Free-stretch-blow Investigation of Poly(ethylene terephthalate) over a Large Process Window

This paper highlights for the first time a full comprehension of the deformation procedure during the injection stretch blow moulding (ISBM) process of poly(ethylene terephthalate) (PET) containers, namely thin-walled rigid bottles. The processes required to form PET bottles are complicated and extensive; any development in understanding the nature of material deformation can potentially improve the bottle optimisation process. Removing the bottle mould and performing free-stretch-blow (FSB) experiments revealed insight into the bottle forming characteristics at various preform temperatures and blowing rates. Process outputs cavity pressure and stretch-rod force were recorded using at instrumented stretch-rod and preform surface strain mapping was determined using a combination of a unique patterning procedure and high speed stereoscopic digital image correlation. The unprecedented experimental analysis reveals that the deformation behaviour varies considerably with contrasting process input parameters. Investigation into the effect on deformation mode, strain rate and final bottle shape provide a basis for full understanding of the process optimisation and therefore how the process inputs may aid development of the preferred optimised container.

Glass fibre-reinforced polyethylene composites in rotational moulding

Fibre-reinforced mouldings are of growing interest to the rotational moulding industry due to their outstanding price performance ratio. However, a particular problem that arises when using reinforcements in this process is that the process is low shear and good mixing of resin and reinforcement is not optimum under those conditions. There is also a problem of the larger/heavier reinforcing agents segregating out of the powder to lay up on the inner part surface. In this paper we report on studies to incorporate, short glass fibres into rotationally moulded parts. Four different approaches were investigated; direct addition of fibre in between two powder shots, addition of a layer of pre-compounded polyethylene-glass fibre pellets between two powder shots, addition of a layer of pre-compounded polyethylene-glass fibre powder between two powder shots and a single layer of glass-reinforced, pre-compounded powder. Results indicate that pre-compounding is necessary to gain performance enhancement and the single layer part made from glass-reinforced, pre-compounded powder exhibited the highest tensile and flexural modulus.

Rotary moulding of ceramic hollow wares

A novel processing method for the fast and economic production of hollow ceramic components has been developed by combining in situ coagulation moulding with a modified version of the technique of rotary moulding[Binner, J. G. P., Al-Dawery, I. A., Tari, G. and Yan, Y., Rotary casting technique. UK Patent application No. 0506349.0, March 2005], the latter being adapted from the polymer industry. The process was found to require a high solids content suspension, hence development work was performed in this direction though in the end a new, commercial suspension was utilised. Of the three forming routes of gel casting, direct coagulation casting and in situ coagulation moulding, the latter was found to be the most promising for the new process of rotary moulding of ceramics. Due to the low value of clay-based ceramics, a new low cost coagulant was identified and the effect of lactone concentration and temperature on setting time determined. Following substantial optimisation work, it was found that a two-speed approach to multi-axial rotation was the most successful; medium sized cream jugs could be produced in just 7 min. With respect to mould materials, the porous resin normally used for pressure casting of sanitary ware was found to be the best option, though since this is quite expensive conventional plaster-of-paris moulds were found to be a suitable material to enable companies, particularly SMEs, to become familiar with the technology whilst avoiding high costs for trials. The processed articles could be successfully fired and glazed using gas-fired kilns with no sign of any black cores. Major advantages of the process include the ability to precisely calculate the amount of ceramic slip required, eliminating either slip wastage or the need to pour used slip back into the virgin material as currently happens with slip casting. In addition, since the precursor suspension has a very high solids content, the time and energy required to dry the green product and associated moulds has been considerably reduced. © 2008 Elsevier Ltd. All rights reserved.

The adaption of microwave heating to the rotational moulding process

This paper details the results from a large European Union rotomoulding research project on the adaptation and development of industrial microwave oven technology to the rotational moulding process. Following computer modelling, an industrial scale microwave oven was specifically designed, manufactured and attached to the drop-arm of a convention rotational moulding machine where extensive moulding trials were carried out. The design and development of the microwave oven and test mould, together with the savings in terms of energy efficiency and mould heating rate that were achieved are discussed.

