Three-dimensional fuzzy influence analysis of fitting algorithms on integrated chip topographic modeling

Proceedings of the Institution of Mechanical Engineers, Part B:

Wen

et al. 2020

Self Cite

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.

Section: Platform Preparation and Data Measuringmentioning

confidence: 99%

“…Furthermore, to confirm the prediction qualities based on the comparative evaluations of significance level, a set of assessment indexes are designed or introduced specifically. 26,28,32,33,36 Recursive complexity index…”

Section: Assessments Of Adaptive Predictionmentioning

confidence: 99%

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

Proceedings of the Institution of Mechanical Engineers, Part B:

Wen

et al. 2020

Self Cite

“…For instance, the high convergence in APE emerges as network V is tested, which is suitable to control the amount of sprinkling water or the consumption of deep percolation; network III ensures an excellent evaluation of SIE in AAPE and RMSE, for which provides an accurate effectiveness calibration of infiltration depth and water distribution efficiency, and keeps high credibility in the conditions of evaporation losses and non-productive run-off flows; network IV presents a satisfactory result in Max APE and shows a more-precisely evaluation result of SIE when transpiration losses or soil water seepage are concerned about. Network V deserves high attention in the benchmark coefficient of R and simultaneously focuses on the average irrigation efficiency, soil water redistribution, and moisture diffusivity as well; an excellent result could be recognized in ORPF when network II is tested, describing the fact that it proposes a robust evaluation of infiltration flow rate and transpiration losses for actual irrigation experiments [57][58][59][60] . Figure 12 The statistical analysis for ANFIS evaluations when different network layouts being applied…”

Section: Figure 11mentioning

confidence: 99%

Influence analysis of sprinkler irrigation effectiveness using ANFIS

International Journal of Agricultural and Biological Engineering

Wen

et al. 2019

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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.

“…After that, a vector diagram of waterjet flow and a spatial representation for fluid bouncing distribution can be acquired, which contributed to the integrated expressions of dynamic waterjet variations and energetic changes. Since the real-time measurements of multi-phase abrasive waterjet, including the transparent or semitransparent fluid flows in high-speed motion, usually outperforms the capability of traditional monitoring, a strain gauge system consists by dual-plane sensors, EMF flow meter (OPTI-FLUX 4000, from KROHNE), and pressure sensors was used as an effective supplementary for flow measurement (Liang, Ye, Wang, & Brauwer, 2012). It was mounted on the backside of sheet-typed ring by 2-D array with vertical and span wise axes, to inspect the shock-wave vibration and stress distribution of waterjet energy beam concurrently.…”

Section: Orthogonal Experiments For the Verification Of Fuzzy Predictionmentioning

confidence: 99%

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

Shan

Engineering Applications of Computational Fluid Mechanics

et al. 2017

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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