Metodologia de análise e diagnóstico de fratura em um componente automotivo sujeito a cargas cíclicas alternadas

The competitiveness of the trade generated by the higher availability of products with lower quality and cost promoted a new reality of industrial production with small clearances. Track deviations at the production are not discarded, uncertainties can statistically occur. The world consumer and the Brazilian one are supported by the consumer protection code, in lawsuits against the products poor quality. An automobile is composed of various systems and thousands of constituent parts, increasing the likelihood of failure. The dynamic and security systems are critical in relation to the consequences of possible failures. The investigation of the failure gives us the possibility of learning and contributing to various improvements. Our main purpose in this work is to develop a systematic, specific methodology by investigating the root cause of the flaw occurred on an axle end of the front suspension of an automobile, and to perform comparative data analyses between the fractured part and the project information. Our research was based on a flaw generated in an automotive suspension system involved in a mechanical judicial cause, resulting in property and personal damages. In the investigations concerning the analysis of mechanical flaws, knowledge on materials engineering plays a crucial role in the process, since it enables applying techniques for characterizing materials, relating the technical attributes required from a respective part with its structure of manufacturing material, thus providing a greater scientific contribution to the work. The specific methodology developed follows its own flowchart. In the early phase, the data in the records and information on the involved ones were collected. The following laboratory analyses were performed: macrography of the fracture, micrography with SEM (Scanning Electron Microscope) of the initial and final fracture, phase analysis with optical microscopy, Brinell hardness and Vickers microhardness analyses, quantitative and qualitative chemical analysis, by using X-ray fluorescence and optical spectroscopy for carbon analysis, qualitative study on the state of tension was done. Field data were also collected. In the analyses data of the values resulting from the fractured stock parts and the design values were compared. After the investigation, one concluded that: the developed methodology systematized the investigation and enabled crossing data, thus minimizing diagnostic error probability, the morphology of the fracture indicates failure by the fatigue mechanism in a geometrically propitious location, a tension hub, the part was subjected to low tensions by the sectional area of the final fracture, the manufacturing material of the fractured part has low ductility, the component fractured in an earlier moment than the one recommended by the manufacturer, the percentages of C, Si, Mn and Cr of the fractured part present values which differ from the design ones, the hardness value of the superior limit of the fractured part is higher than that of the design, and there is no manufacturing uniformity between stock and fractured part. The work will contribute to optimizing the guidance of the actions in a mechanical engineering judicial expertise

Fuzzy Logic and Geostatistical Techniques for Spatialization of Soil Texture in Region with Rough Terrains

From the geotechnical standpoint, it is interesting to analyse the soil texture in regions with rough terrain due to its relation with the infiltration and runoff processes and, consequently, the effect on erosion processes. The purpose of this paper is to present a methodology that provides the soil texture spatialization by using Fuzzy logic and Geostatistic. The results were correlated with maps drawn specifically for the study area. The knowledge of the spatialization of soil properties, such as the texture, can be an important tool for land use planning in order to reduce the potential soil losses during rain seasons. (c) 2011 Published by Elsevier Ltd. Selection and peer-review under responsibility of Spatial Statistics 2011

Improved geometric parameterisation techniques for continuation power flow

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Model-Driven requirements engineering process aided by ontologies and natural controlled languages

Researches in Requirements Engineering have been growing in the latest few years. Researchers are concerned with a set of open issues such as: communication between several user profiles involved in software engineering; scope definition; volatility and traceability issues. To cope with these issues a set of works are concentrated in (i) defining processes to collect client s specifications in order to solve scope issues; (ii) defining models to represent requirements to address communication and traceability issues; and (iii) working on mechanisms and processes to be applied to requirements modeling in order to facilitate requirements evolution and maintenance, addressing volatility and traceability issues. We propose an iterative Model-Driven process to solve these issues, based on a double layered CIM to communicate requirements related knowledge to a wider amount of stakeholders. We also present a tool to help requirements engineer through the RE process. Finally we present a case study to illustrate the process and tool s benefits and usage

