Metallic powder injection molding using low pressure

The demand by high performance materials that have to support severe service conditions at a reasonable cost has been forcing the powder metallurgy to improve constantly. The most recent and more important innovation in the area is the process of powder injection.Powder injection molding (PIM) is a technology capable of producing a new range of components from powders. This advanced technology overcomes the existent limitations in the forming of products with complex geometry. The process presents countless variations which are used in the industry today. Invariably, it consists of mixing the powders and a thermo-plastic binder, injecting the mass in the mold in the wanted form, debinding, sintering and making optional secondary operations, as for example, machinery.The purpose of this work is to review the metal injection molding techniques and apply the low pressure injection molding process to family of parts using metallic powder with 10 mum particle size. This work also comments the design and construction of a low pressure injection machine and injection molds. (C) 2001 Elsevier B.V. B.V. All rights reserved.

Desenvolvimento de sistemas automáticos de alimentação e descarga de injectora de plásticos

A indústria de componentes para automóveis necessita de constante inovação, no sentido de manter a elevada competitividade, imprescindível à sobrevivência de qualquer empresa neste sector. A automação e robótica são vias incontornáveis para a prossecução dos objetivos de produtividade desejados. Mesmo dentro da automação, a evolução é constante. Para além disso, a crescente inovação nos produtos fabricados, exige também novas soluções em termos de processos de fabrico. Isto leva a que, soluções válidas até uma determinada altura, passem facilmente a obsoletas, com necessidade premente de se implementarem novos sistemas que correspondam às necessidades atuais. Este trabalho teve por base uma necessidade detetada numa empresa fabricante de acessórios para a indústria automóvel: estruturas para estofo de assento automóvel, entre muitos outros. Atualmente, a estrutura do estofo automóvel assenta numa grelha constituída por um conjunto de arames, que é agregado por uma série de tiras de plástico injetadas em seu redor. As máquinas de injeção responsáveis por este processo têm superfície de apartação horizontal, e necessitam de mão-de-obra adjacente para a colocação dos arames no molde e descarga do conjunto, na razão de uma pessoa para duas máquinas, dependendo do tempo de ciclo da injeção. O trabalho consistiu no desenvolvimento de um sistema automático de alimentação e descarga da máquina de injeção, que tivesse por base um sistema já existente, mas que passasse a comportar outras funções até agora não desempenhadas pelos sistemas antigos, ou seja, que fossem capazes de ultrapassar os problemas colocados pela complexidade atual dos produtos em fabricação. O projeto foi concluído com sucesso, tendo sido validado pela empresa FicoCables, através da construção, teste e colocação em funcionamento de vários protótipos.

Aplicação de redes NeuroFuzzy ao processamento de peças automotivas por meio de injeção de polímeros

The injection molding of automotive parts is a complex process due to the many non-linear and multivariable phenomena that occur simultaneously. Commercial software applications exist for modeling the parameters of polymer injection but can be prohibitively expensive. It is possible to identify these parameters analytically, but applying classical theories of transport phenomena requires accurate information about the injection machine, product geometry, and process parameters. However, neurofuzzy networks, which achieve a synergy by combining the learning capabilities of an artificial neural network with a fuzzy set's inference mechanism, have shown success in this field. The purpose of this paper was to use a multilayer perceptron artificial neural network and a radial basis function artificial neural network combined with fuzzy sets to produce an inference mechanism that could predict injection mold cycle times. The results confirmed neurofuzzy networks as an effective alternative to solving such problems.

Análise comparativa entre sistemas de resfriamento de injeção de plástico

This study aims to prove the economic feasibility of the installation of mechanical compression chillers on plastic injection molding machines in order to reduce the production cycle time of toothbrush cables in a specific case study. This evidence was confirmed by the comparative analysis of the system replaced and the new system installed. The old system had only one closed loop cooling tower which pumped chilled water to the injection molds, and the new system has the same tower sending cold water to the condensers of individual chillers installed on each injection machine. We conducted an analysis of energy efficiency in each system, showing that in terms of thermal efficiency virtually nothing has changed, but in terms of electricity demand the new system consumes 60.3 kW more. We conducted an analysis of machine productivity for both systems, showing a much higher productivity of the new system due to reduced cycle times caused by the presence of chillers and their greater cooling capacities. Equipped with data such as electricity rates, increases in operating costs and initial investments, the increase in consumption and demand of electricity plus the cycle time reduction were also calculated over so the simple payback 1 year and 2 months was reached

