Experiment on the Fabrication of the Ni-Fe Micro Structure

2013 ◽  
Vol 658 ◽  
pp. 209-212
Author(s):  
Xiao Hu Zheng
Keyword(s):  
Process Parameters ◽  
Ph Value ◽  
Back Propagation ◽  
Orthogonal Experimental Design ◽  
Main Process ◽  
Back Propagation Algorithm ◽  
Optimal Process ◽  
Micro Structure ◽  
Artificial Neural Network Ann ◽  
Electroplating Process

The artificial neural network (ANN) and Orthogonal experimental design was applied in the research of main process parameters of electroforming Ni-Fe microstructure. In order to obtain the suitable parameters of the deposit, an ANN with 3 layers was built and trained based on orthogoality experiment using back propagation algorithm. The characteristics of the micro electroforming process were analysed systematically. And the optimal process parameters to obtain Ni-20% Fe deposition was as following: FeSO4•7H20 concentration: 5.5 g/L; PH value of the solution: 2.5; current density: 3.5 A/dm2; electrolyte temperature: 55 °C. The results indicate that the Ni-Fe deposit is bright and compact. Electrodeposited Ni-20% Fe has a strong paramagnetic effect with the smallest value of remanence 0.036 mA•m2 and the coercivity: 0.187 kA/m. The Ni-Fe micro electroplating process for the fabrication of microstructure was optimised.

Intelligent Vision Inspection System for IC Engine Head: An ANN Approach

2012 ◽  
Vol 479-481 ◽  
pp. 2242-2245 ◽  
Author(s):  
Rajesh Kanna ◽  
Manikandan Saravana
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Back Propagation ◽  
Engine Block ◽  
Inspection System ◽  
Back Propagation Algorithm ◽  
Ic Engine ◽  
Vision Inspection ◽  
System Module ◽  
Artificial Neural Network Ann

A machine vision system based on Artificial Neural Network (ANN) for inspection of IC Engine block was developed to identify the misalignment and improper diminishing of holes in the IC Engine block. The developed machine vision and ANN module is compared with the commercial MATLAB® software and found results were satisfactory. This work is broadly divided into four stages, namely Intelligent inspection module, Machine Vision module, ANN module and Expert system module. A system with a camera was used to capture the various segments of head of the IC Engine block. The captured bitmap format image of IC Engine block has to be filtered to remove the noises present while capturing and the size is also altered using SPIHT method to an acceptable size and will be given as input to ANN. Generalized ANN with Back-propagation algorithm was used to inspect the IC Engine block. ANN has to be trained to provide the inspected report.

Prediction of collapsibility of moulds and cores of CO2 sands using a neural network

2003 ◽  
Vol 217 (4) ◽  
pp. 475-487 ◽  
Author(s):  
R K Ohdar ◽  
P T Pushp
Keyword(s):  
Neural Network ◽  
Test Data ◽  
Process Parameters ◽  
Coal Dust ◽  
Back Propagation ◽  
Maximum Error ◽  
Ann Model ◽  
Input Process ◽  
Artificial Neural Network Ann ◽  
Hidden Neurons

The CO2 process of making sand moulds and cores is a well-established process and suitable for all types of foundry. However, the collapsibility of CO2 sand is quite poor. A variety of additives are used to improve collapsibility of CO2 sands. Several other process parameters also affect collapsibility of CO2 sands. In the present investigation an attempt has been made to use an artificial neural network (ANN) model for prediction of the collapsibility of CO2 sand. Experiments were conducted with various input process parameters, such as binder content, gassing time, temperature and additive content using three different additives, namely coal dust, dextrin and alumina. The objective of the experiments was to generate basic data to train a back-propagation ANN model and finally predict collapsibility in terms of retained compressive strength of CO2 sands for the test data. A three-layer neural network model with six input neurons corresponding to six input process parameters, one output neuron corresponding to collapsibility and 19 hidden neurons has been suggested, which gives a maximum error of 2 per cent in prediction of test data. Results indicate that prediction of the collapsibility of CO2 sand with an ANN model is feasible. Predicted values match experimental values quite closely.

