Using geometric shape variations to create an inverse model for a sheet metal process

The output of the sheet metal forming process is subject to much variation. This paper develops a method to measure shape variation in channel forming and relate this back to the corresponding process parameter levels of the manufacturing set-up to create an inverse model. The shape variation in the channels is measured using a modified form of the point distribution model (also known as the active shape model). This means that channels can be represented by a weighting vector of minimal linear dimension that contains all the shape variation information from the average formed channel.

The inverse models were created using classifiers that related the weighting vectors to the process parameter levels for the blank holder force (BHF), die radii (DR) and tool gap (TG) of the parameters. Several classifiers were tested: linear, quadratic Gaussian and artificial neural networks. The quadratic Gaussian classifiers were the most accurate and the most consistent type of classifier over all the parameters.

Geometric shape errors in forging: developing a metric and an inverse model

The complexity of the forging process ensures that there is inherent variability in the geometric shape of a forged part. While knowledge of shape error, comparing the desired versus the measured shape, is significant in measuring part quality the question of more interest is what can this error suggest about the forging process set-up? The first contribution of this paper is to develop a shape error metric which identifies geometric shape differences that occur from a desired forged part. This metric is based on the point distribution deformable model developed in pattern recognition research. The second contribution of this paper is to propose an inverse model that identifies changes in process set-up parameter values by analysing the proposed shape error metric. The metric and inverse models are developed using two sets of simulated hot-forged parts created using two different die pairs (simple and 'M'-shaped die pairs). A neural network is used to classify the shape data into three arbitrarily chosen levels for each parameter and it is accurate to at least 77 per cent in the worst case for the simple die pair data and has an average accuracy of approximately 80 per cent when classifying the more complex 'M'-shaped die pair data.

Improving an inverse model of sheet metal forming by neural network based regression

An inverse model for a sheet meta l forming process aims to determine the initial parameter levels required to form the final formed shape. This is a difficult problem that is usually approached by traditional methods such as finite element analysis. Formulating the problem as a classification problem makes it possible to use well established classification algorithms, such as decision trees. Classification is, however, generally based on a winner-takes-all approach when associating the output value with the corresponding class. On the other hand, when formulating the problem as a regression task, all the output values are combined to produce the corresponding class value. For a multi-class problem, this may result in very different associations compared with classification between the output of the model and the corresponding class. Such formulation makes it possible to use well known regression algorithms, such as neural networks. In this paper, we develop a neural network based inverse model of a sheet forming process, and compare its performance with that of a linear model. Both models are used in two modes, classification mode and a function estimation mode, to investigate the advantage of re-formulating the problem as a function estimation. This results in large improvements in the recognition rate of set-up parameters of a sheet metal forming process for both models, with a neural network model achieving much more accurate parameter recognition than a linear model.

A significant inverse relationship between concentrations of plasma hoocysteine and phospholipid docosahexaenoic acid in healthy male subjects

The aim of this study was to investigate the possibility of a relationship between plasma homocysteine (Hcy) and phospholipid FA (PUFA) in healthy Australian males. One hundred thirty six healthy male subjects aged 20–55 yr were recruited from the Melbourne metropolitan area. Each volunteer completed a semiquantitative food frequency questionnaire and gave a blood sample. Plasma Hcy concentrations were determined by an established HPLC method; the plasma phospholipid FA were determined by standard methods. Plasma Hcy concentration was significantly negatively correlated with plasma phospholipid concentration of the PUFA 20∶5n−3 (r=−0.226, P=0.009), 22∶5n−3 (r=−0.182, P=0.036), 22∶6n−3 (r=−0.286, P=0.001), total n−3 (r=−0.270, P=0.002) and the ratio n−3/n−6 PUFA (r=−0.265, P=0.002), and significantly positively correlated with 20∶4n−6 (r=0.180, P=0.037). In the partial correlation analysis, after controlling for serum vitamin B12 and folate concentration, plasma Hcy was significantly negatively correlated with the plasma phospholipid concentration of 22∶6n−3 (r=−0.205, P=0.019), total n−3 (r=−0.182, P=0.038) and the ratio n−3/n−6 PUFA (r=−0.174, P=0.048). Evidence indicates that an increased concentration of n−3 PUFA in tissues has a beneficial effect on cardiovascular health. Our findings provide further evidence that increased consumption of dietary n−3 PUFA increases the concentration of n−3 PUFA in plasma phospholipid, which is associated with a protective effect on cardiovascular diseases and lower plasma Hcy levels. The mechanism that might explain the association between plasma 22∶6n−3 and Hcy levels is not clear.

