MATEC Web Conf.
Volume 318, 20207th International Conference of Materials and Manufacturing Engineering (ICMMEN 2020)
|Number of page(s)||6|
|Published online||14 August 2020|
Experimental Analysis and Soft Computing Modeling of Abrasive Waterjet Milling of Steel Workpieces
National Technical University of Athens, School of Mechanical Engineering, Heroon Polytechniou 9, 15780 Zografou, Athens, Greece
2 AGH University of Science and Technology, Faculty of Mechanical Engineering and Robotics, al. Mickiewicza 30, 30-059 Cracow, Poland
3 University of Zaragoza, Department of Design and Manufacturing, C/Mar a de Luna 3 -50018 Zaragoza, Spain
4 University of Western Macedonia, Dept. of Product and Systems Design Engineering, Kila Kozani, GR50100, Greece
* Corresponding author: firstname.lastname@example.org
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.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.