Estudo da viabilidade de produção da peça alimentação/ladrão para banheiras

The product development field daily works in the chase of new materials and technologies that fulfills the requirements of the consumer market. So, projects are developed in order to theoretically predict what will actually happen. The production of household appliance is not different. To provide a pleasant bath to the costumer, the manufacture of a bathtub counts on many components. With this in mind, this work goal is to study the possibility of production by plastic injection of the assembly water source/overflow pipe used in bathtubs and compare its production cost with the cost of the assembly acquired from third parties. The injection process is widely used on the world stage because of its numerous advantages, however, due to the high cost of the molds, it is important to estimate the time required for the return of the initial investment. To do this, a model was designed to examine its dimensions and then initiate the study of the possibility to inject the components with the available injection machine and the production cycle. With the results, it was found that there was a reduction in the cost of the finished assembly, but a very long time to return the initial investment due to the current financial scenery of the country

Estudo da viabilidade de produção da peça alimentação/ladrão para banheiras

The product development field daily works in the chase of new materials and technologies that fulfills the requirements of the consumer market. So, projects are developed in order to theoretically predict what will actually happen. The production of household appliance is not different. To provide a pleasant bath to the costumer, the manufacture of a bathtub counts on many components. With this in mind, this work goal is to study the possibility of production by plastic injection of the assembly water source/overflow pipe used in bathtubs and compare its production cost with the cost of the assembly acquired from third parties. The injection process is widely used on the world stage because of its numerous advantages, however, due to the high cost of the molds, it is important to estimate the time required for the return of the initial investment. To do this, a model was designed to examine its dimensions and then initiate the study of the possibility to inject the components with the available injection machine and the production cycle. With the results, it was found that there was a reduction in the cost of the finished assembly, but a very long time to return the initial investment due to the current financial scenery of the country

Two-step postmortem angiography with a modified heart-lung machine: preliminary results.

OBJECTIVE: The purpose of this study was to adapt and improve a minimally invasive two-step postmortem angiographic technique for use on human cadavers. Detailed mapping of the entire vascular system is almost impossible with conventional autopsy tools. The technique described should be valuable in the diagnosis of vascular abnormalities. MATERIALS AND METHODS: Postmortem perfusion with an oily liquid is established with a circulation machine. An oily contrast agent is introduced as a bolus injection, and radiographic imaging is performed. In this pilot study, the upper or lower extremities of four human cadavers were perfused. In two cases, the vascular system of a lower extremity was visualized with anterograde perfusion of the arteries. In the other two cases, in which the suspected cause of death was drug intoxication, the veins of an upper extremity were visualized with retrograde perfusion of the venous system. RESULTS: In each case, the vascular system was visualized up to the level of the small supplying and draining vessels. In three of the four cases, vascular abnormalities were found. In one instance, a venous injection mark engendered by the self-administration of drugs was rendered visible by exudation of the contrast agent. In the other two cases, occlusion of the arteries and veins was apparent. CONCLUSION: The method described is readily applicable to human cadavers. After establishment of postmortem perfusion with paraffin oil and injection of the oily contrast agent, the vascular system can be investigated in detail and vascular abnormalities rendered visible.

Comparison of accuracy between compression- and injection-molded complete dentures

Statement of problem. A clinically significant incisal pin opening may occur after processing complete dentures if a compression molding technique is used. To recover the proper vertical dimension of occlusion, a time-consuming occlusal adjustment is necessary that often destroys the anatomy of the artificial teeth. A new injection molding process claims to produce dentures that require few, if any, occlusal adjustments in the laboratory after processing.Purpose. This laboratory study compared incisal pin opening, dimensional accuracy, and laboratory working time for dentures fabricated by this new injection system with dentures constructed by the conventional compression molding technique.Material and methods. Two groups of 6 maxillary and 6 mandibular dentures were evaluated as follows: group 1 (control), Lucitone 199, compression molded with a long cure cycle; and group 2, Lucitone 199, injection molded with a long cure. Incisal pin opening was measured with a micrometer immediately after deflasking. A computerized coordinate measuring machine was used to measure dimensional accuracy of 3-dimensional variations in selected positions of artificial teeth in 4 stages of denture fabrication. Analysis of variance (ANOVA) and t tests were performed to compare the groups.Results. A significant difference was found in pin opening between groups (t test). Horizontal dimensional changes evaluated with repeated measures ANOVA revealed no significant differences between groups. However, analysis of vertical dimensional changes disclosed significant differences between the groups. There was no appreciable difference in laboratory working time for flasking and molding denture bases between the injection and compression molding techniques when polymethyl methacrylate resin was used.Conclusion. The injection molding method produced a significantly smaller incisal pin opening over the standard compression molding technique. The injection molding technique, using polymethyl methacrylate, was a more accurate method for processing dentures. There were no appreciable differences in laboratory working time between the injection and compression molding techniques.