Modelling regular and estimable inverse demand systems : a distance function approach

To be useful for policy simulation in the current climate of rapid structural change, inverse demand systems must remain regular over substanstial variations in quantities. The distance function is a convenient vehicle for generating such systems. It also allows convenient imposition of prior ideas about the structure of preferences required for realistic policy work. While the distance function directly yields Hicksian inverse demand functions via the Shepard-Hanoch lemma, they are usually explicit in the unobservable level of utility (u), but lack a closed-form representation in terms of the observable variables. Note however that the unobservability of u need not hinder estimation. A simple one-dimensional numerical inversion allows the estimation of the distance function via the parameters of the implied Marshallian inverse demand functions. This paper develops the formal theory for using distance functions in this context, and reports on initial trials on the operational feasibility of the method.

The inverse relationship between number of steps per day and obesity in a population-based sample - the AusDiab study

Background:
Physical activity (PA) is inversely associated with obesity but the effect has been difficult to quantify using questionnaires. In particular, the shape of the association has not yet been well described. Pedometers provide an opportunity to better characterize the association.

Methods:
Residents of households over the age of 25 years in randomly selected census districts in Tasmania were eligible to participate in the AusDiab cross-sectional survey conducted in 1999–2000. 1848 completed the AusDiab survey and 1126 of these (609 women and 517 men) wore a pedometer for 2-weekdays. Questionnaire data on recent PA, TV time and other factors were obtained. The outcomes were waist circumference (in cm) and body mass index (BMI) (kg/m2).

Results:
Increasing daily steps were associated with a decline in the obesity measures. The logarithmic nature of the associations was indicated by a sharper decline for those with lower daily steps. For example, an additional 2000 steps for those taking only 2000 steps per day was associated with a reduction of 2.8 (95% confidence interval (CI): 2.1,4.4) cm in waist circumference among men (for women; 2.2 (95% CI: 0.6, 3.9 cm)) with a baseline of only 2000, steps compared to a 0.7 (95% CI 0.3, 1.1) cm reduction (for women; 0.6 (95% CI: 0.2, 1.0)) for those already walking 10 000 steps daily. In the multivariable analysis, clearer associations were detected for PA and these obesity measures using daily step number rather than PA time by questionnaire.

Interpretation:
Pedometer measures of activity indicate that the inverse association between recent PA and obesity is logarithmic in form with the greatest impact for a given arithmetic step number increase seen at lower levels of baseline activity. The findings from this study need to be examined in prospective settings.

Inverse opal gas sensors : Zn(II)-doped tin dioxide systems for low temperature detection of pollutant gases

Focusing here on the effects of zinc doping in a nanocrystalline matrix of tin dioxide, inverse opal prototype sensors are presented and extensively studied as superior candidates for gas sensing applications. Courtesy of factors including controlled porosity, enhanced surface to volume ratio and homogeneous dispersion of species in the crystalline lattice assured by the sol–gel technique, prototype sensors were prepared with high dopant ratios in a range of new compositions. Exploiting their high sensitivities to low-gas concentrations at low working temperatures, and thanks to the presented templated sol–gel approach, the prepared sensors open up new frontiers in compositional control over the sensing oxide materials, consequently widening the possibilities available in on-demand gas sensor synthesis.

