Quality control and improvement of the aluminum alloy castings for the next generation of engine block cast components.

This research focuses on the quality control and improvement of the W319 aluminum alloy engine blocks produced at the NEMAK Windsor Aluminum Plant (WAP). The present WAP Quality Control (QC) system was critically evaluated using the cause and effect diagram and therefore, a novel Plant Wide Quality Control (PWQC) system is proposed. This new QC system presents novel tools for off line as well as on line quality control. The off line tool uses heating curve analysis for the grading of the ingot suppliers. The on line tool utilizes Tukey control charts of the Thermal Analysis (TA) parameters for statistical process control. An Artificial Neural Network (ANN) model has also been developed for the on-line prediction and control of the Silicon Modification Level (SiML). The student t-statistical analysis has shown that even small scale variations in the Fe and Mn levels significantly affect the shrink porosity level of the 3.0L V6 engine block bulkhead. When the Fe and Mn levels are closer to their upper specification limits (0.4 wt.% and 0.3wt.%, respectively), the probability of low bulkhead shrink porosity is as high as 0.73. Elevated levels of Sn (∼0.04 wt.%) and Pb (∼0.03 wt.%) were found to lower the Brinell Hardness (HB) of the V6 bulkhead after the Thermal Sand Removal (TSR) and Artificial Aging (AA) processes. Therefore, Sn and Pb levels must be kept below 0.0050 wt.% and 0.02 wt.%, respectively, to satisfy the bulkhead HB requirements. The Cosworth electromagnetic pump reliability studies have indicated that the life of the pump has increased from 19,505 castings to 43,904 castings (225% increase) after the implementation of preventive maintenance. The optimum preventive maintenance period of the pump was calculated to be 43,000 castings. The solution treatment parameters (temperature and time) of the Novel Solution Treatment during the Solidification (NSTS) Process were optimized using ANN and the Simulated Annealing (SA) algorithm. The optimal NSTS process (516°C and 66 minutes) would significantly reduce the present Thermal Sand Removal (TSR) time (4 hours) and would avoid the problem of incipient melting without sacrificing the mechanical properties. In order to improve the cast component characteristics and to lower the alloy price, a new alloy, Al 332, (Si=10.5 wt.% & Cu=2 wt.%) was developed by optimizing the Si and Cu levels of 3XX Al alloys as a replacement for the W319 alloy. The predicted as cast characteristics of the new alloy were found to satisfy the requirements of Ford engineering specification WSE-M2A-151-A2/A4.* *This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation).Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .F735. Source: Dissertation Abstracts International, Volume: 66-11, Section: B, page: 6201. Thesis (Ph.D.)--University of Windsor (Canada), 2005

Challenges for Data Mining on Sensor Data of Interlinked Processes

In industries like steel production, interlinked production processes leave no time for assessing the physical quality of intermediate products. Failures during the process can lead to high internal costs when already defective products are passed through the entire value chain. However, process data like machine parameters and sensor data which are di- rectly linked to quality can be recorded. Based on a rolling mill case study, the paper discusses how decentralized data mining and intelligent machine-to-machine communication could be used to predict the physical quality of intermediate products online and in real-time for detecting quality issues as early as possible. The recording of huge data masses and the distributed but sequential nature of the problem lead to challenging research questions for the next generation of data mining

Book of abstracts of the 14th International Symposium of Croatian Metallurgical Society - SHMD \u272020, Materials and metallurgy

Book of abstracts of the 14th International Symposium of Croatian Metallurgical Society - SHMD \u272020, Materials and metallurgy held in Šibenik, Croatia, June 21-26, 2020. Abstracts are organized in four sections: Materials - section A; Process metallurgy - Section B; Plastic processing - Section C and Metallurgy and related topics - Section D

Near net shape manufacturing of metal : a review of approaches and their evolutions