Characterisation of polyethylene powders for rotational moulding and effect of powder size and shape on densification behaviour

The particle size, shape and distribution of a range of rotational moulding polyethylenes (PEs) ground to powder was investigated using a novel visual data acquisition and analysis system (TP Picture®), developed by Total Petrochemicals. Differences in the individual particle shape factors of the powder samples were observed and correlations with the grinding conditions were determined. When heated, the bubble dissolution behaviour of the same powders was investigated and the shape factor correlated with densification rate, bubble size and bubble distribution.

Study into the densification behaviour of polyethylenes for rotational moulding

The melting and densification behaviour of a range of Polyethylenes (PEs) produced from 2 different catalysts, Ziegler-Natta and Metallocene types, were investigated using a novel visual data acquisition and analysis system (TP Picture®), developed by Total Petrochemicals Research Feluy [1]. Differences in the dissolution behaviour of the bubbles were observed and correlations with the material density, densification rate, bubble size / distribution and MFI were determined.

An innovative electroforming process for oil heated rotational moulding tools

Rotational moulding is a method to produce hollow plastic articles. Heating is normally carried out by placing the mould into a hot air oven where the plastic material in the mould is heated. The most common cooling media are water and forced air. Due to the inefficient nature of conventional hot air ovens most of the energy supplied by the oven does not go to heat the plastic and as a consequence the procedure has very long cycle times. Direct oil heating is an effective alternative in order to achieve better energy efficiency and cycle times. This research work has combined this technology with new innovative design of mould, applying the advantages of electroforming and rapid prototyping. Complex cavity geometries are manufactured by electroforming from a rapid prototyping mandrel. The approach involves conformal heating and cooling channels , where the oil flows into a parallel channel to the electroformed cavity (nickel or copper). Because of this the mould enables high temperature uniformity with direct heating and cooling of the electroformed shell, Uniform heating and cooling is important not only for good quality parts but also for good uniform wall thickness distribution in the rotationally moulded part. The experimental work with the manufactured prototype mould has enabled analysis of the thermal uniformity in the cavity, under different temperatures. Copyright © 2008 by ASME.

The use of annodised aluminum as a rotational moulding tool material

This paper presents the results from investigations into the differences in moulding with a novel anodised machined aluminium mould material and a conventional machined aluminium mould material. Significant differences in terms of cycletime were observed between the moulding carried out with the anodised moulding and the conventional aluminium mould surface material with no change in the shrinkage, part appearance or mechanical properties noted between either.

Mixing behaviour of side injection of air jets and gaseous fuel jets into the axial flow of tunnel kilns

Otto-von-Guericke-Universität Magdeburg, Fakultät für Verfahrens- und Systemtechnik, Dissertation, 2016

Applied web traffic analysis for numerical encoding of SQL Injection attack features.

SQL Injection Attack (SQLIA) remains a technique used by a computer network intruder to pilfer an organisation’s confidential data. This is done by an intruder re-crafting web form’s input and query strings used in web requests with malicious intent to compromise the security of an organisation’s confidential data stored at the back-end database. The database is the most valuable data source, and thus, intruders are unrelenting in constantly evolving new techniques to bypass the signature’s solutions currently provided in Web Application Firewalls (WAF) to mitigate SQLIA. There is therefore a need for an automated scalable methodology in the pre-processing of SQLIA features fit for a supervised learning model. However, obtaining a ready-made scalable dataset that is feature engineered with numerical attributes dataset items to train Artificial Neural Network (ANN) and Machine Leaning (ML) models is a known issue in applying artificial intelligence to effectively address ever evolving novel SQLIA signatures. This proposed approach applies numerical attributes encoding ontology to encode features (both legitimate web requests and SQLIA) to numerical data items as to extract scalable dataset for input to a supervised learning model in moving towards a ML SQLIA detection and prevention model. In numerical attributes encoding of features, the proposed model explores a hybrid of static and dynamic pattern matching by implementing a Non-Deterministic Finite Automaton (NFA). This combined with proxy and SQL parser Application Programming Interface (API) to intercept and parse web requests in transition to the back-end database. In developing a solution to address SQLIA, this model allows processed web requests at the proxy deemed to contain injected query string to be excluded from reaching the target back-end database. This paper is intended for evaluating the performance metrics of a dataset obtained by numerical encoding of features ontology in Microsoft Azure Machine Learning (MAML) studio using Two-Class Support Vector Machines (TCSVM) binary classifier. This methodology then forms the subject of the empirical evaluation.

