An approach based on current and sound signals for in-process tool wear monitoring

This paper presents a tool condition monitoring system (TCMS) for on-line tool wear monitoring in turning. The proposed TCMS was developed taking into account the necessary trade-off between cost and performance to be applicable in practice, in addition to a high success rate. The monitoring signals were the feed motor current and the sound signal. The former was used to estimate the feed cutting force using the least squares version of support vector machines (LS-SVM). Singular spectrum analysis (SSA) was used to extract information correlated with tool wear from the sound signal. The estimated feed cutting force and the SSA decomposition of the sound signal alone with the cutting conditions constitute the input data to the TCMS. Again LS-SVM was used to estimate tool condition and its reliability for on-line implementation was validated by experiments using AISI 1040 steel. The results showed that the proposed TCMS is fast and reliable for tool condition monitoring.     

Real-time lane tracking using Rao-Blackwellized particle filter

A novel approach to real-time lane modeling using a single camera is proposed. The proposed method is based on an efficient design and implementation of a particle filter which applies the concepts of the Rao-Blackwellized particle filter (RBPF) by separating the state into linear and non-linear parts. As a result the dimensionality of the problem is reduced, which allows the system to perform in real-time in embedded systems. The method is used to determine the position of the vehicle inside its own lane and the curvature of the road ahead to enhance the performance of advanced driver assistance systems. The effectiveness of the method has been demonstrated implementing a prototype and testing its performance empirically on road sequences with different illumination conditions (day and nightime), pavement types, traffic density, etc. Results show that our proposal is capable of accurately determining if the vehicle is approaching the lane markings (Lane Departure Warning), and the curvature of the road ahead, achieving processing times below 2 ms per frame for laptop CPUs, and 12 ms for embedded CPUs.     

Performance bounds in linear control of unstable MIMO systems with pole location constraint

This paper proposes a methodology to compute quadratic performance bounds when the closed loop poles of a discrete-time multivariable control loop are confined to a disk, centred at the origin, and with radius less than one. The underlying philosophy in this constraint is to avoid certain undesirable dynamic features which arise in quadratic optimal designs. An expression for the performance loss due to the pole location constraint is also provided. Using numerical examples, we show that the performance loss is compensated by an improved transient performance, specially visible in the control signals.     

Relaxation of the mouse pubic symphysis during late pregnancy is not accompanied by the influx of granulocytes

In some animals, such as mice and guinea pigs, a hormonally controlled mechanism increases the flexibility of the pubic symphysis and enhances the cervical remodeling necessary for safe delivery. Cervical ripening during pregnancy is associated with a paradoxical influx of leukocytes. However, the changes in cell metabolism during relaxation of the mouse pubic symphysis for delivery have not been extensively studied. In this work, we used light microscopy and transmission and scanning electron microcopy, as well as immunohistochemistry and Western blotting for MMP-8, to investigate the involvement of granulocytes or resident stromal cells in the relaxation of the virgin pubic symphysis during late pregnancy (days 18 and 19, before delivery) in vivo and in explanted joints. MMP-8 was studied because this collagenase is a hallmark for cervical ripening associated with the influx of granulocytes during late pregnancy. Extensive dissolution and disorganization of the extracellular matrix was seen around fibroblastic-like cells in late pregnancy. In contrast to the cervix (positive control), morphological and immunohistochemical analyses revealed that there was no characteristic cellular inflammatory response in the interpubic tissue. Staining for MMP-8 was observed in chondroid and fibroblastic-like cells of virgin and relaxed interpubic ligament, respectively. However, no granulocytes were seen during the extensive remodeling of the pubic joint in late pregnancy. These results indicate that constitutive stromal cells may have an important role in tissue relaxation during remodeling of the pubic symphysis in pregnancy.     

Design Optimization of Composite Prosthetic Tubes Using GA-ANN Algorithm Considering Tsai-Wu Failure Criteria

The investigation of possible failures in composite materials is a matter of very great importance, and the Tsai-Wu criterion is an effective criterion for analyzing those flaws in anisotropic materials and defining whether the material at a given load will or will not suffer structural failure. In this study, an optimization procedure is proposed to minimize the maximum value of Tsai-Wu of laminated composite tubes subject to axial loading. Artificial neural networks and genetic algorithms are chosen as optimization tools. The results of this study show that the developed algorithm converges faster. Then, the maximum Tsai-Wu value is used as the objective function and the fiber orientations are the constraints in the optimization process. The results yielded by them are compared and discussed. Optimal results are compared with respect to the usual initial design. The design approach is recommended for structures where composites are the key load-carrying members such as orthopedic prosthesis.     

