Formation behaviours of blowhole and chill layer by GTAW in cast iron

Cast irons have some excellent characteristics in wear resistance, vibrational damping etc., and are widely used in many industrial machinery applications. But, the weldability has been much inferior originated from its high-carbon content. Several researches have reported that some of the important factors affecting the weldability were formation of blowhole and chill layer during fusion welding and subsequent rapid cooling.

In this study, the formation behaviours of those were investigated in both flake and nodular graphite cast irons. Tungsten inert gas arc welding was performed using several kinds of filler metal varied nickel and chromium content.

The study was made by paying attention to carbon behaviour and influence of nickel and chromium in filler metal on it.

Experimental results obtained were as follows.

1. The amount of blowhole increased with nickel content and decreased with chromium content.

2. On the other hand, the amount of chill layer increased with chromium content and decreased with nickel content.

3. It was estimated that both blowhole and chill layer formation were related to activity and diffusivity changes of carbon affected by nickel and chromium content. Then consequently they have a correlation relationship each other.

     

Incremental learning of Bayesian sensorimotor models: from low-level behaviours to large-scale structure of the environment

This paper concerns the incremental learning of hierarchies of representations of space in artificial or natural cognitive systems. We propose a mathematical formalism for defining space representations (Bayesian Maps) and modelling their interaction in hierarchies of representations (sensorimotor interaction operator).

We illustrate our formalism with a robotic experiment. Starting from a model based on the proximity to obstacles, we learn a new one related to the direction of the light source. It provides new behaviours, like phototaxis and photophobia. We then combine these two maps so as to identify parts of the environment where the way the two modalities interact is recognisable. This classification is a basis for learning a higher level of abstraction map that describes the large-scale structure of the environment.

In the final model, the perception–action cycle is modelled by a hierarchy of sensorimotor models of increasing time and space scales, which provide navigation strategies of increasing complexities.     

Connectionism,Processing, Memory

computing devices such as Turing machines resolve the dilemma between the necessary finitude of effective procedures and the potential infinity of a function's domain by distinguishing between a finite-state processing part, defined over finitely many representation types, and a memory sufficiently large to contain representation tokens for any of the function's arguments and values. Connectionist networks have been shown to be (at least) Turing-equivalent if provided with infinitely many nodes or infinite-precision activation values and weights. Physical computation, however, is necessarily finite.

The notion of a processing-memory system is introduced to discuss physical computing systems. Constitutive for a processing-memory system is that its causal structure supports the functional distinction between processing part and memory necessary for employing a type-token distinction for representations, which in turn allows for representations to be the objects of computational manipulation. Moreover, the processing part realized by such systems provides a criterion of identity for the function computed as well as helps to define competence and performance of a processing-memory system.

Networks, on the other hand, collapse the functional distinction between processing part and memory. Since preservation of this distinction is necessary for employing a type-token distinction for representation, connectionist information processing does not consist in the computational manipulation of representations. Moreover, since we no longer have a criterion of identity for the function processed other than the behaviour of the network itself, we are left without a competence-performance distinction for connectionist networks,     

Research at the Dr Sheelagh Campbell Laboratory at Teledyne-Impulse-PDM

Dr Sheelagh Campbell FIMF, Honorary Editor-in-Chief of Transactions (2002–2010) and Principal Lecturer at the University of Portsmouth, made important and varied contributions in the fields of chemistry and applied electrochemistry, in research, teaching and committee work, throughout her career.

Following the sad loss of Dr Campbell in 2010, readers and contributors to the journal will be delighted to hear about a research laboratory opened in her name at Teledyne-Impulse-PDM.     

Model for microbiologically influenced corrosion potential assessment for the oil and gas industry

Corrosion is one of the major causes of failure in onshore and offshore oil and gas operations. Microbiologically influenced corrosion (MIC) is inherently more complex to predict, detect and measure because, for instance, the presence of biofilm and/or bacterial products is not sufficient to indicate active microbiological corrosion. The major challenge for current MIC models is to correlate factors that influence corrosion (i.e. chemical, physical, biological and molecular variables) with the potential of having MIC. Previous work has proposed the potential for MIC as a simple product of multiple factors, without fully considering the synergy or the interference among the factors. The present work proposes a network-based approach to analyse and predict MIC potential considering the complex interactions among a total of 60 influencing factors and 20 screening parameters. The proposed model has the ability to capture the complex interdependences and the synergic interactions of the factors used to assess MIC potential and uses an object-oriented approach based on a Bayesian Network. The model has been tested and verified using real data from a pipeline leakage incident that was a result of MIC. The proposed model constitutes a significant step in deepening the understanding of when MIC occurs and its predictability.

List of acronyms: APB: acid producing bacteria; Aw: water activity; BN: Bayesian network; MIC: microbiologically influenced corrosion; MMMs: molecular microbiological methods; NRB: nitrate-reducing bacteria; OOBN: object-oriented Bayesian network; PWRI: produced water re-injection; SPs: screening parameters; SRB: sulphate-reducing bacteria; SRPs: sulphate-reducing prokaryotes; TDSs: total dissolved solids     

Electrochemical behaviour and corrosion performance of Mg–Li–Al–Zn anodes with high Al composition

This study investigated the electrochemical and corrosion performance of Mg–Li–Al–Zn anodes with Al compositions of 3 wt.% and 9 wt.%. Mg–Li–Al–Zn alloy with 9 wt.% Al had a relatively negative open-circuit potential and a high discharge voltage in MgCl₂ electrolyte, owing to the distribution of numerous AlLi particles in the matrix of the alloy. AlLi particles were believed to transform to Al particles during the corrosion of the Mg–Li–Al–Zn anode. The high-Al anode material exhibited good corrosion performance since a dense and continuous Mg(OH)₂/Al composite layer covered the surface of the high-Al anode. Experimentally, increasing the Li⁺ concentration in the electrolyte improved the corrosion performance of the Mg anode.