A Study of a New ELID Grinding Fluid by BP Neural Network Model

2011 ◽  
Vol 58-60 ◽  
pp. 1792-1796
Author(s):  
Wei Li ◽  
Yu Jie Fan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Grinding Wheel ◽  
Precision Grinding ◽  
Elid Grinding ◽  
Environmental Friendly ◽  
Grinding Fluid ◽  
Hard And Brittle Materials ◽  
Ultra Precision ◽  
Bp Neural Network Model

Electronic in-process dressing (ELID) grinding will be a main technology of ultra-precision grinding which has been widely adopted to the ultra-precision and high effectively machining of hard and brittle materials. This study puts forward a new environmental friendly bamboo charcoal bonded (BCB) grinding wheel and develops a new ELID grinding fluid. An oxide layer is mostly determined by the electric performance of grinding fluid in the experiment. This paper founds a model to forecast grinding fluid’s electric performance by BP neural network and MATLAB. This method can be used in developing of ELID grinding machining fluid to improve the ELID grinding effect.

Experimental study on the characteristic and function of oxide film formed on grinding wheel in ELID precision grinding

2019 ◽  
Vol 72 (5) ◽  
pp. 549-555
Author(s):  
Jia-Bo Zhang ◽  
Yang Yang ◽  
Xiao-Hui Zhang ◽  
Jia-Liang Guan ◽  
Li-Yan Zheng ◽  
...  
Keyword(s):  
Oxide Film ◽  
Formation Rate ◽  
Combination Method ◽  
Grinding Wheel ◽  
Precision Grinding ◽  
Content Type ◽  
Elid Grinding ◽  
Grinding Fluid ◽  
Film Forming ◽  
And Function

Purpose The purpose of this study is to investigate the characteristic and function of oxide film formed on grinding wheel in electrolytic in-process dressing (ELID) precision grinding and improve the quality of ELID grinding. Design/methodology/approach Dynamic film forming experiments were carried out with a simulation device close to the actual processing conditions. Then, the ELID grinding experiments of bearing rings were performed using grinding wheels with good film forming effect. The experiment was designed by quadratic regression general rotation combination method. The influence of grinding depth, electrolytic voltage, duty cycle and grinding wheel linear speed on grinding effect is analyzed. Findings A mathematical model for the formation rate of oxide film was established. The experiments show that the composition of grinding wheel and grinding fluid, as well as the electrical parameters, influence the film forming effect. Thus, the oxide film plays an important role in ELID grinding. Originality/value This study provides a reference for the design and selection of grinding wheel and grinding fluid and the setting of process parameters in ELID grinding.

Study on Electrolysis Performance of Bamboo Charcoal Bonded Grinding Wheel

2010 ◽  
Vol 135 ◽  
pp. 447-451
Author(s):  
Wei Li ◽  
Jian Wu ◽  
Bao Gong Geng
Keyword(s):  
Smooth Surface ◽  
Phenolic Resin ◽  
Xrd Analysis ◽  
Surface Generation ◽  
Grinding Wheel ◽  
Bamboo Charcoal ◽  
Sintering Process ◽  
Elid Grinding ◽  
Environmental Friendly ◽  
Hard And Brittle Materials

Electrolytic in-process dressing (ELID) Grinding was an effective machining method for gaining of super smooth surface for hard and brittle materials due to its excellent surface generation capabilities. Bamboo charcoal bonded (BCB) grinding wheel was an environmental friendly ELID grinding wheel which was made up of bamboo charcoal and phenolic resin as bonding agent with high temperature sintering process. In this paper, the electrolysis performances of the BCB grinding wheel with the different resin ratios were researched, and the surface of BCB grinding wheel formed a dense oxide layer in electrolysis action, was illustrated with SEM and XRD analysis.

