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TEM investigations of damage structure of pure gold after nitrogen plasma immersion ion implantation (PIII) using RF discharges

Ławrynowicz Z.

Journal of Polish CIMAC

2010

Vol. 5, no 3

87-94

The paper presents experimental results concerning microstructural changes caused by PIII. Thus, the main aim of these experiments was to make preliminary investigations of structural damage introduced by N+ PIII into gold in such circumstances where precipitation processes are not available and structural damage only exists. After e1ectropo1ishing Au foi1s with a [100] preferred orientation were PIII imp1anted to a total dose of 2×1017 N+ ions/cm2. Gold was used in the present study for three reasons: first, the noble character of gold will minimize contamination prob1ems, second, thus far there are no definite evidence about gold nitride formation even when the dose was as high as 2×1018 N+/cm2, and third, theoretica1 ca1cu1ations give for this metal the highest interstitial and substitutional solubility. It has been observed that pure gold after nitrogen PIII contains a great deal of small gas bubbles. These bubbles can grow as a result of their migration, consequent collision and coalescence. It may be assumed that bubble creation, migration, coalescence and growth can best be reduced by the introduction of stable precipitates into the metal.

Artificial neural networks for modelling the microhardness of plasma immersion ion implanted AISI 304 SS

Durmus H., Saklakoglu N.

Computer Methods in Materials Science

2008

Vol. 8, No. 2

86-92

This paper reports on the effectiveness of a back-propagation artificial neural network model that predicts the micro-hardness of 304 austenitic stainless steel samples which have been implanted with nitrogen using plasma immersion ion implantation (PIH) at different temperatures between 350 and 500°C. Artificial Neural Networks (ANNs) have the capacity to eliminate the need for expensive and difficult experimental investigation in testing and manufacturing processes. This paper shows that ANNs can be employed for optimizing the process parameters of AISI 304 austenitic stainless steel. Predicted values from the model and experimental values are in close agreement and this indicates the usefulness of applying ANNs in predicting hardness results.

W artykule opisano efektywność modelu opartego o sieć neuronową wstecznej propagacji, który przewiduje mikrotwardość stali nierdzewnej AISI 304 poddanej implantacji jonowej plazmowym zanurzeniem w różnych temperaturach pomiędzy 350 i 500°C. Sztuczna sieć neuronowa (SNN) stwarza możliwość ograniczenia kosztownych i trudnych badań doświadczalnych i prób w warunkach przemysłowych. W artykule pokazano, że SNN może zostać zastosowana do optymalizacji parametrów procesu dla austenitycznej stali nierdzewnej AISI 304. Zaobserwowano dobrą zgodność między przewidywaniami modelu i obserwacjami doświadczalnymi, co potwierdza przydatność SNN w modelowaniu twardości wyrobów po implantacji jonowej plazmowym zanurzeniem.