Prediction of residual stresses in roller burnished components : a finite element approach

Surface finish is an important factor in creating the durable metal components, and fatigue strength can be improved if compressive residual stresses are produced in the surface. Burnishing is a finishing process and compressive residual stresses are induced during the process. The present study of minimizing the surface roughness based on the experimental work, and finite element model was developed to evaluate the analytical results. Commercial purity Mild Steel and Aluminium were selected as work specimens and a high carbon high chromium roller was used as a tool for the burnishing process.

Prediction of functional oucomes in stroke inpatients receiving rehabilitation

Background and Purpose: Early identification of predictive factors relevant to functional outcomes for stroke patients is important to the establishment of an effective continuing care program. The objective of this studywas to identify the predictive factors related to functional outcome at discharge after stroke rehabilitation therapy. Methods: 105 first-time stroke patients admitted to the inpatient rehabilitation department of a university-based medical center were recruited for this prospective study. The functional outcomes of the patients were assessed at admission and at discharge using the Functional Independence Measure (FIM). Severity of stroke was determined using the Canadian Neurological Scale (CNS). Age, gender, side of hemiplegia (SIDE), type of stroke (TYPE), onset to admission interval (OAI), and length of rehabilitation stay (LORS) were also included as predictor variables. Results: The mean (′SD) FIM score at discharge (76.6 ′ 26.4) correlated strongly (r = 0.78, p < 0.001) with the admission FIM score (56.3 ′ 24.1), moderately (r = 0.46, p < 0.001) with the admission CNS score (6.1 ′ 2.2), negatively (r = -0.38, p < 0.001) with age (63.2 ′ 12.3 years), negatively (r = -0.26, p = 0.009) with OAI (24.2 ′ 16.0 days), and negatively (r = -0.29, p = 0.002) with LORS (34.7 ′ 16.8 ays). Stepwise regression analyses indicated that admission FIM score, age, and admission CNS score were the stronge predictors of functional outcome and accounted for 66% of the total variation in discharge FIM total score. The admission FIM score was the best predictor and accounted for 61% of the variation. Conclusions: The findings of this study imply that the admission FIM scores for inpatients receiving stroke rehabilitation can be used to predict functional outcomes at discharge from hospital.

Variable definitions: implications for the prediction of pulmonary complications after adult cardiac surgery

Aim: The aim of this paper was to review the implications that variable definitions have for the prediction of post-operative pulmonary complications after cardiac surgery.

Method: A review of the literature from 1980 to 2002. Selected studies demonstrated an original attempt to examine multivariate associations between pre, intra or post-operative antecedents and pulmonary outcomes in patients undergoing coronary artery bypass grafting (CABG). Reports that described the validation of established clinical prediction rules, testing interventions or research conducted in non-human cohorts were excluded from this review.

Results: Consistently, variable factor and outcome definitions are combined for the development of multivariate prediction models that subsequently have limited clinical value. Despite being prevalent there are very few attempts to examine post-operative pulmonary complications (PPC) as endpoints in isolation. The trajectory of pulmonary dysfunction that precedes complications in the post-operative context is not clear. As such there is little knowledge of post-operative antecedents to PPC that are invariably excluded from model development.

Conclusion: Multivariate clinical prediction rules that incorporate antecedent patient and process factors from the continuum of cardiovascular care for specific pulmonary outcomes are recommended. Models such as these would be useful for practice, policy and quality improvement.

Analytic prediction of structural stress-strain relations of microstructured metal

Nanostructured and ultra-fine grained metals have higher strength but extremely limited ductility compared to coarse grained metals. However, their ductility can be greatly improved by introducing a specific range of grain sizes in the microstructures. In the paper, multiscale unit cell approach (UCA) is developed and applied to predict the averaged stress-strain relations of the multiscale microstructure metals. The unit cell models are three-phase structured at different scale lengths of 100 nm, 1 μm and 10 μm with different volume fractions and periodic boundary conditions. The contributions of multi-scale microstructures to the macroscopic structural properties of metals are also studied using a analytic approach—two-step mean-field method (TSMF), where three microstructural parameters are introduced and thus mechanical properties such as strength and ductility are presented as a function of these parameters. For verification of these proposed numerical and theoretical algorithms, the structural properties of the pure nickel with three-grain microstructures are studied and the results from FEA and the proposed theory have good agreement.

