Estimation of cutting speed in abrasive water jet using an adaptive wavelet neural network

Ergür, H. Sevil; Oysal, Yusuf

doi:10.1007/s10845-013-0798-y

Cited by 12 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Erkan, Demetgül, Işik, and Tansel (2014) used the Taguchi method and GONNs (Genetically Optimized Neural Network systems) to analyze the discrete data of abrasive flow and the fuzzy selection of waterjet parameters; Zain, Haron, and Sharif (2011) employed the ANN (Artificial Neural Network) and annealing approaches to predict the optimal process parameters of flow models. Ergur and Oysal (2015) studied on the fuzzy optimization of waterjet machining, and the estimation of cutting speed in abrasive waterjet either. Furthermore, Shabgard, Badamchizadeh, Ranjbary, and Amini (2013) investigated the fuzzy predictions of material removal rate, tool wear ratio, and surface roughness, with typical flow models were carefully considered about.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy prediction of AWJ turbulence characteristics by using typical multi-phase flow models

Liang

Shan

Liu

et al. 2017

Engineering Applications of Computational Fluid Mechanics

View full text Add to dashboard Cite

For the purpose of improving the working performance of AWJ (Abrasive waterjet) grinding in actual machining, and revealing the unknown influence mechanism of waterjet machining prediction, this research conducted a theoretical and experimental investigation concerning with the influences caused by multi-phase flow models, on the fuzzy prediction of turbulence characteristics. First, typical flow models describing the abrasive waterjet were presented and enunciated; Second, a series of turbulence characteristics were defined to quantitatively demonstrate the waterjet flow, which are responsible for the actual performances of AWJ grinding in the contact zone; Then a new fuzzy prediction system equipped with logic protocols was established and introduced to predict turbulence characteristics based upon the machining parameters by employing different flow models, therefore a set of quantified prediction results generated from the CFD (Computational Fluid Dynamics) simulation, orthogonal experiments and actual measurements can be obtained and distinguished systematically. Through a detailed performance analysis with benchmark indexes and prediction comparisons, the theoretical superiorities and specific applications of these flow models were fully discussed and then an integrated conclusion assessing their inherent influence mechanism was acquired. Based on these innovative achievements, the instantaneous machining inspection or parameter optimization in AWJ grinding process can be facilitated, simultaneously the new research ideas aimed at waterjet monitoring or grinding improvements can be presented as well. ARTICLE HISTORY

show abstract

Section: Introductionmentioning

confidence: 99%