The Property of Ni-Fe Electrodeposits for MEMS

2007 ◽  
Author(s):  
Xiao-Hu Zheng
Keyword(s):  
Saturation Magnetization ◽  
Process Parameters ◽  
Current Density ◽  
Ph Value ◽  
Electromagnetic Property ◽  
Electrolyte Temperature ◽  
Optimal Process ◽  
Fe Content ◽  
Coating Microstructure

This paper was focused on the composition controlling, coating microstructure and electromagnetic character of Ni-Fe deposit. The results indicated that the Ni-Fe deposit was bright and compact; the crystal-planes of the deposit were (111), (220) and (200). And the optimal process parameters to obtain Ni-20%Fe deposition was as following: FeSO4·7H20 concentration was 6g/L; PH value of the solution was 2.5; current density was 3.5 A/dm2; electrolyte temperature 55°C. The resistivity of deposit was about 30μΩ·cm, when the Fe(wt.%) ranged from 10% to 50% in the deposit. Electrodeposited Ni-20%Fe has a strong paramagnetism effect with the smallest remanence of 0.5 emu, The coercivity show a monotonic decrease with increasing Fe content in deposit, and the saturation magnetization was only 10% of that of the IJ85 permalloy, which proved that the electroformed Ni-20%Fe alloy has good electromagnetic property and could be used in MEMS actuator fabrication.

scholarly journals Simulation of two-stage automotive shredder residue pyrolysis and gasification process using the Aspen Plus model

BioResources ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. 5964-5984
Author(s):  
Bin Yang ◽  
Ming Chen
Keyword(s):  
Process Parameters ◽  
Aspen Plus ◽  
Product Distribution ◽  
Recycling Rate ◽  
Main Process ◽  
Optimal Process ◽  
Two Stage ◽  
Shredder Residue ◽  
Gasification Process ◽  
Automotive Shredder Residue

The disposal of automotive shredder residue (ASR) directly affects China’s goal of achieving a 95% recycling rate for end-of-life vehicles. Pyrolysis and gasification have gradually become the most commonly used thermochemical technologies for ASR recycling. To obtain more hydrogen-rich syngas, it is necessary to determine the optimal process parameters of the ASR pyrolysis and gasification process. The main process parameters of the two-stage ASR pyrolysis and gasification process were studied using the established Aspen Plus model. Through analyzing the effects of process parameters, such as the temperature, equivalence ratio, and mass ratio of steam to ASR feedstock, on the product distribution and product characteristics of ASR pyrolysis and gasification, the optimal process parameters were determined. A series of comparative experiments under different conditions were conducted. The experimental results verified the accuracy and reliability of the Aspen Plus simulation model for the ASR pyrolysis and gasification processes and verified the practical feasibility of the process parameters obtained from the simulation analysis.

Analysis of artificial neural network performance based on influencing factors for temperature forecasting applications

2020 ◽  
Vol 26 (3) ◽  
pp. 209-223
Author(s):  
M. Madhiarasan ◽  
M. Tipaldi ◽  
P. Siano
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Influencing Factors ◽  
Network Performance ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Research Fields ◽  
Temperature Forecasting ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Artificial neural network (ANN)-based methods belong to one of the most growing research fields within the artificial intelligence ecosystem, and many novel contributions have been developed over the last years. They are applied in many contexts, although some “influencing factors” such as the number of neurons, the number of hidden layers, and the learning rate can impact the performance of the resulting artificial neural network-based applications. This paper provides a deep analysis about artificial neural network performance based on such factors for real-world temperature forecasting applications. An improved back propagation algorithm for such applications is also presented. By using the results of this paper, researchers and practitioners can analyse the encountered issues when applying ANN-based models for their own specific applications with the aim of achieving better performance indexes.

scholarly journals Processing Efficiency, Simulation and Enzyme Activities Analysis of an Air-Lift Multilevel Circulation Membrane Bioreactor (AMCMBR) on Marine Domestic Sewage Treatment

2019 ◽  
Author(s):  
Yuhang Cai ◽  
Xin Li ◽  
Asad A. Zaidi ◽  
Yue Shi ◽  
Kun Zhang ◽  
...  
Keyword(s):  
Ph Value ◽  
Sewage Treatment ◽  
Back Propagation ◽  
Ecological Factors ◽  
Domestic Sewage ◽  
Reactor Performance ◽  
Processing Efficiency ◽  
Emission Standard ◽  
Artificial Neural Network Ann ◽  
Air Lift

The implementation of latest International Maritime Organization emission standard raised stringent requirements for marine domestic sewage discharge. In this study, an air-lift multilevel circulation membrane reactor (AMCMBR) was operated to analyze effects of various ecological factors on effluent of marine domestic sewage. Back-propagation (BP)-Artificial Neural Network (ANN) was used to simulate effect of each ecological factor on reactor performance. The activities of four enzymes were investigated to reveal microbial activities in reactor. Experimental results indicates that the Hydraulic Retention Time (HRT), Mixed Liquid Suspended Solids (MLSS) and pH value cannot be less than 4 h, 3000 mg/L and 6, respectively to meet the IMO emission standard for effluent COD. A small value of mean square error (0.00147) indicated that BP-ANN can well describe the relationship between operation parameters (influent COD, HRT, MLSS, and pH) and effluent COD. The order of relative importance was pH ≈ MLSS > HRT > influent COD. Polyphenol oxidase and urease can serve as indicating factors for reactor performance, whereas dehydrogenase and nitrate reductase showed less susceptible towards varied influent COD and MLSS.

