The fatigue properties of pressure diecast zinc-aluminium based alloys

The fatigue behaviour of the cold chamber pressure-die-cast alloys: Mazak3, ZA8, ZA27, M3K, ZA8K, ZA27K, K1, K2 and K3 was investigated at temperature of 20°C. The alloys M3K, ZA8K and ZA27K were also examined at temperatures of 50 and 100°C. The ratio between fatigue strength and tensile strength was established at 20°C at 107 cycles. The fatigue life prediction of the alloys M3K, ZA8K and ZA27K was formulated at 20, 50 and 100°C. The prediction formulae were found to be reasonably accurate. All of the experimental alloys were heterogeneous and contained large but varying amounts of pores. These pores were a major contribution and dominated the alloys fatigue failure. Their effect, however, on tensile failure was negligible. The ZA27K possessed the highest tensile strength but the lowest fatigue strength. The relationship between the fracture topography and the microstructure was also determined by the use of a mixed signal of a secondary electron and a back-scattered electron on the SEM. The tensile strength of the experimental alloys was directly proportional to the aluminium content within the alloys. The effect of copper content was also investigated within the alloys K1, K2, ZA8K and K3 which contained 0%, 0.5%, 1.0% and 2.0% respectively. It was determined that the fatigue and tensile strengths improved with higher copper contents. Upon ageing the alloys Mazak3, ZA8 and ZA27 at an ambient temperature for 5 years, copper was also found to influence and maintain the metastable Zn-Al (αm) phase. The copper free Mazak3 upon ageing lost this metastable phase. The 1.0% copper ZA8 alloy had lost almost 50% of its metastable phase. Finally the 2.0% copper ZA27 had merely lost 10% of its metastable phase. The cph zinc contained a limited number of slip systems, therefore twinning deformation was unavoidable in both fatigue and tensile testing.

Failure in composite materials

The economic and efficient exploitation of composite materials in critical load bearing applications relies on the ability to predict safe operational lives without excessive conservatism. Developing life prediction and monitoring techniques in these complex, inhomogeneous materials requires an understanding of the various failure mechanisms which can take place. This article describes a range of damage mechanisms which are observed in polymer, metal and ceramic matrix composites.

Multiple attribute decision making approach to petroleum pipeline route selection

Petroleum pipelines are the nervous system of the oil industry, as this transports crude oil from sources to refineries and petroleum products from refineries to demand points. Therefore, the efficient operation of these pipelines determines the effectiveness of the entire business. Pipeline route selection plays a major role when designing an effective pipeline system, as the health of the pipeline depends on its terrain. The present practice of route selection for petroleum pipelines is governed by factors such as the shortest distance, constructability, minimal effects on the environment, and approachability. Although this reduces capital expenditure, it often proves to be uneconomical when life cycle costing is considered. This study presents a route selection model with the application of an Analytic Hierarchy Process (AHP), a multiple attribute decision making technique. AHP considers all the above factors along with the operability and maintainability factors interactively. This system has been demonstrated here through a case study of pipeline route selection, from an Indian perspective. A cost-benefit comparison of the shortest route (conventionally selected) and optimal route establishes the effectiveness of the model.

A Bayesian modeling approach to multi-dimensional sentiment distributions prediction

Sentiment analysis has long focused on binary classification of text as either positive or negative. There has been few work on mapping sentiments or emotions into multiple dimensions. This paper studies a Bayesian modeling approach to multi-class sentiment classification and multidimensional sentiment distributions prediction. It proposes effective mechanisms to incorporate supervised information such as labeled feature constraints and document-level sentiment distributions derived from the training data into model learning. We have evaluated our approach on the datasets collected from the confession section of the Experience Project website where people share their life experiences and personal stories. Our results show that using the latent representation of the training documents derived from our approach as features to build a maximum entropy classifier outperforms other approaches on multi-class sentiment classification. In the more difficult task of multi-dimensional sentiment distributions prediction, our approach gives superior performance compared to a few competitive baselines. © 2012 ACM.

Automated prediction of deterioration of infants in paediatric intensive care using SpO2

Acute life-threatening events are mostly predictable in adults and children. Despite real-time monitoring these events still occur at a rate of 4%. This paper describes an automated prediction system based on the feature space embedding and time series forecasting methods of the SpO2 signal; a pulsatile signal synchronised with heart beat. We develop an age-independent index of abnormality that distinguishes patient-specific normal to abnormal physiology transitions. Two different methods were used to distinguish between normal and abnormal physiological trends based on SpO2 behaviour. The abnormality index derived by each method is compared against the current gold standard of clinical prediction of critical deterioration. Copyright © 2013 Inderscience Enterprises Ltd.

Hierarchical approach to project planning:the case of a petroleum pipeline construction

Projects exposed to an uncertain environment must be adapted to deal with the effective integration of various planning elements and the optimization of project parameters. Time, cost, and quality are the prime objectives of a project that need to be optimized to fulfill the owner's goal. In an uncertain environment, there exist many other conflicting objectives that may also need to be optimized. These objectives are characterized by varying degrees of conflict. Moreover, an uncertain environment also causes several changes in the project plan throughout its life, demanding that the project plan be totally flexible. Goal programming (GP), a multiple criteria decision making technique, offers a good solution for this project planning problem. There the planning problem is considered from the owner's perspective, which leads to classifying the project up to the activity level. GP is applied separately at each level, and the formulated models are integrated through information flow. The flexibility and adaptability of the models lies in the ease of updating the model parameters at the required level through changing priorities and/or constraints and transmitting the information to other levels. The hierarchical model automatically provides integration among various element of planning. The proposed methodology is applied in this paper to plan a petroleum pipeline construction project, and its effectiveness is demonstrated.