摘要：Conventional machining processes such as turning, milling and drilling have long been prominent in the metalworking industry but alternative processes which do not require the use of a cutting tool in order to conduct material removal have also been proven to be sufficiently capable of achieving high efficiency in various cases. In particular, Abrasive Waterjet (AWJ) machining can be regarded as a rather appropriate choice for cutting operations, taking into consideration that it involves no heat affected zones, is able to process all material types and create a variety of complex features with success. In the present work, a comprehensive study on the effect of four process parameters, namely jet traverse speed, stand-off distance, abrasive mass flow rate and jet pressure on the width and depth of machined slots on a steel workpiece is conducted. The results are first analyzed with statistical methods in order to determine the effect and the relative importance of each parameter on the produced width and depth of the slots. Finally, these results are used to develop soft computing predictive models based on Artificial Neural Networks (ANN), which can efficiently relate the process parameters with its outcome.
