Abstract
This paper reports on the effectiveness of a back-propagation artificial neural network model that predicts the microhardness of 304 austenitic stainless steel samples which have been implanted with nitrogen using plasma immersion ion implantation (PIII) 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.