the microstructures formed in the near-surface region of gray iron castings, determine what was responsible for formation, and how these microstructures behaved during the machining process. A series of experiments were performed to evaluate the effect of graphite flake morphology, matrix microstructure, and alloying elements on near-surface machinability. Three-dimensional cutting forces, quantitative metallography, and high-speed photographic measurements were used to evaluate the behavior of flake graphite, ferrite, coarse/dense pearlite, steadite, and carbides during the machining process. Data from the experiments also indentified the importance of inoculation practice, cooling rate, and mold sand properties on the final near-surface microstructure/machinability behavior. A case study was then performed for industrial brake rotor castings produced from class 35 gray cast iron, in which diagnosis of a machinability problem proved to be near-surface microstructure related. It was found that a combination of mold sand properties and inoculation practice were responsible for surface free-ferrite/graphite morphology microstructural defects --Abstract, page iii

Tool manufacturing by metal casting in sand moulds produced by additive manufacturing processes

Thesis (D. Tech. ( Mechanical Engineering )) - Central University of technology, Free State, 2012In this study an alternative indirect Rapid Tooling process is proposed. It essentially consists of producing sand moulds by Additive Manufacturing (AM) processes followed by casting of tools in the moulds. Various features of this tool making method have been investigated. A process chain for the proposed tool manufacturing method was conceptually developed. This process chain referred to as Rapid Casting for Tooling (RCT) is made up of five steps including Computer Aided Design (CAD) modeling, casting simulation, AM of moulds, metal casting and finishing operations. A validation stage is also provided to determine the suitability of the tool geometry and material for RCT. The theoretical assessment of the RCT process chain indicated that it has potential benefits such as short manufacturing time, low manufacturing cost and good quality of tools in terms of surface finish and dimensional accuracy. Focusing on the step of AM of the sand moulds, the selection of available AM processes between the Laser Sintering (LS) using an EOSINT S 700 machine and Three Dimensional Printing using a Z-Corporation Spectrum 550 printer was addressed by means of the Analytic Hierarchy Process (AHP). The criteria considered at this stage were manufacturing time, manufacturing cost, surface finish and dimensional accuracy. LS was found to be the most suitable for RCT compared to Three Dimensional Printing. The overall preferences for these two alternatives were respectively calculated at 73% and 27%. LS was then used as the default AM process of sand moulds in the present research work. A practical implementation of RCT to the manufacturing of foundry tooling used a case study provided by a local foundry. It consisted of the production of a sand casting pattern in cast iron for a high pressure moulding machine. The investigation confirmed the feasibility of RCT for producing foundry tools. In addition it demonstrated the crucial role of casting simulation in the prevention of casting defects and the prediction of tool properties. The challenges of RCT were found to be exogenous mainly related to workmanship. An assessment of RCT manufacturing time and cost was conducted using the case study above mentioned as well as an additional one dealing with the manufacturing of an aluminium die for the production of lost wax patterns. Durations and prices of RCT steps were carefully recorded and aggregated. The results indicated that the AM of moulds was the rate determining and cost driving step of RCT if procurement of technology was considered to be a sunk cost. Overall RCT was found to be faster but more expensive than machining and investment casting. Modern surface analyses and scanning techniques were used to assess the quality of RCT tools in terms of surface finish and dimensional accuracy. The best surface finish obtained for the cast dies had Ra and Rz respectively equal to 3.23 μm and 11.38 μm. In terms of dimensional accuracy, 82% of cast die points coincided with die Computer Aided Design (CAD) data which is within the typical tolerances of sand cast products. The investigation also showed that mould coating contributed slightly to the improvement of the cast tool surface finish. Finally this study also found that the additive manufacturing of the sand mould was the chief factor responsible for the loss of dimensional accuracy. Because of the above, it was concluded that light machining will always be required to improve the surface finish and the dimensional accuracy of cast tools. Durability was the last characteristic of RCT tools to be assessed. This property was empirically inferred from the mechanical properties and metallographic analysis of castings. Merit of durability figures of 0.048 to 0.152 were obtained for the cast tools. It was found that tools obtained from Direct Croning (DC) moulds have merit of durability figures three times higher than the tools produced from Z-Cast moulds thus a better resistance to abrasion wear of the former tools compared to the latter

Aerodynamic Simulation of Ice Accretion on Airfoils

This report describes recent improvements in aerodynamic scaling and simulation of ice accretion on airfoils. Ice accretions were classified into four types on the basis of aerodynamic effects: roughness, horn, streamwise, and spanwise ridge. The NASA Icing Research Tunnel (IRT) was used to generate ice accretions within these four types using both subscale and full-scale models. Large-scale, pressurized windtunnel testing was performed using a 72-in.- (1.83-m-) chord, NACA 23012 airfoil model with high-fidelity, three-dimensional castings of the IRT ice accretions. Performance data were recorded over Reynolds numbers from 4.5 x 10(exp 6) to 15.9 x 10(exp 6) and Mach numbers from 0.10 to 0.28. Lower fidelity ice-accretion simulation methods were developed and tested on an 18-in.- (0.46-m-) chord NACA 23012 airfoil model in a small-scale wind tunnel at a lower Reynolds number. The aerodynamic accuracy of the lower fidelity, subscale ice simulations was validated against the full-scale results for a factor of 4 reduction in model scale and a factor of 8 reduction in Reynolds number. This research has defined the level of geometric fidelity required for artificial ice shapes to yield aerodynamic performance results to within a known level of uncertainty and has culminated in a proposed methodology for subscale iced-airfoil aerodynamic simulation