In the last thirty years the concept of manufacturability has been applied to many different processes in numerous industries. This has resulted in the emergence of several different "Design for Manufacturing" methodologies which have in common the aim of reducing productions costs through the application of general manufacturing rules. Near net shape technologies have expanded these concepts, targeting mainly primary shaping process, such as casting or forging. The desired outcomes of manufacturability analysis for near-net-shape (NNS) processes are cost and lead/time reduction through minimization of process steps (in particular cutting and finishing operations) and raw material saving. Product quality improvement, variability reduction and component design functionality enhancement are also achievable through NNS optimization. Process parameters, product design and material selection are the changing variables in a manufacturing chain that interact in complex, non-linear ways. Consequently modeling and simulation play important roles in the investigation of alternative approaches. However defining the manufacturing capability of different processes is also a “moving target” because the various NNS technologies are constantly improving and evolving so there is challenge in accurately reflecting their requirements and capabilities. In the last decade, for example, CAD, CNC technologies and innovation in materials have impacted enormously on the development of NNS technologies. This paper reviews the different methods reported for NNS manufacturability assessment and examines how they can make an impact on cost, quality and process variability in the context of a specific production volume. The discussion identifies a lack of structured approaches, poor connection with process optimization methodologies and a lack of empirical models as gaps in the reported approaches

Optimization of “Deoxidation Alloying” Batching Scheme

In this paper, a mathematical model was established to predict the deoxidation alloying and to optimize the type and quantity of input alloys. Firstly, the GCA method was used to obtain the main factors affecting the alloy yield of carbon and manganese based on the historical data. Secondly, the alloy yield was predicted by the stepwise MRA, the BP neural network and the regression SVM models, respectively. The conclusion is that the regression SVM model has the highest prediction accuracy and the maximum deviation between the test set prediction result and the real value was only 0.0682 and 0.0554. Thirdly, in order to reduce the manufacturer's production cost, the genetic algorithm was used to calculate the production cost mathematical programming model. Finally, sensitivity analysis was performed on the prediction model and the cost optimization model. The unit price of 20% of the alloy raw materials was increased by 20%, and the total cost change rate was 0.7155%, the lowest was -0.4297%, which proved that the mathematical model established presented strong robustness and could be certain reference value for the current production of iron and steel enterprises

Intelligent approach based on FEM simulations and soft computing techniques for filling system design optimisation in sand casting processes

This paper reports an intelligent approach for modeling and optimisation of filling system design (FSD) in the case of sand casting process of aluminium alloy. In order to achieve this purpose, physics-based process modeling using finite element method (FEM) has been integrated with artificial neural networks (ANN) and genetic algorithm (GA) soft computing techniques. A three dimensional FE model of the studied process has been developed and validated, using experimental literature data, to predict two melt flow behaviour (MFB) indexes named ingate velocity and jet high. Two feed-forward back-propagation ANN-based process models were developed and optimised to establish the relationship between the FSD input parameters and each studied MFB index. Both ANN models were trained, tested and tuned by using database generated from FE computations. It was found that both ANN models could independently predict, with a high accuracy, the values of the ingate velocity and the jet high for training and test data. The developed ANN models were coupled with an evolutionary GA to select the optimal FSD for each one. The validity of the found solutions was tested by comparing ANN-GA prediction with FE computation for both studied MFB indexes. It was found that error between predicted and simulated values does not exceed 5.61% and 6.31% respectively for the ingate velocity and the jet high, which proves that the proposed approach is reliable and robust for FSD optimisation

Effect of homogenization and alloying elements on hot deformation behaviour of 1XXX series aluminum alloys = Effet des éléments d'alliage et d'homogénéisation sur le comportement à la déformation à chaud des alliages d'aluminium de la série 1XXX