Avaliação sistemática de abordagens de derivação de produto

Product derivation tools are responsible for automating the development process of software product lines. The configuration knowledge, which is responsible for mapping the problem space to the solution space, plays a fundamental role on product derivation approaches. Each product derivation approach adopts different strategies and techniques to manage the existing variabilities in code assets. There is a lack of empirical studies to analyze these different approaches. This dissertation has the aim of comparing systematically automatic product derivation approaches through of the development of two different empirical studies. The studies are analyzed under two perspectives: (i) qualitative that analyzes the characteristics of approaches using specific criteria; and (ii) quantitative that quantifies specific properties of product derivation artifacts produced for the different approaches. A set of criteria and metrics are also being proposed with the aim of providing support to the qualitative and quantitative analysis. Two software product lines from the web and mobile application domains are targets of our study

Biomechanics Studies in Dentistry: Bioengineering Applied in Oral Implantology

The application of engineering knowledge in dentistry has helped the understanding of biomechanics aspects related to osseointegrated implants. Several techniques have been used to evaluate the biomechanical load oil implants comprising the use of photoelastic stress analysis, finite element stress analysis, and strain-gauge analysis. Therefore, the purpose of this Study was to describe engineering methods used in dentistry to evaluate the biomechanical behavior of osseointegrated implants. Photoelasticity provides good qualitative information oil the overall location and concentration of stresses but produces limited quantitative information. The method serves as ail important tool for determining the critical stress points in a material and is often used for determining stress concentration factors in irregular geometries. The application of strain-gauge method oil dental implants is based oil the use of electrical resistance strain gauges and its associated equipment and provides both in vitro and vivo measurements strains under static and dynamic loads. However, strain-gauge method provides only the data regarding strain at the gauge. Finite element analysis can Simulate stress using a computer-created model to calculate stress, strain, and displacement. Such analysis has the advantage of allowing several conditions to be changed easily and allows measurement of stress distribution around implants at optional points that are difficult to examine clinically All the 3 methodologies call be useful to evaluate biomechanical implant behavior close to the clinical condition but the researcher should have enough knowledge in model fabrication (experimental delineation) and results analysis.

Petroleum well drilling monitoring through cutting image analysis and artificial intelligence techniques

Petroleum well drilling monitoring has become an important tool for detecting and preventing problems during the well drilling process. In this paper, we propose to assist the drilling process by analyzing the cutting images at the vibrating shake shaker, in which different concentrations of cuttings can indicate possible problems, such as the collapse of the well borehole walls. In such a way, we present here an innovative computer vision system composed by a real time cutting volume estimator addressed by support vector regression. As far we know, we are the first to propose the petroleum well drilling monitoring by cutting image analysis. We also applied a collection of supervised classifiers for cutting volume classification. (C) 2010 Elsevier Ltd. All rights reserved.

Structural health evaluation by optimization techinique and artificial neural network

This paper presents two different approaches to detect, locate, and characterize structural damage. Both techniques utilize electrical impedance in a first stage to locate the damaged area. In the second stage, to quantify the damage severity, one can use neural network, or optimization technique. The electrical impedance-based, which utilizes the electromechanical coupling property of piezoelectric materials, has shown engineering feasibility in a variety of practical field applications. Relying on high frequency structural excitations, this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors, and therefore, it is able to detect the damage in its early stage. Optimization approaches must be used for the case where a good condensed model is known, while neural network can be also used to estimate the nature of damage without prior knowledge of the model of the structure. The paper concludes with an experimental example in a welded cubic aluminum structure, in order to verify the performance of these two proposed methodologies.

Remote sensing techniques applied to slope failure analysis

The paper evaluates the applicability of products of remote sensing in studies related to the structural conditionings of slope stability in saprolites, usually conducted through field surveys. In this article we use a regional approach concentrating on an area of lane duplication of a major highway. In that area, resistance reduction to stress and the low cohesions of muscovite saprolites - schists and gneiss which are associated to geological discontinuities, all result in inumerous instabilities. The joints and foliations were extracted from satellite images as well as aerial photographs. Following that, the study area was divided into various sectors based on the directions and dips of the foliation. Different relationships between the structures and the slopes were analyzed in order to indicate the most feasible type of slope failure in each sector of analysis. The aim of the study is to subsidize further detailed future research.

