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Article

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Title

Efficiency of Tool Steel Cutting by Water Jet with Recycled Abrasive Materials

Authors

[ 1 ] Wydział Techniczny, Akademia im. Jakuba z Paradyża | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology
[2.9] Mechanical engineering

Year of publication

2022

Published in

Materials

Journal year: 2022 | Journal volume: Vol. 15 | Journal number: 11

Article type

scientific article

Publication language

english

Keywords
EN
  • abrasive water jet; AWJ; cutting depth; process efficiency; molybdenum tool steel; response surface method; RSM; modeling; optimization
Abstract

EN High-pressure water jet machining is characterized by wide possibilities of cutting diverse materials together with multi-layer materials with dissimilar properties, accurate cutting complex profiles, as well as conducting them in uncommon conditions, especially in cases of thick materials. An additional advantage of water jet technology is its environmental friendliness. This paper presents tests of the cutting performance of tool steel with the use of an abrasive water jet (AWJ). The state-of-the-art has shown insufficient scientific evidence in AWJ tool steels cutting using recycled abrasive materials. Therefore, the main motivation for this paper was to carry out research from an environment aspect. The reuse of abrasives and the use of recycled materials have immense potential to reduce processing costs while remaining environmentally friendly. The RSM method was used for modeling and optimization. A response surface design (RSM) is a package of an advanced design-of-experiment (DOE) approaches that support better understanding and optimize response, exploring the relationships between several explanatory variables and one or more response variables. Based on this research, feed rate is the key factor influencing the depth of cut, while the water nozzle diameter has a secondary effect, and the concentration of abrasive has the least influence on the depth of cut. High level of variance (the percentage of variability in the reaction that is interpreted by the formula) confirms that the models fit well to the investigational data.

Date of online publication

06.2022

Pages (from - to)

3978(1) - 3978(16)

DOI

https://doi.org/10.3390/ma15113978

License type

CC BY (attribution alone)

Open Access Mode

open journal

Open Access Text Version

final published version

Release date

06.2022 (Date presumed)

Date of Open Access to the publication

at the time of publication

Ministry points / journal

140