PCA-ANFIS based prediction for water Injection effectiveness cycle in oil fields.

By proposing a numerical based method on PCA-ANFIS(Adaptive Neuro-Fuzzy Inference System), this paper is focusing on solving the problem of uncertain cycle of water injection in the oilfield. As the dimension of original data is reduced by PCA, ANFIS can be applied for training and testing the new data proposed by this paper. The correctness of PCA-ANFIS models are verified by the injection statistics data collected from 116 wells inside an oilfield, the average absolute error of testing is 1.80 months. With comparison by non-PCA based models which average error is 4.33 months largely ahead of PCA-ANFIS based models, it shows that the testing accuracy has been greatly enhanced by our approach. With the conclusion of the above testing, the PCA-ANFIS method is robust in predicting the effectiveness cycle of water injection which helps oilfield developers to design the water injection scheme.

PCA-FNN based performance prediction for water injection in oilfields.

In order to solve the problem of uncertain cycle of water injection in the oilfield, this paper proposed a numerical method based on PCA-FNN, so that it can forecast the effective cycle of water injection. PCA is used to reduce the dimension of original data, while FNN is applied to train and test the new data. The correctness of PCA-FNN model is verified by the real injection statistics data from 116 wells of an oilfield, the result shows that the average absolute error and relative error of the test are 1.97 months and 10.75% respectively. The testing accuracy has been greatly improved by PCA-FNN model compare with the FNN which has not been processed by PCA and multiple liner regression method. Therefore, PCA-FNN method is reliable to forecast the effectiveness cycle of water injection and it can be used as an decision-making reference method for the engineers.

An investigation into PL/SQL Injection.

SQL injection is a common attack method used to leverage infor-mation out of a database or to compromise a company’s network. This paper investigates four injection attacks that can be conducted against the PL/SQL engine of Oracle databases, comparing two recent releases (10g, 11g) of Oracle. The results of the experiments showed that both releases of Oracle were vulner-able to injection but that the injection technique often differed in the packages that it could be conducted in.

Numerical encoding to tame SQL injection attacks.

Recent years have seen an astronomical rise in SQL Injection Attacks (SQLIAs) used to compromise the confidentiality, authentication and integrity of organisations’ databases. Intruders becoming smarter in obfuscating web requests to evade detection combined with increasing volumes of web traffic from the Internet of Things (IoT), cloud-hosted and on-premise business applications have made it evident that the existing approaches of mostly static signature lack the ability to cope with novel signatures. A SQLIA detection and prevention solution can be achieved through exploring an alternative bio-inspired supervised learning approach that uses input of labelled dataset of numerical attributes in classifying true positives and negatives. We present in this paper a Numerical Encoding to Tame SQLIA (NETSQLIA) that implements a proof of concept for scalable numerical encoding of features to a dataset attributes with labelled class obtained from deep web traffic analysis. In the numerical attributes encoding: the model leverages proxy in the interception and decryption of web traffic. The intercepted web requests are then assembled for front-end SQL parsing and pattern matching by applying traditional Non-Deterministic Finite Automaton (NFA). This paper is intended for a technique of numerical attributes extraction of any size primed as an input dataset to an Artificial Neural Network (ANN) and statistical Machine Learning (ML) algorithms implemented using Two-Class Averaged Perceptron (TCAP) and Two-Class Logistic Regression (TCLR) respectively. This methodology then forms the subject of the empirical evaluation of the suitability of this model in the accurate classification of both legitimate web requests and SQLIA payloads.