A graph-based modelling methodology for high-pressure networks applied on waterjet machining. (November 2018)

• Record Type: Journal Article
• Authors: Niederberger, S.; Orjuela, R.; Schleuniger, P.; Anderegg, R.; Basset, M.
• Is Part Of: Mechatronics. Volume 55(2018)
• Page Start: 115
• Page End: 128
• Publication Date: 2018-11
• Subjects: High-pressure network modelling -- Graph network -- Lumped parameter model -- Varying parameter model -- Waterjet machining; Computer integrated manufacturing systems -- Periodicals; Flexible manufacturing systems -- Periodicals; Mechatronics -- Periodicals; Productique -- Périodiques; Fabrication, Systèmes flexibles de -- Périodiques; Mécatronique -- Périodiques; Computer integrated manufacturing systems; Flexible manufacturing systems; Mechatronics; Periodicals; 629.89
• DOI: 10.1016/j.mechatronics.2018.08.009
• Languages: English
• ISSNs: 0957-4158

Abstract

Abstract: This paper proposes a graph-based methodology that models high-pressure networks of various topologies. Therefore, a mathematical modelling of a supply network for waterjet machining will be introduced. High-pressure components are assigned to homogeneous segments, each representing a local pressure state as a differential equation. Segments are subsequently interconnected along the fluid flow path as an algebraic equation that allocates a fluid flow to the interconnections, resulting in a lumped parameter model. For this purpose, a graph network description has been used to approximate the spatially distributed high-pressure system. In this way, the proposed methodology offers a flexible modelling to cope with different network topologies. Moreover, a variable fluid compressibility has also been introduced so that a wide operating range can be included. This modelling methodology has been applied to a supply network for waterjet machining. The resulting mathematical model has been verified by measurements from a test bench with a pressure range of 100−400 MPa. It was shown that a variable fluid compressibility improves the model's accuracy and that modelling errors can be reduced in comparison to other existing methodologies.