Análise de junta aparafusada de biela automotiva

Pós-graduação em Engenharia Mecânica - FEG

Utilização da função desirability na otimização do processo de usinagem da superliga NIMONIC 80A

In the current economic scenario, it is important to the incessant search for improvements in production quality and also in reducing costs. The great competition and technological innovation makes customers stay more and more demanding and seek multiple sources of improvement in production. The work aimed to use the general desirability to optimize a process involving multiple answers in machining experiment. The process of cylindrical turning is one of the most common processes of metal cutting machining and involves several factors, in which will be analysed the best combination of the input factors in machining process, with variable response to surface roughness (Ra) and cutting length (Lc) that vary important answers to measure process efficiency and product quality. The method is a case study, since it involves a study of a tool well addressed in the literature. Data analysis was used in the process of doctoral thesis of Ricardo Penteado on the theme using metaheuristicas combined with different methods of bonding for the optimization of a turning process of multiple responses, then used the desirability and analysis tool. Joint optimization by desirability, the method proposed the following combination of input variables, variable cutting speed at 90 m/min ( -1 level), the breakthrough in 0, 12 mm/revol. ( -1 level), the machining depth should be in 1.6 mm (level 1), gum used must be the TP2500 ( -1 level), in abundant fluid (level 1) and laminated material (level 1) to the maximization of the cutting length (Lc) and minimization of roughness (Ra)

Utilização da função desirability na otimização do processo de usinagem da superliga NIMONIC 80A

In the current economic scenario, it is important to the incessant search for improvements in production quality and also in reducing costs. The great competition and technological innovation makes customers stay more and more demanding and seek multiple sources of improvement in production. The work aimed to use the general desirability to optimize a process involving multiple answers in machining experiment. The process of cylindrical turning is one of the most common processes of metal cutting machining and involves several factors, in which will be analysed the best combination of the input factors in machining process, with variable response to surface roughness (Ra) and cutting length (Lc) that vary important answers to measure process efficiency and product quality. The method is a case study, since it involves a study of a tool well addressed in the literature. Data analysis was used in the process of doctoral thesis of Ricardo Penteado on the theme using metaheuristicas combined with different methods of bonding for the optimization of a turning process of multiple responses, then used the desirability and analysis tool. Joint optimization by desirability, the method proposed the following combination of input variables, variable cutting speed at 90 m/min ( -1 level), the breakthrough in 0, 12 mm/revol. ( -1 level), the machining depth should be in 1.6 mm (level 1), gum used must be the TP2500 ( -1 level), in abundant fluid (level 1) and laminated material (level 1) to the maximization of the cutting length (Lc) and minimization of roughness (Ra)

Fuzzy linguistic model for bioelectrical impedance vector analysis

Backgrounds Ea aims: The boundaries between the categories of body composition provided by vectorial analysis of bioimpedance are not well defined. In this paper, fuzzy sets theory was used for modeling such uncertainty. Methods: An Italian database with 179 cases 18-70 years was divided randomly into developing (n = 20) and testing samples (n = 159). From the 159 registries of the testing sample, 99 contributed with unequivocal diagnosis. Resistance/height and reactance/height were the input variables in the model. Output variables were the seven categories of body composition of vectorial analysis. For each case the linguistic model estimated the membership degree of each impedance category. To compare such results to the previously established diagnoses Kappa statistics was used. This demanded singling out one among the output set of seven categories of membership degrees. This procedure (defuzzification rule) established that the category with the highest membership degree should be the most likely category for the case. Results: The fuzzy model showed a good fit to the development sample. Excellent agreement was achieved between the defuzzified impedance diagnoses and the clinical diagnoses in the testing sample (Kappa = 0.85, p < 0.001). Conclusions: fuzzy linguistic model was found in good agreement with clinical diagnoses. If the whole model output is considered, information on to which extent each BIVA category is present does better advise clinical practice with an enlarged nosological framework and diverse therapeutic strategies. (C) 2012 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Influence of HSM cutting parameters on the surface integrity characteristics of hardened AISI H13 steel

The purpose of this study is to evaluate the influence of the cutting parameters of high-speed machining milling on the characteristics of the surface integrity of hardened AISI H13 steel. High-speed machining has been used intensively in the mold and dies industry. The cutting parameters used as input variables were cutting speed (v c), depth of cut (a p), working engagement (a e) and feed per tooth (f z ), while the output variables were three-dimensional (3D) workpiece roughness parameters, surface and cross section microhardness, residual stress and white layer thickness. The subsurface layers were examined by scanning electron and optical microscopy. Cross section hardness was measured with an instrumented microhardness tester. Residual stress was measured by the X-ray diffraction method. From a statistical standpoint (the main effects of the input parameters were evaluated by analysis of variance), working engagement (a e) was the cutting parameter that exerted the strongest effect on most of the 3D roughness parameters. Feed per tooth (f z ) was the most important cutting parameter in cavity formation. Cutting speed (v c) and depth of cut (a p) did not significantly affect the 3D roughness parameters. Cutting speed showed the strongest influence on residual stress, while depth of cut exerted the strongest effect on the formation of white layer and on the increase in surface hardness.

