scholarly journals Stiffness-Based Cell Setup Optimization for Robotic Deburring with a Rotary Table

2021 ◽  
Vol 11 (17) ◽  
pp. 8213
Author(s):  
Janez Gotlih ◽  
Miran Brezocnik ◽  
Timi Karner
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Industrial Robot ◽  
Local Minimizer ◽  
Rotary Table ◽  
A Cell ◽  
Low Stiffness ◽  
Artificial Neural Network Ann ◽  
Robotic Automation

Deburring is recognized as an ideal technology for robotic automation. However, since the low stiffness of the robot can affect the deburring quality and the performance of an industrial robot is generally inhomogeneous over its workspace, a cell setup must be found that allows the robot to track the toolpath with the desired performance. In this work, the problems of robotic deburring are addressed by integrating components commonly used in the machining industry. A rotary table is integrated with the robotic deburring cell to increase the effective reach of the robot and enable it to machine a large workpiece. A genetic algorithm (GA) is used to optimize the placement of the workpiece based on the stiffness of the robot, and a local minimizer is used to maximize the stiffness of the robot along the deburring toolpath. During cutting motions, small table rotations are allowed so that the robot maintains high stiffness, and during non-cutting motions, large table rotations are allowed to reposition the workpiece. The stiffness of the robot is modeled by an artificial neural network (ANN). The results confirm the need to optimize the cell setup, since many optimizers cannot track the toolpath, while for the successful optimizers, a performance imbalance occurs along the toolpath.

Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Sandip K Lahiri ◽  
Kartik Chandra Ghanta
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Misclassification Error ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Pipeline Design ◽  
Hybrid Artificial Neural Network

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.

Application of Neural Network Based on Genetic Algorithm to Predict Hypotension Events during Spinal Anesthesia

2012 ◽  
Vol 542-543 ◽  
pp. 1394-1397
Author(s):  
Yu Qiu ◽  
Hai Jun Dai
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Global Optimization ◽  
General Anesthesia ◽  
Bp Neural Network ◽  
Simulation Experiment ◽  
Optimal Parameters ◽  
Accuracy Requirement ◽  
Artificial Neural Network Ann

An accurate and efficient artificial neural network (ANN) based genetic algorithm (GA) is presented for predicting hypotension during general anesthesia. The genetic algorithm global optimization characteristics are used to optimize the BP neural network weights, and learning samples are trained and modeled by BP neural network with optimal parameters. The simulation experiment is carried out with MATLAB. The result indicated that the model forecasting results are close with the actual results and meet the accuracy requirement to General Anesthesia.

Optimization Waiting Time at Berthing Area of Port Container Terminal with Hybrid Genetic Algorithm (GA) and Artificial Neural Network (ANN)

2014 ◽  
Vol 902 ◽  
pp. 431-436 ◽  
Author(s):  
A. Shahpanah ◽  
S. Poursafary ◽  
S. Shariatmadari ◽  
A. Gholamkhasi ◽  
S.M. Zahraee
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Waiting Time ◽  
Container Terminal ◽  
Hybrid Approach ◽  
Simulation Software ◽  
Optimum Number ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

A queuing network model related to arrival, departure and berthing process of ships at port container terminal is presented in this paper. The important datas collected from PTP port container terminal located at Malaysia. Based on the case study the model was built with using Arena 13.5 simulation software. Especially this study proposes a hybrid approach consisting of Genetic algorithm (GA), Artificial Neural Network (ANN) to find the the optimum number of equipments at berthing area of port container terminal. The input data that used in ANN obtained from Arena results. The main goal of this study is reduced waiting time of each ship at port container terminal, and Based on the result the optimum waiting time 50 will be achieved.

scholarly journals Mitigating Economic Denial of Sustainability (EDoS) in Cloud Environment using Genetic Algorithm and Artificial Neural Network

2019 ◽  
Vol 8 (10) ◽  
pp. 3415-3421
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Virtual Machines ◽  
Fitness Function ◽  
Medium Size ◽  
Cloud Environment ◽  
Edos Attacks ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Economic Denial of Sustainability (EDoS) is a latest threat in the cloud environment in which EDoS attackers continually request huge number of resources that includes virtual machines, virtual security devices, virtual networking devices, databases and so on to slowly exploit illegal traffic to trigger cloud-based scaling capabilities. As a result, the targeted cloud ends with a consumer bill that could lead to bankruptcy. This paper proposes an intelligent reactive approach that utilizes Genetic Algorithm and Artificial Neural Network (GANN) for classification of cloud server consumer to minimize the effect of EDoS attacks and will be beneficial to small and medium size organizations. EDoS attack encounters the illegal traffic so the work is progressed into two phases: Artificial Neural Network (ANN) is used to determine affected path and to detect suspected service provider out of the detected affected route which further consist of training and testing phase. The properties of every server are optimized by using an appropriate fitness function of Genetic Algorithm (GA) based on energy consumption of server. ANN considered these properties to train the system to distinguish between the genuine overwhelmed server and EDoS attack affected server. The experimental results show that the proposed Genetic and Artificial Neural Network (GANN) algorithm performs better compared to existing Fuzzy Entropy and Lion Neural Learner (FLNL) technique with values of precision, recall and f-measure are increased by 3.37%, 10.26% and 6.93% respectively.

scholarly journals Application of Artificial Neural Network and Genetic Algorithm to Healthcarewaste Prediction

2013 ◽  
Vol 3 (4) ◽  
pp. 243-250 ◽  
Author(s):  
Samira Arabgol ◽  
Hoo Sang Ko
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Prediction Accuracy ◽  
Negative Impact ◽  
Healthcare Waste ◽  
Mean Squared Errors ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract Prompt and proper management of healthcare waste is critical to minimize the negative impact on the environment. Improving the prediction accuracy of the healthcare waste generated in hospitals is essential and advantageous in effective waste management. This study aims at developing a model to predict the amount of healthcare waste. For this purpose, three models based on artificial neural network (ANN), multiple linear regression (MLR), and combination of ANN and genetic algorithm (ANN-GA) are applied to predict the waste of 50 hospitals in Iran. In order to improve the performance of ANN for prediction, GA is applied to find the optimal initial weights in the ANN. The performance of the three models is evaluated by mean squared errors. The obtained results have shown that GA has significant impact on optimizing initial weights and improving the performance of ANN.

scholarly journals Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Montri Inthachot ◽  
Veera Boonjing ◽  
Sarun Intakosum
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Stock Price ◽  
Future Trend ◽  
Past Time ◽  
Hybrid Intelligence ◽  
Artificial Neural ◽  
Input Variables ◽  
Artificial Neural Network Ann

This study investigated the use of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction of Thailand’s SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid’s prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span.

Sign in / Sign up

Export Citation Format

Share Document