The Research of ELID Grinding Effect and Surface Quality on Carbonized Cold-Rolled Steel

2012 ◽  
Vol 229-231 ◽  
pp. 542-546
Author(s):  
J.L. Guan ◽  
Li Li Zhu ◽  
H.W. Lu ◽  
Zhi Wei Wang
Keyword(s):  
Surface Roughness ◽  
Surface Quality ◽  
Grinding Wheel ◽  
Rolled Steel ◽  
Precision Grinding ◽  
Cold Rolled Steel ◽  
Elid Grinding ◽  
Peripheral Velocity ◽  
Ultra Precision ◽  
Cold Rolled

In this document, the electrolytic in-process dressing ( ELID ) grinding technique is used for ultra-precision processing experimental research on the carbonized cold-rolled steel (HRC60～80).A surface roughness of Ra6～8nm was obtained after ELID precision grinding. The results proved that adopting micro grain size (W1.5～W36) and high hardness cast iron based diamond grinding wheel, increasing the wheel peripheral velocity (18～20m/s) and reducing grinding depth can effectively improve surface quality and bring the surface roughness down. The wheel peripheral velocity, grinding depth as well as grinding fluid are the main factors during ultra-precision grinding.

Study on the Machining Principle and Experiment of Grinding

2013 ◽  
Vol 395-396 ◽  
pp. 1000-1003
Author(s):  
Qiang Xiao
Keyword(s):  
New Technology ◽  
Surface Damage ◽  
Grinding Wheel ◽  
Process Technology ◽  
Precision Grinding ◽  
Brittle To Ductile Transition ◽  
Elid Grinding ◽  
Ductile Mode ◽  
Grinding Mechanism ◽  
Ultra Precision

ELID for SiC which enables the improvement of surface quality is put forward. ELID grinding technology is new technology of ultra-precision grinding, and the oxide film is formed on grinding wheel by electrolytic in-process technology, thus the wheel is in-process dressed. SiC material removal mechanism and ELID grinding mechanism is analyzed, the character and condition of brittle to ductile transition of SiC and surface formation mechanism of ductile mode grinding of SiC are studied, the results show that ELID grinding can realize ductile grinding ,this will lower the surface damage and improve the machining efficiency.

Research on ELID Ultra-Precision Grinding Performance of SiCp/Al Composites

2014 ◽  
Vol 614 ◽  
pp. 75-78
Author(s):  
Jia Liang Guan ◽  
Lei Zhu ◽  
Ling Chen ◽  
Xin Qiang Ma ◽  
Xiao Hui Zhang
Keyword(s):  
Surface Roughness ◽  
Cast Iron ◽  
Rotation Speed ◽  
Grinding Wheel ◽  
Feed Speed ◽  
Precision Grinding ◽  
Machined Surface ◽  
Elid Grinding ◽  
Machined Surface Roughness ◽  
Ultra Precision

The electrolytic in-process dressing (ELID) grinding technology was adopted for ultra-precision grinding experiments of SiCp/Al composites; the machined surface roughness can obtain Ra0.030μm. The experiments show that: with the grinding wheel rotation speed of 1500r/min, the grinding depth of 0.1μm, and feed speed of 2m/min and using W5 cast iron bonded diamond grinding wheel, the grinding effect can achieve optimal.

Manufacturing Technology of α-Fe Bonded Grinding Wheel Free Abrasive

2018 ◽  
Vol 780 ◽  
pp. 111-115 ◽  
Author(s):  
Ji Cai Kuai ◽  
Dmitrii V. Ardashev ◽  
Jia Qi Zhang ◽  
Hua Li Zhang
Keyword(s):  
Oxide Film ◽  
High Surface ◽  
Grinding Wheel ◽  
Mirror Surface ◽  
Precision Grinding ◽  
Abrasive Particles ◽  
Elid Grinding ◽  
Abrasive Grain ◽  
Ultra Precision ◽  
Free Abrasive

ELID ultra-precision grinding mirror surface can achieve nanometer precision. However, after the grinding wheel passivates the abrasive particles in electrolysis, it is easy to scratch the ultra-precision ELID grinding surface into the grinding process. In order to solve this problem, a non-abrasive grain α-Fe bonded grinding wheel is propose, which contains no abrasive particles. After electrolysis, oxide film is formed on the surface of the wheel. In ultra-precision ELID grinding, there is no abrasive particles involved, only the polishing effect of oxide film. There is no need to worry about the scratching of exfoliated abrasive particles that have been machined on ultra-precision ELID surfaces. Thus achieving extremely high surface accuracy.