Comparing the Slack-Variable Mixture Model With Other Alternatives

There have been many linear regression models proposed to analyze mixture experiments including the Scheffé model, the slack-variable model, and the Kronecker model. The use of the slack-variable model is somewhat controversial within the mixture experiment research community. However, in situations that the slack-variable ingredient is used to fill in the formulation and the remaining ingredients have constraints such that they can be chosen independently of one another, the slack-variable model is extremely popular by practitioners mainly due to the ease of interpretation. In this article, we advocate that for some mixture experiments the slack-variable model has appealing properties including numerical stability and better prediction accuracy when model-term selection is performed. We also explain how the effects of the slack-variable model components should be interpreted and how easy it is for practitioners to understand the components effects. We also investigate how to choose the slack-variable component, what transformation should be used to reduce collinearity, and under what circumstances the slack-variable model should be preferred. Both simulation and practical examples are provided to support the conclusions.     

Local influence in estimating equations

Local influence diagnostics based on estimating equations as the role of a gradient vector derived from any fit function are developed for repeated measures regression analysis. Our proposal generalizes tools used in other studies ( [Cook, 1986] and [Cadigan and Farrell, 2002]), considering herein local influence diagnostics for a statistical model where estimation involves an estimating equation in which all observations are not necessarily independent of each other. Moreover, the measures of local influence are illustrated with some simulated data sets to assess influential observations. Applications using real data are presented.     

Optimal performance assessment for a photo‐Fenton degradation pilot plant driven by solar energy using artificial neural networks

Artificial neural networks (ANN) were proposed as a multivariate experimental design tool for monitoring a photo‐Fenton treatment of wastewaters containing a synthetic mixture of pesticides. ANN and Nelder‐Mead simplex methods were used to find out the optimum operating parameters of a photo‐Fenton pilot plant. ANN was developed to predict the most important operating parameters (e.g., the total organic carbon and the initial mineralization kinetic rate constants of the reactions), which determine the photo‐catalytic degradation efficiency in photo‐Fenton processes. Experimental measurements of temperature, pH, hydrogen peroxide (H₂O₂) consumption, initial concentration of Fe²⁺, and the AE were used as input data for the ANN learning. A feed‐forward with one hidden layer, a Levenberg–Marquardt learning algorithm, a hyperbolic tangent sigmoidal transfer function and a linear transfer function were used to develop the ANN model. The best fitting of the training database was obtained with an ANN architecture constituted by seven neurons in the hidden layer. The simulated results were validated with experimental measurements, showing an acceptable agreement (R² > 0.99). The ANN was subsequently coupled with a Nelder–Mead simplex method to obtain the optimum operating parameters of the photo‐Fenton pilot plant. The H₂O₂ consumption was used as key variable for evaluating the optimization procedure. Errors less than 1% between simulated and experimental data were found. The obtained results showed that the use of ANN provides an excellent predictive performance tool with the additional capability to assess the influence of each operating parameter on the removal process of water pollutants. Copyright © 2011 John Wiley & Sons, Ltd.     

Inactivation of Neosartorya fischeri and Paecilomyces variotii on paperboard packaging material by hydrogen peroxide and heat

This study reports on the influence of heat and hydrogen peroxide combination on the inactivation kinetics of two heat resistant molds: Neosartorya fischeri and Paecilomyces variotii. Spores of different ages (1 and 4 months) of these molds were prepared and D-values (the time required at certain temperature/hydrogen peroxide combination to inactivate 90% of the mold ascospores) were determined using thermal death tubes. D-values found for P. variotii ranged from 1.2 to 25.1 s after exposure to different combinations of heat (40 or 60 °C) and hydrogen peroxide (35 or 40% w/w) while for N. fischeri they varied from 2.7 to 14.3 s after exposure to the same hydrogen peroxide concentrations and higher temperatures (60 or 70 °C). The influence of temperature and hydrogen peroxide concentration on the d-values varied with the genus of mold and their ages. A synergistic effect of heat and hydrogen peroxide in reducing D-values of Paecilomyces variotti and N. fischeri has been observed. In addition to strict control of temperature, time and hydrogen concentration, hygienic storage and handling of laminated paperboard material must be considered to reduce the probability of package’s contamination. All these measures together will ensure package’s sterility that is imperative for the effectiveness of aseptic processing and consequently to ensure the microbiological stability of processed foods during shelf-life.