Two-step postmortem angiography with a modified heart-lung machine: preliminary results

OBJECTIVE: The purpose of this study was to adapt and improve a minimally invasive two-step postmortem angiographic technique for use on human cadavers. Detailed mapping of the entire vascular system is almost impossible with conventional autopsy tools. The technique described should be valuable in the diagnosis of vascular abnormalities. MATERIALS AND METHODS: Postmortem perfusion with an oily liquid is established with a circulation machine. An oily contrast agent is introduced as a bolus injection, and radiographic imaging is performed. In this pilot study, the upper or lower extremities of four human cadavers were perfused. In two cases, the vascular system of a lower extremity was visualized with anterograde perfusion of the arteries. In the other two cases, in which the suspected cause of death was drug intoxication, the veins of an upper extremity were visualized with retrograde perfusion of the venous system. RESULTS: In each case, the vascular system was visualized up to the level of the small supplying and draining vessels. In three of the four cases, vascular abnormalities were found. In one instance, a venous injection mark engendered by the self-administration of drugs was rendered visible by exudation of the contrast agent. In the other two cases, occlusion of the arteries and veins was apparent. CONCLUSION: The method described is readily applicable to human cadavers. After establishment of postmortem perfusion with paraffin oil and injection of the oily contrast agent, the vascular system can be investigated in detail and vascular abnormalities rendered visible.

Applied web traffic analysis for numerical encoding of SQL Injection attack features.

SQL Injection Attack (SQLIA) remains a technique used by a computer network intruder to pilfer an organisation’s confidential data. This is done by an intruder re-crafting web form’s input and query strings used in web requests with malicious intent to compromise the security of an organisation’s confidential data stored at the back-end database. The database is the most valuable data source, and thus, intruders are unrelenting in constantly evolving new techniques to bypass the signature’s solutions currently provided in Web Application Firewalls (WAF) to mitigate SQLIA. There is therefore a need for an automated scalable methodology in the pre-processing of SQLIA features fit for a supervised learning model. However, obtaining a ready-made scalable dataset that is feature engineered with numerical attributes dataset items to train Artificial Neural Network (ANN) and Machine Leaning (ML) models is a known issue in applying artificial intelligence to effectively address ever evolving novel SQLIA signatures. This proposed approach applies numerical attributes encoding ontology to encode features (both legitimate web requests and SQLIA) to numerical data items as to extract scalable dataset for input to a supervised learning model in moving towards a ML SQLIA detection and prevention model. In numerical attributes encoding of features, the proposed model explores a hybrid of static and dynamic pattern matching by implementing a Non-Deterministic Finite Automaton (NFA). This combined with proxy and SQL parser Application Programming Interface (API) to intercept and parse web requests in transition to the back-end database. In developing a solution to address SQLIA, this model allows processed web requests at the proxy deemed to contain injected query string to be excluded from reaching the target back-end database. This paper is intended for evaluating the performance metrics of a dataset obtained by numerical encoding of features ontology in Microsoft Azure Machine Learning (MAML) studio using Two-Class Support Vector Machines (TCSVM) binary classifier. This methodology then forms the subject of the empirical evaluation.

Numerical encoding to tame SQL injection attacks.