Estimating nitrogen contents of apple leaves based on hyperspectral parameter models at different phenophases

2016 ◽  
Vol 6 (2) ◽  
pp. 942-952
Author(s):  
Xicun ZHU ◽  
Zhuoyuan WANG ◽  
Lulu GAO ◽  
Gengxing ZHAO ◽  
Ling WANG
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Shoot Growth ◽  
Hyperspectral Data ◽  
Estimation Model ◽  
Apple Leaves ◽  
Bp Neural Network Model ◽  
Nitrogen Contents

The objective of the paper is to explore the best phenophase for estimating the nitrogen contents of apple leaves, to establish the best estimation model of the hyperspectral data at different phenophases. It is to improve the apple trees precise fertilization and production management. The experiments were done in 20 orchards in the field, measured hyperspectral data and nitrogen contents of apple leaves at three phenophases in two years, which were shoot growth phenophase, spring shoots pause growth phenophase, autumn shoots pause growth phenophase. The study analyzed the nitrogen contents of apple leaves with its original spectral and first derivative, screened sensitive wavelengths of each phenophase. The hyperspectral parameters were built with the sensitive wavelengths. Multiple stepwise regressions, partial least squares and BP neural network model were adopted in the study. The results showed that 551 nm, 716 nm, 530 nm, 703 nm; 543 nm, 705 nm, 699 nm, 756 nm and 545 nm, 702 nm, 695 nm, 746 nm were sensitive wavelengths of three phenophases. R551+R716, R551*R716, FDR530+FDR703, FDR530*FDR703; R543+R705, R543*R705, FDR699+FDR756, FDR699*FDR756and R545+R702, R545*R702, FDR695+FDR746, FDR695*FDR746 were the best hyperspectral parameters of each phenophase. Of all the estimation models, the estimated effect of shoot growth phenophase was better than other two phenophases, so shoot growth phenophase was the best phenophase to estimate the nitrogen contents of apple leaves based on hyperspectral models. In the three models, the 4-3-1 BP neural network model of shoot growth phenophase was the best estimation model. The R2 of estimated value and measured value was 0.6307, RE% was 23.37, RMSE was 0.6274.

scholarly journals Regional logistics demand forecasting: a BP neural network approach

2021 ◽  
Author(s):  
Lijuan Huang ◽  
Guojie Xie ◽  
Wende Zhao ◽  
Yan Gu ◽  
Yi Huang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Demand Forecasting ◽  
Indicator System ◽  
Regional Logistics ◽  
Logistics Enterprises ◽  
Logistics Demand ◽  
Bp Neural Network Model

AbstractWith the rapid development of e-commerce, the backlog of distribution orders, insufficient logistics capacity and other issues are becoming more and more serious. It is very significant for e-commerce platforms and logistics enterprises to clarify the demand of logistics. To meet this need, a forecasting indicator system of Guangdong logistics demand was constructed from the perspective of e-commerce. The GM (1, 1) model and Back Propagation (BP) neural network model were used to simulate and forecast the logistics demand of Guangdong province from 2000 to 2019. The results show that the Guangdong logistics demand forecasting indicator system has good applicability. Compared with the GM (1, 1) model, the BP neural network model has smaller prediction error and more stable prediction results. Based on the results of the study, it is the recommendation of the authors that e-commerce platforms and logistics enterprises should pay attention to the prediction of regional logistics demand, choose scientific forecasting methods, and encourage the implementation of new distribution modes.

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