Prediction of paediatric asthma hospitalisation using data mining techniques

Research into the prevalence of hospitalisation among childhood asthma cases is undertaken, using a data set local to the Barwon region of Victoria. Participants were the parents/guardians on behalf of children aged between 5-11 years. Various data mining techniques are used, including segmentation, association and classification to assist in predicting and exploring the instances of childhood hospitalisation due to asthma. Results from this study indicate that children in inner city and metropolitan areas may overutilise emergency department services. In addition, this study found that the prediction of hospitalisaion for asthma in children was greater for those with a written asthma management plan.

Improving the prediction of the roll separating force in a hot steel finishing mill

This paper focuses on the development of a hybrid phenomenological/inductive model to improve the current physical setup force model on a five stand industrial hot strip finishing mill. We approached the problem from two directions. In the first approach, the starting point was the output of the current setup force model. A feedforward multilayer perceptron (MLP) model was then used to estimate the true roll separating force using some other available variables as additional inputs to the model.

It was found that it is possible to significantly improve the estimation of a roll separating force from 5.3% error on average with the current setup model to 2.5% error on average with the hybrid model. The corresponding improvements for the first coils are from 7.5% with the current model to 3.8% with the hybrid model. This was achieved by inclusion, in addition to each stand's force from the current model, the contributions from setup forces from the other stands, as well as the contributions from a limited set of additional variables such as: a) aim width; b) setup thickness; c) setup temperature; and d) measured force from the previous coil.

In the second approach, we investigated the correlation between the large errors in the current model and input parameters of the model. The data set was split into two subsets, one representing the "normal" level of error between the current model and the measured force value, while the other set contained the coils with a "large" level of error. Additional set of data with changes in each coil's inputs from the previous coil's inputs was created to investigate the dependency on the previous coil.

The data sets were then analyzed using a C4.5 decision tree. The main findings were that the level of the speed vernier variable is highly correlated with the large errors in the current setup model. Specifically, a high positive speed vernier value often correlated to a large error. Secondly, it has been found that large changes to the model flow stress values between coils are correlated frequently with larger errors in the current setup force model.

Prediction of ductile fracture in the cold forging of steel

The thesis describes the development of a model of the cold forging process that accurately predicts the initiation of ductile fracture. The effect of the deformation history of the material during multiple forming operations is considered. Both two- and three-dimensional numerical models of the forging process were combined with a ductile fracture criterion to predict the material ductility and damage distribution in the workpiece.

Prediction of wool knitwear pilling propensity using support vector machines

The propensity of wool knitwear to form entangled fiber balls, known as pills, on the surface is affected by a large number of factors. This study examines, for the first time, the application of the support vector machine (SVM) data mining tool to the pilling propensity prediction of wool knitwear. The results indicate that by using the binary classification method and the radial basis function (RBF) kernel function, the SVM is able to give high pilling propensity prediction accuracy for wool knitwear without data over-fitting. The study also found that the number of records available for each pill rating greatly affects the learning and prediction capability of SVM models.

Prediction of children's flavour preferences. Effect of age and stability in reported preferences

The present study investigated whether children were able to communicate stable flavour preferences and whether mothers’ ability to correctly identify their child's flavour preferences is related to the stability of their child's flavour preferences. On 2 consecutive days, 75 girls and 77 boys (3–10 years, mean age = 7.1 ± 2.3 years) carried out a preference ranking task for five ice-cream flavours: mint, coffee, chocolate and two variants of vanilla. Without input from their child, mothers ranked four of these flavours according to their own understanding of their child's flavour preferences. Spearman rank order correlations suggest that older aged children (5–10 years) have more stable flavour preferences than younger aged children (3–5 years) (p < 0.05). Only 39% of mothers were able to correctly predict children's most preferred flavour, but significantly more parents (61%) were able to predict children's least preferred flavour (p < 0.05). Mothers’ ability to correctly predict their children's least preferred flavour seemed to be facilitated by children's ability to communicate their least preferred flavours in a consistent manner (p < 0.05). It is recommended to apply proper sensory methodologies with children rather than relying on mothers’ report when interested in children's likes. When interested in children's dislikes mothers’ report might be suitable.