The Predictive Model of Surface Texture Generated by Abrasive Water Jet for Austenitic Steels

Austenitic stainless steel belongs to the best oxidation-resistant alloys, which must function effectively and reliably when used in a corrosion environment. Their attractive combination of properties ensures their stable position in the steel industry. They belong to a group of difficult-to-cut mat...

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Bibliographic Details
Main Authors: Ján Kmec, Miroslav Gombár, Marta Harničárová, Jan Valíček, Milena Kušnerová, Jiří Kříž, Milan Kadnár, Monika Karková, Alena Vagaská
Format: Article
Language:English
Published: MDPI AG 2020-05-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/10/9/3159
Description
Summary:Austenitic stainless steel belongs to the best oxidation-resistant alloys, which must function effectively and reliably when used in a corrosion environment. Their attractive combination of properties ensures their stable position in the steel industry. They belong to a group of difficult-to-cut materials, and the abrasive water jet cutting technology is often used for their processing. Samples made of stainless steel AISI 304 has been used as the experimental material. Data generated during experiments were used to study the effects of AWJ process parameters (high-pressure water volume flow rate, the diameter of the abrasive nozzle, the distance of the nozzle from the material surface, cutting head feed rate, abrasive mass flow, and material thickness) on surface roughness. Based on the analysis and interpretation of all data, a prediction model was created. The main goal of the long-term research was to create the simplest and most usable prediction model for the group of austenitic steels, based on the evaluation of the practical results obtained in the company Watting Ltd. (Budovateľská 3598/38, Prešov, Slovakia) during 20 years of operation and cooperation with customers from industrial practice. Based on specific customer requirements from practice, the publication also contains specific recommendations for practice and a proposal for the classification of the predicted cut quality.
ISSN:2076-3417