Recent years have seen an astronomical rise in SQL Injection Attacks (SQLIAs) used to compromise the confidentiality, authentication and integrity of organisations’ databases. Intruders becoming smarter in obfuscating web requests to evade detection combined with increasing volumes of web traffic from the Internet of Things (IoT), cloud-hosted and on-premise business applications have made it evident that the existing approaches of mostly static signature lack the ability to cope with novel signatures. A SQLIA detection and prevention solution can be achieved through exploring an alternative bio-inspired supervised learning approach that uses input of labelled dataset of numerical attributes in classifying true positives and negatives. We present in this paper a Numerical Encoding to Tame SQLIA (NETSQLIA) that implements a proof of concept for scalable numerical encoding of features to a dataset attributes with labelled class obtained from deep web traffic analysis. In the numerical attributes encoding: the model leverages proxy in the interception and decryption of web traffic. The intercepted web requests are then assembled for front-end SQL parsing and pattern matching by applying traditional Non-Deterministic Finite Automaton (NFA). This paper is intended for a technique of numerical attributes extraction of any size primed as an input dataset to an Artificial Neural Network (ANN) and statistical Machine Learning (ML) algorithms implemented using Two-Class Averaged Perceptron (TCAP) and Two-Class Logistic Regression (TCLR) respectively. This methodology then forms the subject of the empirical evaluation of the suitability of this model in the accurate classification of both legitimate web requests and SQLIA payloads.

Development of numerical methodologies to predict the liquid fuel sprays - wall interaction to optimize the mixing process of direct injection spark ignition engines

Nowadays the development of new Internal Combustion Engines is mainly driven by the need to reduce tailpipe emissions of pollutants, Green-House Gases and avoid the fossil fuels wasting. The design of dimension and shape of the combustion chamber together with the implementation of different injection strategies e.g., injection timing, spray targeting, higher injection pressure, play a key role in the accomplishment of the aforementioned targets. As far as the match between the fuel injection and evaporation and the combustion chamber shape is concerned, the assessment of the interaction between the liquid fuel spray and the engine walls in gasoline direct injection engines is crucial. The use of numerical simulations is an acknowledged technique to support the study of new technological solutions such as the design of new gasoline blends and of tailored injection strategies to pursue the target mixture formation. The current simulation framework lacks a well-defined best practice for the liquid fuel spray interaction simulation, which is a complex multi-physics problem. This thesis deals with the development of robust methodologies to approach the numerical simulation of the liquid fuel spray interaction with walls and lubricants. The accomplishment of this task was divided into three tasks: i) setup and validation of spray-wall impingement three-dimensional CFD spray simulations; ii) development of a one-dimensional model describing the liquid fuel – lubricant oil interaction; iii) development of a machine learning based algorithm aimed to define which mixture of known pure components mimics the physical behaviour of the real gasoline for the simulation of the liquid fuel spray interaction.

Development of advanced methods for the simulation of the reacting mixture formation in internal combustion engines with the use of machine learning algorithms

Besides increasing the share of electric and hybrid vehicles, in order to comply with more stringent environmental protection limitations, in the mid-term the auto industry must improve the efficiency of the internal combustion engine and the well to wheel efficiency of the employed fuel. To achieve this target, a deeper knowledge of the phenomena that influence the mixture formation and the chemical reactions involving new synthetic fuel components is mandatory, but complex and time intensive to perform purely by experimentation. Therefore, numerical simulations play an important role in this development process, but their use can be effective only if they can be considered accurate enough to capture these variations. The most relevant models necessary for the simulation of the reacting mixture formation and successive chemical reactions have been investigated in the present work, with a critical approach, in order to provide instruments to define the most suitable approaches also in the industrial context, which is limited by time constraints and budget evaluations. To overcome these limitations, new methodologies have been developed to conjugate detailed and simplified modelling techniques for the phenomena involving chemical reactions and mixture formation in non-traditional conditions (e.g. water injection, biofuels etc.). Thanks to the large use of machine learning and deep learning algorithms, several applications have been revised or implemented, with the target of reducing the computing time of some traditional tasks by orders of magnitude. Finally, a complete workflow leveraging these new models has been defined and used for evaluating the effects of different surrogate formulations of the same experimental fuel on a proof-of-concept GDI engine model.

Flow injection green method for the quantitative analysis of ketoconazole in pharmaceutical preparations

A flow injection method for the quantitative analysis of ketoconazole in tablets, based on the reaction with iron (III) ions, is presented. Ketoconazole forms a red complex with iron ions in an acid medium, with maximum absorbance at 495 nm. The detection limit was estimated to be 1×10--4 mol L-1; the quantitation limit is about 3×10--4 mol L-1 and approximately 30 determinations can be performed in an hour. The results were compared with those obtained with a reference HPLC method. Statistical comparisons were done using the Student's t procedure and the F test. Complete agreement was found at the 0.95 significance level between the proposed flow injection and the HPLC procedures. The two methods present similar precision, i.e., for HPLC the mean relative standard deviation was ca. 1.2% and for FIA ca. 1.6%.