Development of intelligent systems for well drilling and petroleum production

Domains where knowledge representation is too complex to be described analytically and in a deterministic way is very common in the petroleum industry, particularly in the field of exploration and production. In these domains, applications of artificial intelligence techniques are very suitable, especially in cases where the preservation of corporate and technical knowledge is important. The Laboratory for Research on Artificial Intelligence Applied to Petroleum Engineering (LIAP) at Unicamp, has, during the last 10 years, dedicated research efforts to build intelligent systems in well drilling and petroleum production fields. In the following sections, recent advances in intelligent systems, under development in the research laboratory, are described. (C) 2001 Published by Elsevier B.V. B.V.

H-2 and H-infinity - norm control of intelligent structures using LMI techniques

Linear Matrix Inequalities (LMIs) is a powerful too] that has been used in many areas ranging from control engineering to system identification and structural design. There are many factors that make LMI appealing. One is the fact that a lot of design specifications and constrains can be formulated as LMIs [1]. Once formulated in terms of LMIs a problem can be solved efficiently by convex optimization algorithms. The basic idea of the LMI method is to formulate a given problem as an optimization problem with linear objective function and linear matrix inequalities constrains. An intelligent structure involves distributed sensors and actuators and a control law to apply localized actions, in order to minimize or reduce the response at selected conditions. The objective of this work is to implement techniques of control based on LMIs applied to smart structures.

Decision-Making Tool for Knowledge-Based Projects in Offshore Production Systems

The development of an offshore field demands knowledge of many experts to choose the different components of an offshore production system. All the specialized parts of this knowledge are intrinsically related. The aim of this paper is to use Fuzzy Sets and knowledge-based systems to describe and formalize the phases of development of an offshore production system project, in order to share and to manage the required knowledge for carrying out a project, while at the same time proposing alternatives for the oil field configuration.

Extended Hierarchical Censored Production Rules (EHCPRs) system: An approach toward generalized knowledge representation

One common problem in all basic techniques of knowledge representation is the handling of the trade-off between precision of inferences and resource constraints, such as time and memory. Michalski and Winston (1986) suggested the Censored Production Rule (CPR) as an underlying representation and computational mechanism to enable logic based systems to exhibit variable precision in which certainty varies while specificity stays constant. As an extension of CPR, the Hierarchical Censored Production Rules (HCPRs) system of knowledge representation, proposed by Bharadwaj & Jain (1992), exhibits both variable certainty as well as variable specificity and offers mechanisms for handling the trade-off between the two. An HCPR has the form: Decision If(preconditions) Unless(censor) Generality(general_information) Specificity(specific_information). As an attempt towards evolving a generalized knowledge representation, an Extended Hierarchical Censored Production Rules (EHCPRs) system is suggested in this paper. With the inclusion of new operators, an Extended Hierarchical Censored Production Rule (EHCPR) takes the general form: Concept If (Preconditions) Unless (Exceptions) Generality (General-Concept) Specificity (Specific Concepts) Has_part (default: structural-parts) Has_property (default:characteristic-properties) Has_instance (instances). How semantic networks and frames are represented in terms of an EHCPRs is shown. Multiple inheritance, inheritance with and without cancellation, recognition with partial match, and a few default logic problems are shown to be tackled efficiently in the proposed system.

Progress on holographic techniques to study photo-chemical reactions in solid state

In this paper it is presented recent developments in the heterodyne detection holographic techniques for studding photosensitive materials. The actual state of the technique allows simultaneous and independent measurement of the refractive index and of the absorption coefficient changes in photosensitive materials and their use to self-stabilize the fringe pattern. The modeling of the measured signal together with the fringe stabilization allow the long term-fitting of the optical properties and the study the photosensitive materials close to the saturation.

Tuning of fuzzy inference systems through unconstrained optimization techniques

This paper presents a new methodology for the adjustment of fuzzy inference systems. A novel approach, which uses unconstrained optimization techniques, is developed in order to adjust the free parameters of the fuzzy inference system, such as its intrinsic parameters of the membership function and the weights of the inference rules. This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used. Therefore, this methodology is expandable either to the Mandani architecture or also to that suggested by Takagi-Sugeno. The validation of the presented methodology is accomplished through an estimation of time series. More specifically, the Mackey-Glass chaotic time series estimation is used for the validation of the proposed methodology.