The 1xxx series of aluminum alloys are widely used for applications in which excellent formability and thermal and electrical conductivity are required such as heatexchanger tubing and coaxial cable sheathing. The demand for high productivity during processing leads to the requirement for an increase in hot workability to provide low flow stress with desirable final mechanical properties. Commercially, D.C cast billets are typically homogenized prior to extrusion or rolling to improve hot workability and mechanical properties. However, there is very limited prior work on the effectiveness of the homogenization treatment in 1xxx alloy production. Furthermore, no systematic investigation of the influence of different alloying elements (Fe, Si, Mn and Cu) on the hot deformation behavior of dilute Al-Fe-Si alloys is available in the literature. In the present study, the effect of different alloying elements as well as the homogenization treatment on the hot workability and microstructure of dilute Al-Fe-Si alloys was investigated using hot compression tests, optical microscopy, SEM, electron EBSD, TEM, electrical conductivity measurements. The effect of the homogenization treatment on the microstructure and hot workability of two dilute Al-Fe-Si alloys was first investigated. Homogenization promoted the phase transformation from the metastable AlmFe or α-AlFeSi phase to the Al3Fe equilibrium phase and induced a significant change in solute levels in the solid solution. Homogenization at 550°C significantly reduced the solid solution levels due to the elimination of the supersaturation originating from the cast ingot and produced the lowest flow stress under all of the deformation conditions studied. An increase in the homogenization temperature from 550 to 630°C increased the flow stress by 10 to 23% and 15 to 45% for the Al-0.3Fe-0.1Si and Al-0.3Fe-0.25Si alloys, respectively, over the range of deformation conditions examined. The hot deformation behavior of dilute Al-Fe-Si alloys containing different amounts of Fe (0.1 to 0.7 wt%) and Si (0.1 to 0.25 wt%) was studied by uniaxial compression tests conducted at various temperatures (350-550 °C) and strain rates (0.01-10 s-1). The flow stress of the 1xxx alloys increased with increasing Fe and Si content. Increasing the Fe content from 0.1 to 0.7% raised the flow stress by 11-32% in Al-Fe-0.1Si alloys, whereas the flow stress increased 5-14% when the Si content increased from 0.1 to 0.25% in Al-0.1Fe-Si alloys. The experimental stress-strain data were employed to drive constitutive equations correlating flow stress, deformation temperature and strain rate considering the influence of the chemical composition. The microstructural analysis results revealed that dynamic recovery is the sole softening mechanism during hot deformation of dilute Al-Fe-Si alloys. Increasing the Fe and Si content retarded dynamic recovery and resulted in a decrease in the subgrain size and mean misorientation angle of the boundaries. Furthermore, the hot deformation behavior of dilute Al-Fe-Si alloys containing various Mn (0.1 and 0.2 wt%) and Cu (0.05, 0.18 and 0.31 wt%) contents was also investigated. It was found that both manganese and copper in solid solution have a significant influence on the hot workability of dilute Al-Fe-Si alloys. On a wt% basis, Mn exhibits a stronger strengthening effect compared to Cu. The activation energies for deformation were calculated from experimental data for all the alloys investigated. With a 0.2 wt% Mn addition, the activation energy increased from 161 and 176 kJ/mol for low-Fe (0.1wt%) and high-Fe (0.5wt%) base alloys to 181 and 192 kJ/mol, respectively. The addition of Cu up to 0.31 wt% only slightly increased the activation energy of low-Fe base alloy from 161 to 166 kJ/mol. Solute diffusion acted as the deformation rate controlling mechanism in these dilute alloys. Mn containing alloys have higher flow stress and higher activation energy due to the considerably lower diffusion rate of Mn in aluminum compared to Cu containing alloys. An addition of Mn and Cu also retarded the dynamic recovery and resulted in a decrease in the subgrain size and mean misorientation angle of the grain boundaries. In addition, based on hot compression tests, an artificial neural network model was developed to predict the high temperature flow behavior of Al-0.12Fe-0.1Si-Cu alloys as a function of chemical composition (with Cu contents of 0.002-0.31wt%) and process parameters. A three-layer feed-forward back-propagation artificial neural network with 20 neurons in a hidden layer was established in this study to predict the flow behavior of Al-0.12Fe-0.1Si alloy with various levels of Cu addition (0.002-0.31wt%) at different deformation conditions. The input parameters were Cu content, temperature, strain rate and strain, while the flow stress was the output. The performance of the proposed model was evaluated using various standard statistical parameters. An excellent agreement between experimental and predicted results was obtained. Sensitivity analysis indicated that the strain rate is the most important parameter, while the Cu content exhibited a modest but significant influence on the flow stress. The ANN model proposed in this study can accurately predict the hot deformation behavior of Al-0.12Fe-0.1Si alloys. Les séries 1xxx des alliages d'aluminium sont largement utilisées pour des applications où une excellente aptitude au formage et de la conductivité thermique et électrique sont nécessaires, tels que les tubes d'échangeur de chaleur et les câbles coaxiaux de revêtement. La demande pour une productivité élevée pendant le traitement conduit à une augmentation de l'aptitude au formage à chaud pour fournir une contrainte d'écoulement faible avec les propriétés mécaniques finales souhaitées. Commercialement, les billettes coulées sont généralement homogénéisés avant l'extrusion