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Effect of Electrode Induction Melting Gas Atomization Process on Fine Powder Yields: Continuous Metal Melt Flow

Wu Jialun, Xia Min, Wang Junfeng, Ge Changchun

Archives of Metallurgy and Materials

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2023

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Vol. 68, iss. 3

839--849

EN

Electrode induction melting gas atomization (EIGA) is a newly developed method for preparing ultra-clean metal powders, and is a completely crucible-free melting and atomization process. Based on conducted several atomization experiments, we found that the fine powder yields obtained during the EIGA process were greatly affected by the status of metal melt flow. While, continuous metal melt flow was beneficial for the yield of fine powders, it was in conflict with the principle described for the vacuum induction melting inert gas atomization (VIGA) process. To understand the critical role of continuous metal melt flow in the EIGA process, a computational fluid dynamics (CFD) approach was developed to simulate the gas atomization process. The D50 particle size of powder prepared by atomization under continuous liquid metal flow was about 70 μm, while that obtained by atomization under non-continuous liquid metal flow was about 100 μm. The diameter distribution results of numerical simulations agreed well with the experimental measurements, which demonstrated the accuracy of our simulation method. This study provides theoretical support for understanding the critical role of continuous metal melt flow and improving fine powder yields in the EIGA process.

2

Nozzle Clogging in Vacuum Induction Melting Gas Atomization: Influence of the Delivery-Tube and Nozzle Coupling

Wang Junfeng, Xia Min, Wu Jialun, Ge Changchun

Archives of Metallurgy and Materials

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2022

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Vol. 67, iss. 4

1359--1370

EN

Nozzle clogging seriously affects the continuity of spraying powder in vacuum induction melting gas atomization (VIGA) process and increases the consumption of gas and raw materials. However, there are few systematic studies on nozzle clogging. This paper reports the physics of nozzle clogging in gas atomization production. The influence of coupling-length of different melt delivery-tubes on nozzle clogging is studied numerically and experimentally. The interface tracking method of Volume of Fluid (VOF) and the large eddy simulation (LES) model are performed for visualizing the melt droplets flow traces in primary atomization and the associated simulation cloud images compared with experimental results. Four delivery-tube coupling-lengths (0 mm, 3 mm, 5 mm, and 7 mm) relative to nozzle position and two gas pressures (3 MPa and 4.5 MPa) are chosen for this study. The results indicated that the coupling-lengths of 0 mm and 3 mm increases the strength of the recirculation zone, the melt droplets backflow is obvious, and the nozzle is blocked. However, this phenomenon eliminated with increasing coupling-lengths, the atomization process is continuous, but the final fine powder yield decreases. This research is of guiding significance and reference for understanding the nozzle clogging of vacuum induction melting gas atomization (VIGA) technology.

3

Intelligent Forecasting of Automatic Train Protection System Failure Rate in China High-speed Railway

Kang Renwei, Wang Junfeng, Cheng Jianfeng, Chen Jianqiu, Pang Yanzhi

Eksploatacja i Niezawodność

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2019

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Vol. 21, no. 4

567--576

EN

Intelligent and personalized dynamic maintenance and spare parts configuration of high-speed railway have been the main trend to guarantee the safety capability of trains. In this paper, a new Automatic Train Protection (ATP) system failure rate calculation method is proposed, and the delay time and embedded dimension are determined by C-C algorithm. Then the phase space is reconstructed from one-dimensional time series to high-dimensional space. Based on chaotic characteristics of failure rate, a short-term intelligent forecasting model of failure rate of ATP system is established. The actual failure statistics from 2010 to 2018 are used as samples to train and test the validity of the model. From prediction results, it shows that the proposed chaos prediction model has an accuracy of 99.71%, which is better than the support vector machine model. Through the intelligent prediction of failure rate, this paper solves the maintenance inflexibility and imbalance of supply and demand of spare parts configuration.

PL

Inteligentna i spersonalizowana dynamiczna konserwacja i konfiguracja części zamiennych pociągów kolei dużych prędkości stanowią ostatnio główny trend w zakresie zapewniania bezpieczeństwa pociągów. W niniejszym artykule zaproponowano nową metodę obliczania intensywności uszkodzeń systemu Automatycznej Ochrony Pociągu (ATP), a czas opóźnienia i wymiar zanurzenia określano za pomocą algorytmu CC. Następnie, przestrzeń fazową przekształcono z jednowymiarowego szeregu czasowego do przestrzeni wielowymiarowej. Opierając się na chaotycznych charakterystykach intensywności uszkodzeń, utworzono model krótkoterminowego inteligentnego prognozowania awaryjności systemu ATP. Do uczenia modelu i weryfikacji jego trafności wykorzystano rzeczywiste dane statystyczne dotyczące awarii pociągów z lat 2010–2018. Z wyników prognoz wynika, że proponowany model predykcji, oparty na teorii chaosu, cechuje się dokładnością na poziomie 99,71%, czyli wyższą niż model maszyny wektorów nośnych. Dając możliwość inteligentnej predykcji intensywności uszkodzeń, niniejsza praca rozwiązuje problem braku elastyczności w utrzymaniu ruchu pociągów oraz braku równowagi między podażą a popytem na części zamienne.