Inverse opal structure of SnO2 and SnO2 : Zn for gas sensing

In the present work, we propose a low cost synthetic sol-gel route that allows to produce high quality oxide nanostructures with inverse opal architecture which, transferred on alumina substrates provided with Pt interdigitated contacts and heater, are tested as gas sensing devices. An opal template of sintered monodisperse polystyrene spheres was filled with alcoholic solutions of metal oxide precursors and transferred on the alumina substrate. The polystyrene template was removed by thermal treatment, leading to the simultaneous sintering of the oxide nanoparticles. Beside SnO₂, a binary oxide well known for gas sensing application, a Zn containing ternary solid solution (SnO₂:Zn, with Zn 10% molar content) was taken into account for sensor preparation. The obtained high quality macro and meso-porous structures, characterized by different techniques, were tested for pollutant (CO, NO₂) and interfering (methanol) gases, showing that very good detection can be reached through the increase of surface area offered by the inverse opal structure and the tailoring of the chemical composition. The electrical characterization performed on the tin dioxide based sensors shows an enhancement of the relative response towards NO₂ at low temperatures in comparison with conventional SnO₂ sensors obtained with sputtering technique. The addition of Zn increases the separation between the operating temperatures for reducing and oxidizing gases and results in a further enhancement of the selectivity to NO₂ detection.

Inverse opal nanoassemblies : novel architectures for gas sensors the SnO2:Zn case

A novel technique is here presented, based on inverse opal metal oxide structures for the production of high quality macro and meso-porous structures for gas sensing. Taking advantage of a sol-gel templated approach. different mixed semiconducting oxides with high surface area, commonly used in chemical sensing application, were synthesized. In this work we report the
comparison between SnO₂ and SnO₂:Zn. As witnessed by Scanning and Transmission Electron Microscopy (SEM and TEM) analyses and by Powder x-ray Diffraction (PX RD), highly ordered meso-porous structures were formed with oxide crystalline size never exceeding 20 nm . The filled templates. in form of thick films, were bound to allumina substrate with Pt interdigitated contacts
and Pt heater, through in situ calcination, in order to perform standard electrical characterization. Pollutant gases like CO and NO₂ and methanol. as interfering gas, were used for the targeted electrical gas tests. All samples showed low detection limits towards both reducing and oxidizing species in low temperature measurements. Moreover, the addition of high molar percentages of Zn( II) affected the beha viour of electrical response improv ing the se lecti vity of the proposed system.

Toughening of a carbon-fibre composite using electrospun poly(Hydroxyether of Bisphenol A) nanofibrous membranes through inverse phase separation and inter-domain etherification

The interlaminar toughening of a carbon fibre reinforced composite by interleaving a thin layer (~20 microns) of poly(hydroxyether of bisphenol A) (phenoxy) nanofibres was explored in this work. Nanofibres, free of defect and averaging several hundred nanometres, were produced by electrospinning directly onto a pre-impregnated carbon fibre material (Toray G83C) at various concentrations between 0.5 wt % and 2 wt %. During curing at 150 °C, phenoxy diffuses through the epoxy resin to form a semi interpenetrating network with an inverse phase type of morphology where the epoxy became the co-continuous phase with a nodular morphology. This type of morphology improved the fracture toughness in mode I (opening failure) and mode II (in-plane shear failure) by up to 150% and 30%, respectively. Interlaminar shear stress test results showed that the interleaving did not negatively affect the effective in-plane strength of the composites. Furthermore, there was some evidence from DMTA and FT-IR analysis to suggest that inter-domain etherification between the residual epoxide groups with the pendant hydroxyl groups of the phenoxy occurred, also leading to an increase in glass transition temperature (~7.5 °C).

Inverse kinematics analysis of 6-RRCRR parallel manipulators

This paper presents a solution to the inverse kinematics of 6-RRCRR parallel manipulators with orthogonal non-intersecting RR-joint configuration. The inverse kinematics solution of such parallel robots compared with that of parallel robots with orthogonal intersecting RR-joint or universal joint configuration is more complex due to the existence of RR-joint variables. A novel methodology is established to define 6 independent variables of the actuators and 12 dependent RR-joint variables using the pose of the mobile platform with respect to the base frame. The constraint of RR-joints are analysed and the numerical algorithm to obtain joint variables is assessed. The forward kinematics of a 6- RRCRR parallel manipulator is modelled and computational analysis is performed in order to numerically verify the accuracy and effectiveness of the proposed methodology for the inverse kinematics analysis. Numerical results of a trajectory tracking simulation are provided. The results verify high accuracy for the proposed inverse kinematics solution of this special family of parallel micromanipulators.