ou le laminage à chaud, afin d'améliorer leur fluidité et leurs propriétés mécaniques. Cependant, les travaux de recherche antérieurs restent limités au sujet de l'efficacité du traitement d'homogénéisation dans la production des alliages 1xxx. De plus, aucune étude systématique de l'influence des différents éléments d'alliage (Fe, Si, Mn et Cu) sur le comportement de déformation à chaud des alliages diluées Al-Fe-Si est disponible dans la littérature. Dans la présente étude, l'effet des différents éléments d'alliage ainsi que le traitement d'homogénéisation sur le formage à chaud et la microstructure des alliages dilués Al-Fe-Si ont été étudiés en utilisant des tests de compression à chaud, la microscopie optique, SEM, EBSD, TEM, ainsi que les mesures de conductivité électrique. L'effet du traitement d'homogénéisation sur la microstructure et le formage à chaud de deux alliages diluées Al-Fe-Si a été étudiée. L'homogénéisation a favorisé la transformation de phase à partir de la phase métastable AlmFe ou -AlFeSi vers la phase d'équilibre Al3Fe, et induit un changement significatif des concentrations de soluté dans la solution solide. L'homogénéisation à 550 ° C a significativement réduit les niveaux de solution solide en raison de l'élimination de la sursaturation en provenance du lingot coulé et a produit une contrainte d'écoulement plus basse sous toutes les conditions de déformation étudiées. Une augmentation de la température d'homogénéisation de 550 à 630 ° C augmente la contrainte d'écoulement de 10 à 23% et de 15 à 45% pour les alliages Al-0.3Fe-0.1Si et Al-0.3Fe-0.25Si, respectivement, dans la plage des conditions de déformation examinées. Le comportement à la déformation à chaud des alliages diluées Al-Fe-Si contenant diverses quantités de Fe (0,1 à 0,7% en poids) et Si (0,1 à 0,25% en poids) a été étudié par des tests de compression uniaxiale réalisés à différentes températures (350-550 °C) et des vitesses de déformation (de 0,01 à 10 s-1). La contrainte d'écoulement des alliages 1xxx augmente avec l'augmentation de la teneur en Fe et Si. L'augmentation de la teneur en Fe de 0,1 à 0,7% a augmenté la contrainte d'écoulement de 11 à 32% dans les alliages Al-Fe-0.1Si, tandis que la contrainte d'écoulement a augmenté de 5 à 14% lorsque la teneur en Si est portée de 0,1 à 0,25% dans les alliages Al-0,1 Fe-Si. Les données de contrainte-déformation expérimentales ont été utilisées pour dériver les équations constitutives en corrélation entre la contrainte d'écoulement, la température de déformation et la vitesse de déformation, compte tenu de l'influence de la composition chimique. Les résultats de l'analyse de la microstructure a révélé que le recouvrement dynamique est le seul mécanisme de ramollissement lors de la déformation à chaud des alliages diluées Al-Fe-Si. L'augmentation de la teneur en Fe et Si a retardé le recouvrement dynamique et a entraîné une diminution de la taille des sous-grains et de la désorientation des joints des grains. En outre, le comportement en déformation à chaud des alliages dilués Al-Fe-Si contenant diverses teneurs en Mn (0,1 et 0,2% en poids) et en Cu (0,05, 0,18 et 0,31% en poids) a également été étudié. Il a été constaté que le manganèse et le cuivre en solution solide ont une influence significative sur le formage à chaud des alliages dilués Al-Fe-Si. Sur une base de pourcentage massique, le Mn présente un effet de renforcement plus fort par rapport au Cu. Les énergies d'activation pour la déformation ont été calculés à partir de données expérimentales pour tous les alliages étudiés. Avec l’ajout de 0,2% en pourcentage massique de Mn, l'énergie d'activation augmente de 161 et 176 kJ / mol, à faible Fe (0,1% en pourcentage massique) et de haut Fe (0,5% en pourcentage massique) Les alliages à base de 181 et 192 kJ / mol, respectivement. L'addition de Cu jusqu'à 0,31% en pourcentage massique n'a que légèrement augmenté l'énergie d'activation de faible alliage à base de Fe de 161 à 166 kJ / mol. La diffusion du soluté a agi en tant que mécanisme de contrôle des taux de déformation dans ces alliages dilués. Les alliages contenant du Mn ont une contrainte d'écoulement plus élevée et une énergie d'activation plus élevée en raison de la vitesse de diffusion considérablement plus faible dans l’aluminium de Mn par rapport aux alliages contenant du cuivre. Une addition de Mn et Cu a aussi retardé le recouvrement dynamique et a généré une diminution de la taille des sous-grains et une désorientation des joints de grains. En outre, sur la base des données expérimentales des essais de compression à chaud, un modèle base sur les réseaux de neurones artificiels a été développé pour prédire le comportement en écoulement à haute température de l'alliages Al-0.12Fe-0.1Si-Cu en fonction de la composition chimique (avec différentes teneurs en Cu de 0.002-0.31 en pourcentage massique) et les paramètres de procédé. Un réseau de neurones de type backpropagation à trois couches avec 20 neurones dans la couche cachée a été établi dans cette étude pour prédire le comportement de l'écoulement de l'alliage Al-0.12Fe-0.1Si avec différents niveaux de Cu (0.002-0.31 en pourcentage massique) à différentes conditions de déformation. Les paramètres d'entrée étaient la teneur en Cu, la température, la vitesse de déformation et la contrainte, tandis que la contrainte d'écoulement constitue la sortie. La performance du modèle proposé a été évaluée à l'aide des différents paramètres statistiques classiques. Un excellent accord entre les résultats expérimentaux et prédits a été obtenu. L'analyse de sensibilité a indiqué que le taux de déformation est le paramètre le plus important, tandis que la teneur en Cu présentait une influence modeste mais significatif sur la contrainte d'écoulement. Le modèle ANN proposé dans cette étude peut prédire avec précision le comportement de déformation à chaud des alliages Al-0.12Fe-0.1Si