Simultaneous Quantitative Analysis of Clorsulon and Ivermectin in a Veterinary Formulation by Artificial Neural Network

2008 ◽  
Vol 59 (10) ◽  
Author(s):  
Gozde Pektas ◽  
Erdal Dinc ◽  
Dumitru Baleanu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Back Propagation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Simultaneaous spectrophotometric determination of clorsulon (CLO) and invermectin (IVE) in commercial veterinary formulation was performed by using the artificial neural network (ANN) based on the back propagation algorithm. In order to find the optimal ANN model various topogical networks were tested by using different hidden layers. A logsig input layer, a hidden layer of neurons using the logsig transfer function and an output layer of two neurons with purelin transfer function was found suitable for basic configuration for ANN model. A calibration set consisting of CLO and IVE in calibration set was prepared in the concentration range of 1-23 �g/mL and 1-14 �g/mL, repectively. This calibration set contains 36 different synthetic mixtures. A prediction set was prepared in order to evaluate the recovery of the investigated approach ANN chemometric calibration was applied to the simultaneous analysis of CLO and IVE in compounds in a commercial veterinary formulation. The experimental results indicate that the proposed method is appropriate for the routine quality control of the above mentioned active compounds.

Designing Multilayer Feedforward Neural Networks Using Multi-Verse Optimizer

2017 ◽  
pp. 1076-1093 ◽  
Author(s):  
Mohamed F. Hassanin ◽  
Abdullah M. Shoeb ◽  
Aboul Ella Hassanien
Keyword(s):  
Back Propagation ◽  
Feedforward Neural Networks ◽  
Cuckoo Search ◽  
Evolutionary Strategy ◽  
Heuristic Methods ◽  
Grey Wolf ◽  
Back Propagation Algorithm ◽  
Swarm Optimization ◽  
Ann Models ◽  
Artificial Neural Network Ann

Artificial neural network (ANN) models are involved in many applications because of its great computational capabilities. Training of multi-layer perceptron (MLP) is the most challenging problem during the network preparation. Many techniques have been introduced to alleviate this problem. Back-propagation algorithm is a powerful technique to train multilayer feedforward ANN. However, it suffers from the local minima drawback. Recently, meta-heuristic methods have introduced to train MLP like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Cuckoo Search (CS), Ant Colony Optimizer (ACO), Social Spider Optimization (SSO), Evolutionary Strategy (ES) and Grey Wolf Optimization (GWO). This chapter applied Multi-Verse Optimizer (MVO) for MLP training. Seven datasets are used to show MVO capabilities as a promising trainer for multilayer perceptron. Comparisons with PSO, GA, SSO, ES, ACO and GWO proved that MVO outperforms all these algorithms.

scholarly journals Prediction of Drape Coefficient by Artificial Neural Network

2015 ◽  
Vol 15 (4) ◽  
pp. 266-274 ◽  
Author(s):  
Adel Ghith ◽  
Thouraya Hamdi ◽  
Faten Fayala
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
Different Diameters

Abstract An artificial neural network (ANN) model was developed to predict the drape coefficient (DC). Hanging weight, Sample diameter and the bending rigidities in warp, weft and skew directions are selected as inputs of the ANN model. The ANN developed is a multilayer perceptron using a back-propagation algorithm with one hidden layer. The drape coefficient is measured by a Cusick drape meter. Bending rigidities in different directions were calculated according to the Cantilever method. The DC obtained results show a good correlation between the experimental and the estimated ANN values. The results prove a significant relationship between the ANN inputs and the drape coefficient. The algorithm developed can easily predict the drape coefficient of fabrics at different diameters.

An Improved Artificial Neural Network for Recognition of Beam Pump Dynamometer Card

2012 ◽  
Vol 524-527 ◽  
pp. 1331-1334
Author(s):  
Jun Ni ◽  
Zhan Li Ren ◽  
Guo Qing Han
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Linear Mapping ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Beam Pump ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Dynamometer Card

Beam pump dynamometer card plays an important role in identifying the production state of oil wells. With an ability to reflect any non-linear mapping relationship, the artificial neural network (ANN) can be used in pattern recognition. This paper illuminates ANN realization in identifying fault kinds of dynamometer cards, including a back-propagation algorithm, characteristics of the Dynamometer card and some examples. It is concluded that the buildup of a neural network and the abstract of dynamometer cards are important to successful application.

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