Benchmarking with Spine Tango: potentials and pitfalls

The newly released online statistics function of Spine Tango allows comparison of own data against the aggregated results of the data pool that all other participants generate. This comparison can be considered a very simple way of benchmarking, which means that the quality of what one organization does is compared with other similar organizations. The goal is to make changes towards better practice if benchmarking shows inferior results compared with the pool. There are, however, pitfalls in this simplified way of comparing data that can result in confounding. This means that important influential factors can make results appear better or worse than they are in reality and these factors can only be identified and neutralized in a multiple regression analysis performed by a statistical expert. Comparing input variables, confounding is less of a problem than comparing outcome variables. Therefore, the potentials and limitations of automated online comparisons need to be considered when interpreting the results of the benchmarking procedure.

Analysis of postprandial lipemia as a Cardiovascular Disease risk factor using genetic and clinical information: an Artificial Neural Network perspective

Clinical studies indicate that exaggerated postprandial lipemia is linked to the progression of atherosclerosis, leading cause of Cardiovascular Diseases (CVD). CVD is a multi-factorial disease with complex etiology and according to the literature postprandial Triglycerides (TG) can be used as an independent CVD risk factor. Aim of the current study is to construct an Artificial Neural Network (ANN) based system for the identification of the most important gene-gene and/or gene-environmental interactions that contribute to a fast or slow postprandial metabolism of TG in blood and consequently to investigate the causality of postprandial TG response. The design and development of the system is based on a dataset of 213 subjects who underwent a two meals fatty prandial protocol. For each of the subjects a total of 30 input variables corresponding to genetic variations, sex, age and fasting levels of clinical measurements were known. Those variables provide input to the system, which is based on the combined use of Parameter Decreasing Method (PDM) and an ANN. The system was able to identify the ten (10) most informative variables and achieve a mean accuracy equal to 85.21%.

An empirical evaluation of the Random Forests classifier models for variable selection in a large-scale lung cancer case-control study

Random Forests™ is reported to be one of the most accurate classification algorithms in complex data analysis. It shows excellent performance even when most predictors are noisy and the number of variables is much larger than the number of observations. In this thesis Random Forests was applied to a large-scale lung cancer case-control study. A novel way of automatically selecting prognostic factors was proposed. Also, synthetic positive control was used to validate Random Forests method. Throughout this study we showed that Random Forests can deal with large number of weak input variables without overfitting. It can account for non-additive interactions between these input variables. Random Forests can also be used for variable selection without being adversely affected by collinearities. ^ Random Forests can deal with the large-scale data sets without rigorous data preprocessing. It has robust variable importance ranking measure. Proposed is a novel variable selection method in context of Random Forests that uses the data noise level as the cut-off value to determine the subset of the important predictors. This new approach enhanced the ability of the Random Forests algorithm to automatically identify important predictors for complex data. The cut-off value can also be adjusted based on the results of the synthetic positive control experiments. ^ When the data set had high variables to observations ratio, Random Forests complemented the established logistic regression. This study suggested that Random Forests is recommended for such high dimensionality data. One can use Random Forests to select the important variables and then use logistic regression or Random Forests itself to estimate the effect size of the predictors and to classify new observations. ^ We also found that the mean decrease of accuracy is a more reliable variable ranking measurement than mean decrease of Gini. ^

Secchi depth maps for the Baltic Sea, 2003-2012, with links to GeoTIFs

Secchi depth is a measure of water transparency. In the Baltic Sea region, Secchi depth maps are used to assess eutrophication and as input for habitat models. Due to their spatial and temporal coverage, satellite data would be the most suitable data source for such maps. But the Baltic Sea's optical properties are so different from the open ocean that globally calibrated standard models suffer from large errors. Regional predictive models that take the Baltic Sea's special optical properties into account are thus needed. This paper tests how accurately generalized linear models (GLMs) and generalized additive models (GAMs) with MODIS/Aqua and auxiliary data as inputs can predict Secchi depth at a regional scale. It uses cross-validation to test the prediction accuracy of hundreds of GAMs and GLMs with up to 5 input variables. A GAM with 3 input variables (chlorophyll a, remote sensing reflectance at 678 nm, and long-term mean salinity) made the most accurate predictions. Tested against field observations not used for model selection and calibration, the best model's mean absolute error (MAE) for daily predictions was 1.07 m (22%), more than 50% lower than for other publicly available Baltic Sea Secchi depth maps. The MAE for predicting monthly averages was 0.86 m (15%). Thus, the proposed model selection process was able to find a regional model with good prediction accuracy. It could be useful to find predictive models for environmental variables other than Secchi depth, using data from other satellite sensors, and for other regions where non-standard remote sensing models are needed for prediction and mapping. Annual and monthly mean Secchi depth maps for 2003-2012 come with this paper as Supplementary materials.