Numerical simulations of die casting with uncertainty quantification and optimization using neural networks

Die casting is one type of metal casting in which liquid metal is solidified in a reusable die. In such a complex process, measuring and controlling the process parameters is difficult. Conventional deterministic simulations are insufficient to completely estimate the effect of stochastic variation in the process parameters on product quality. In this research, a framework to simulate the effect of stochastic variation together with verification, validation, uncertainty quantification and design optimization is proposed. This framework includes high-speed numerical simulations of solidification, micro-structure and mechanical properties prediction models along with experimental inputs for calibration and validation. In order to have a better prediction of product quality, both experimental data and stochastic variations in process parameters with numerical modeling are employed. This enhances the utility of traditional numerical simulations used in die casting. OpenCast, a novel and comprehensive computational framework to simulate solidification problems in materials processing is developed. Heat transfer, solidification and fluid flow due to natural convection are modeled. Empirical relations are used to estimate the microstructure parameters and mechanical properties. The fractional step algorithm is modified to deal with the numerical aspects of solidification by suitably altering the coefficients in the discretized equation to simulate selectively only in the liquid and mushy zones. This brings significant computational speed up as the simulation proceeds. Complex domains are represented by unstructured hexahedral elements. The algebraic multigrid method, blended with a Krylov subspace solver is used to accelerate convergence. Multiple case studies are presented by coupling surrogate models such as polynomial chaos expansion (PCE) and neural network with OpenCast for uncertainty quantification and optimization. The effects of stochasticity in the alloy composition, boundary and initial conditions on the product quality of die casting are analyzed using PCE. Further, a high dimensional stochastic analysis of the natural convection problem is presented to model uncertainty in the material properties and boundary conditions using neural networks. In die casting, heat extraction from molten metal is achieved by cooling lines in the die which impose nonuniform boundary temperatures on the mold wall. This boundary condition along with the initial molten metal temperature affect the product quality quantified in terms of micro-structure parameters and yield strength. Thus, a multi-objective optimization problem is solved to demonstrate a procedure for improvement of product quality and process efficiency

Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors