Working parameter optimization of strengthen waterjet grinding with the orthogonal-experiment-design-based ANFIS

Liang, Zhongwei; Liao, Shaopeng; Wen, Yiheng; Liu, Xiaochu

doi:10.1007/s10845-016-1285-z

Cited by 19 publications

(13 citation statements)

References 44 publications

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“…Since an accurate description and detailed investigation of irrigation effectiveness influence still kept in an undeveloped state [40] , due to the absence of efficient effectiveness evaluation for their mutual correlation, sprinkler irrigation should be improved through the monitoring of flow properties and the implementation of influence analysis. As Figure 4 presents the tested zone for water sprinkling correction, with the dramatic developments of computer processing power, computational fluid dynamics (CFD) is utilized here to study the detailed knowledge of prediction and evaluation of sprinkler irrigation effectiveness.…”

Section: Methodsmentioning

confidence: 99%

“…As we all know that, sprinkler irrigation is a complicated process in which a bunch of high-pressure sprinkling stream experiences radical and instantaneous changing state caused from different conditional factors [41,42] , some typical flow properties are therefore applied as the crucial factors for irrigation effectiveness analysis: RMS Velocity (R ms ), Turbulence Intensity (T i ), Turbulence Kinetic Energy (T c ), Turbulence Entropy (T e ) and Reynolds Shear Stress (R s ), since they present an accurate and reliable calibration of irrigation effectiveness [40,43,51] . Firstly their reference levels could be determined, with the selected calculation scheme of sprinkling water distribution and infiltration effectiveness, the constructive influences caused by these flow properties on the working capability of sprinkler system are thereby compared with.…”

Section: Methodsmentioning

confidence: 99%

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Influence analysis of sprinkler irrigation effectiveness using ANFIS

Liang

Liu

Wen

et al. 2019

International Journal of Agricultural and Biological Engineering

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In order to improve sprinkler irrigation quality and promote actual irrigation efficiency, the influence analysis of sprinkler irrigation effectiveness (SIE) using ANFIS (Adaptive Neural Fuzzy Inference System) was implemented to balance moisture infiltration and water redistribution in soil field. Firstly, using a detailed description of governing equations proposed for sprinkler irrigation flow, the theoretical foundation and mathematical model of irrigation effectiveness can be established; Secondly, based on a complete preparation of experimental irrigation conditions, a series of calibration indexes quantifying SIE for sprinkler irrigation quality and infiltration efficiency were proposed; Then thirdly, a novel ANFIS system was designed and introduced to evaluate these key effectiveness indexes in actual working operations, so that a series of detailed influence analysis and comprehensive infiltration assessment focusing on sprinkler irrigation effectiveness could be achieved afterwards, which result to the realization of better infiltration equilibrium and higher water redistribution efficiency in actual irrigation test. Therefore, the qualification of sprinkler irrigation effectiveness was achieved, and in addition, the moisture infiltration improvement and soil moisture uniformity were facilitated also in return.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Influence analysis of sprinkler irrigation effectiveness using ANFIS

Liang

Liu

Wen

et al. 2019

International Journal of Agricultural and Biological Engineering

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“…Here, a mixed portion of abrasive grits, including brown-fused alumina grits, cast steel grits, and GCr15 grits is prepared; as Figure 6 presents their microscopic morphology and surface topography, their specific physicochemical properties and chemical compositions could be searched from literature. 21,22 The following representative parameters of strengthen jet grinding, or called as the input parameters of NSAE-ANFIS, which impose great influences on the working effectiveness of abrasive jetting stream are identified, including jetting pressure (P w /MPa), mass of cast steel grits (M c /g), mass of bearing steel grits (M b /g), mass of brown-fused alumina grits (M a /g), and mass rate of abrasives (F a /kg/min). They could be acknowledged from literature 32,33 when the aforementioned flow models are applied accordingly; based on these preparations, the characteristic profile and morphology variation of abrasive jetting stream could be analyzed closely.…”

Section: Simulation and Experimentsmentioning

confidence: 99%

“…To ensure the high repeatability of testing conditions, here P w could be determined by the BF1000-3EB-X full bridge metal foil strain gauges installed on the interior boundary surface of accelerator tank, as Figure 6 illustrates the details; M c , M b , and M a would be delivered by DN6-DN300 metering system and calibrated before each test, and F a is monitored by Coriolis mass flow meter and transmitted on line through signal acquisition from the computerized quadratic encoder. On the other side, V RMS should be obtained by the application of EU-108 ultrasonic flow meter; T i , T c , and T e are determined by the calculation of relevant parametric data based upon the local measurements of abrasive impact stress mentioned above, with their detailed computation equations could be referenced from literature; 21,22 and R s is observed using a set of pressure-probe anemometer (IMFL anemoclinometer) also. Furthermore, it is noteworthy that computer software with graphic interface should be used here for the real-time control of data collection, online fusion, and mathematical analysis.…”

Section: Simulation and Experimentsmentioning

confidence: 99%

“…Gasiunas et al 17 and Braun et al 18 concluded that the increase in turbulent intensity is beneficial and helpful to enhance jetting performance. Brucato and colleagues 19 have simulated two different baffled tanks by employing the Eulerian-Eulerian multi-fluid model (MFM); moreover, references [20][21][22] reported their latest progresses on the working parameter optimization coupled with adaptive neural fuzzy inference system (ANFIS) to predict turbulence effectiveness. It is evident that the effectiveness prediction of abrasive turbulence covers the majority of jetting applications and presents original suggestions to enhance grit-accelerating effectiveness; however, this new topic is still in a state of infancy and its advancement is the motivation for the current work.…”

Section: Introductionmentioning

confidence: 99%

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Effectiveness prediction of abrasive jetting stream of accelerator tank using normalized sparse autoencoder-adaptive neural fuzzy inference system

Liang

Liu

Wen

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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Abrasive jetting stream generated from accelerator tank is crucial to the precision machining of industrial products during the process of strengthen jet grinding. In this article, its effectiveness prediction using normalized sparse autoencoder-adaptive neural fuzzy inference system is carried out to provide an optimal result of jetting stream. A normalized sparse autoencoder-adaptive neural fuzzy inference system capable of calculating the concentration density of abrasive impact stress by normalized sparse autoencoder and identifying the effectiveness indexes of abrasive jetting by adaptive neural fuzzy inference system is proposed to predict the stream effectiveness index in grinding practices, indicating that when turbulence root-mean-square velocity ( VRMS) is 420 m/s, turbulence intensity ( Ti) is 570, turbulence kinetic energy ( Tc) is 540 kJ, turbulence entropy ( Te) is 620 J/K, and Reynolds shear stress ( Rs) is 430 kPa (Error tolerance = ± 5%, the same as follows), the optimized effectiveness quality of abrasive jetting stream could be ensured. The effectiveness prediction involve the following steps: measuring the jet impact data on the interior boundary surface of accelerator tank, calculating the concentration density of abrasive impact stress, establishing the descriptive analytical frame work of normalized sparse autoencoder-adaptive neural fuzzy inference system, adaptive prediction of abrasive jetting stream effectiveness through normalized sparse autoencoder-adaptive neural fuzzy inference system computation, and performance verification of actual effectiveness prediction in the efficiency quantification and quality assessment when it compared to that of alternative approaches, such as genetic, simulated annealing–genetic algorithm, Taguchi, artificial neural network–simulated annealing, and genetically optimized neural network system methods. Objective of this research is to adaptive predict the abrasive jetting stream effectiveness using a new-proposed prediction system, a stable and reliable abrasive jetting stream therefore can be achieved using jetting pressure ( Pw) at 320 MPa, mass of cast steel grits ( Mc) at 270 g, mass of bearing steel grits ( Mb) at 310 g, mass of brown-fused alumina grits ( Ma) at 360 g, and mass rate of abrasives ( Fa) at 0.46 kg/min. It is concluded that normalized sparse autoencoder-adaptive neural fuzzy inference system owns an outstanding predictive capability and possesses a much better working advancement in typical calibration indexes of accuracy and efficiency, meanwhile a high agreement between the fuzzy predicted and actual measured values of effectiveness indexes is ensured. This novel method could be promoted constructively to improve the quality uniformity for abrasive jetting stream and to facilitate the productive managements of abrasive jet machining consequently.

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