Dynamic Performance Optimization of Circular Sawing Machine Gearbox

et al. 2020

Processes

The bubble bursting process existing in the particle flow is a complex gas-liquid-solid three-phase coupling dynamic problem. The bubble bursting mechanism, including dynamic characteristics and wall effects, is not clear. To address the above matters, we present a modeling method for the piecewise linear interface calculation-volume of fluid (PLIC-VOF) based bubble burst. The bubble bursting process near or on the wall is analyzed to reveal the dynamic characteristics of bubble bursting and obtain the effect of a bubble bursting on the surrounding flow field. Then a particle image velocimetry (PIV) based self-developed experimental observation platform is established, and the effectiveness of the proposed method is verified. Research results indicate that, in the high-speed turbulent environment, a large pressure difference existed in the bubble tail, which induces the bubble burst to occur; the distance between the wall and the bubble decreases; the higher the flow velocity is, the less time is acquired for bubble bursting, but when the flow velocity exceeds the critical velocity 50 m/s, more time is needed; the coalescence-burst process of double bubbles increases the bubble bursting time, which causes the acceleration of particle motion to reduce.

Section: Methodsmentioning

confidence: 99%

Dynamic Characteristics and Wall Effects of Bubble Bursting in Gas-Liquid-Solid Three-Phase Particle Flow

et al. 2020

Processes

“…Assuming that the rotor vibration position is represented by a sinusoid function

, the duty cycle’s function can be described as

[ 14 ]. Meanwhile, the cosine function [ 51 , 52 , 53 , 54 ] of the bistate switching voltage and current of the electromagnets in the k th cycle can be expressed as follows:

where

is a constant for the duty cycle of the bias flux,

is the quantity variation, V is the supply voltage of PA,

is the control angular frequency,

is the switching angular frequency and

.…”

Section: Methodsmentioning

confidence: 99%

Synchronous Sampling-Based Direct Current Estimation Method for Self-Sensing Active Magnetic Bearings

et al. 2020

Sensors

Active magnetic bearings (AMBs) commonly use pulse-width modulation to reduce analogous hardware and manufacturing costs, but they experience sensing process, sensing accuracy and stability problems. To address these issues, a synchronous sampling-based direct current estimation (SS-DCE) method is proposed herein with a bistate switching power amplifier. First—considering the reluctance evolution mechanism of AMBs—a coupling relation mathematical model between rotor displacement and voltage/current is presented to acquire the rotor position from the working coil current alone. Then—assuming that the switching current was an approximately triangular signal—a DCE for the rotor position was established based on the estimation inductance of the charging/discharging phase. Finally—to decrease the phase shift caused by the self-sensing filters and position estimation algorithms—the SS-DCE method was introduced to conduct precise position detection for rotors with high velocities. The simulation and experimental results indicated that the proposed method could improve the sensing accuracy and stability. Compared to other AMB position estimation methods, the simple linearity of the SS-DCE method was greatly improved and could be controlled below 4%. Evaluation using frequency response analysis showed that the SS-DCE method had excellent dynamic accuracy and could perform at a higher phase margin, especially for the uprising/landing transient state. Moreover, there was a phase margin of 158° at the natural frequency of 19.26 HZ, and the peak sensitivity in the 50–250 μm range reached 10.7 dB.

“…Ni et al [75] established a numerical model for the sawing gearbox using numerical simulation software and quantified the problems involved in the structure optimization process. The geometrical dimensions and material parameters were initially set according to the specifications of the manufacturing process of the gearbox.…”

Section: Intelligent Multi-body Dynamic Optimizationmentioning

confidence: 99%

Key Technologies and Development Trends in Advanced Intelligent Sawing Equipments

Zhang

Tan

2021

Chin. J. Mech. Eng.

As a starting point in equipment manufacturing, sawing plays an important role in industrial production. Intelligent manufacturing equipment is an important carrier of intelligent manufacturing technologies. Due to the backwardness of intelligent technology, the comprehensive performance of sawing equipments in China is obviously different from that in foreign countries. State of the art of advanced sawing equipments is investigated along with the technical bottleneck of sawing machine tool manufacturing, and a new industrial scheme of replacing turning-milling by sawing is described. The key technologies of processing-measuring integrated control, multi-body dynamic optimization, the collaborative sawing network framework, the distributed cloud sawing platform, and the self-adapting service method are analyzed; with consideration of the problems of poor processing control stableness, low single machine intelligence level, no on-line processing data service and active flutter suppression of sawing with wide-width and heavy-load working conditions. Suggested directions for further research, industry implementation, and industry-research collaboration are provided.