Artificial Neural Networks Modeling of Surface Finish in Electro-Discharge Machining of Tool Steels

2006 ◽  
Author(s):  
A. Markopoulos ◽  
N. M. Vaxevanidis ◽  
G. Petropoulos ◽  
D. E. Manolakos
Keyword(s):  
Neural Networks ◽  
Surface Roughness ◽  
Artificial Neural Networks ◽  
Maximum Height ◽  
Tool Steels ◽  
Electro Discharge Machining ◽  
Marquardt Algorithm ◽  
Operational Variables ◽  
Artificial Neural ◽  
Pulse On Time

Electro-Discharge machining (EDM) is a thermal process with a complex metal removal mechanism that involves the formation of a plasma channel between the tool and the workpiece electrodes and the melting and evaporation of material resulted thus in the generation of a rough surface consisting of a large number of randomly overlapping craters and no preferential direction. EDM is considered especially suitable for machining complex contours, with high accuracy and for materials that are not amenable to conventional removal methods. However, certain phenomena negatively affecting the surface integrity of EDMed workpieces, constrain the expanded application of the technology. Accordingly, it has been difficult to establish models that correlate accurately the operational variables and the performance towards the optimization of the process. In recent years, artificial neural networks (ANN) have emerged as a novel modeling technique that is able to provide reliable results and it can be integrated into a great number of technological areas including various aspects of manufacturing. In this paper ANN models for the prediction of the surface roughness of electro-discharge machined surfaces are presented. A feed-forward artificial ANN trained with the Levenberg-Marquardt algorithm was finally selected. The proposed neural network takes into consideration the pulse current and the pulse-on time as EDM process variables, for three different tool steels in order to determine the center-line average (Ra) and the maximum height of the profile (Rt) surface roughness parameters.

scholarly journals Properties of the Surface Layer After Trochoidal Milling and Brushing: Experimental Study and Artificial Neural Network Simulation

2019 ◽  
Vol 10 (1) ◽  
pp. 75
Author(s):  
Monika Kulisz ◽  
Ireneusz Zagórski ◽  
Jakub Matuszak ◽  
Mariusz Kłonica
Keyword(s):  
Neural Networks ◽  
Surface Roughness ◽  
Artificial Neural Networks ◽  
Cutting Speed ◽  
Mg Alloy ◽  
Polar Component ◽  
Maximum Height ◽  
Roughness Parameters ◽  
Surface Geometry ◽  
Artificial Neural

The aim of this study was to investigate the effect of milling and brushing cutting data settings on the surface geometry and energy parameters of two Mg alloy substrates: AZ91D and AZ31. In milling, the cutting speed and the trochoidal step were modified (vc = 400–1200 m/min and str = 5–30%) to investigate how they affect selected 2D (Rz, Rku, Rsk, RSm, Ra) and 3D (Sa, Sz, Sku, Ssk) roughness parameters. The brushing treatment was carried out at constant parameters: n = 5000 rev/min, vf = 300 mm/min, ap = 0.5 mm. The surface roughness of specimens was assessed with the Ra, Rz, and RSm parameters. The effects of the two treatments on the workpiece surface were analyzed comparatively. It was found that the roughness properties of the machined surface may be improved by the application of a carbide milling cutter and ceramic brush. The use of different machining data was also shown to impact the surface free energy and its polar component of Mg alloy specimens. Complementary to the results from the experimental part of the study, the investigated machining processes were modelled by means of statistical artificial neural networks (the radial basis function and multi-layered perceptron). The artificial neural networks (ANNs) were shown to perform well as a tool for the prediction of Mg alloy surface roughness parameters and the maximum height of the profile (Rz) after milling and brushing.

Ozone Concentration Prediction using Artificial Neural Networks

2017 ◽  
Vol 68 (10) ◽  
pp. 2224-2227 ◽  
Author(s):  
Camelia Gavrila
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ozone Concentration ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Time Data ◽  
Marquardt Algorithm ◽  
Sensitivity Tests ◽  
Artificial Neural ◽  
The Relationship

The aim of this paper is to determine a mathematical model which establishes the relationship between ozone levels together with other meteorological data and air quality. The model is valid for any season and for any area and is based on real-time data measured in Bucharest and its surroundings. This study is based on research using artificial neural networks to model nonlinear relationships between the concentration of immission of ozone and the meteorological factors: relative humidity (RH), global solar radiation (SR), air temperature (TEMP). The ozone concentration depends on following primary pollutants: nitrogen oxides (NO, NO2), carbon monoxide (CO). To achieve this, the Levenberg-Marquardt algorithm was implemented in Scilab, a numerical computation software. Performed sensitivity tests proved the robustness of the model and its applicability in predicting the ozone on short-term.

scholarly journals Applicationof Computer Visionfor Polishing RobotinAutomotive Manufacturing Industries

2014 ◽  
Vol 2 (2) ◽  
Author(s):  
Adnan Rachmat Anom Besari ◽  
Ruzaidi Zamri ◽  
Md. Dan Md. Palil ◽  
Anton Satria Prabuwono
Keyword(s):  
Neural Networks ◽  
Surface Roughness ◽  
Artificial Neural Networks ◽  
Surface Defects ◽  
Vision System ◽  
Real Data ◽  
Surface Measurement ◽  
Vision Sensor ◽  
Skilled Workers ◽  
Artificial Neural

Polishing is a highly skilled manufacturing process with a lot of constraints and interaction with environment. In general, the purpose of polishing is to get the uniform surface roughness distributed evenly throughout part’s surface. In order to reduce the polishing time and cope with the shortage of skilled workers, robotic polishing technology has been investigated. This paper studies about vision system to measure surface defects that have been characterized to some level of surface roughness. The surface defects data have learned using artificial neural networks to give a decision in order to move the actuator of arm robot. Force and rotation time have chosen as output parameters of artificial neural networks. Results shows that although there is a considerable change in both parameter values acquired from vision data compared to real data, it is still possible to obtain surface defects characterization using vision sensor to a certain limit of accuracy. The overall results of this research would encourage further developments in this area to achieve robust computer vision based surface measurement systems for industrial robotic, especially in polishing process.Keywords: polishing robot, vision sensor, surface defects, and artificial neural networks

scholarly journals Prediction of Carcass Meat Percentage in Young Pigs Using Linear Regression Models and Artificial Neural Networks

2016 ◽  
Vol 16 (1) ◽  
pp. 275-286 ◽  
Author(s):  
Magdalena Szyndler-Nędza ◽  
Robert Eckert ◽  
Tadeusz Blicharski ◽  
Mirosław Tyra ◽  
Artur Prokowski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Performance Testing ◽  
Regression Equations ◽  
Young Pigs ◽  
Marquardt Algorithm ◽  
Artificial Neural

Abstract One of the approaches to improving performance testing of pigs is to look for mathematical solutions to increase the accuracy of calculations. This is mainly done through improvement of linear regression equations based on current data on performance tested pigs in Poland. The advances in computer technology and the improvements in mathematical analysis have made it possible to use artificial neural networks (ANNs) for prediction of carcass meat percentage in young pigs. The aim of the study was to compare the potential for live estimation of carcass meat percentage in pigs using two computational methods: linear regression equations and ANNs. The experiment used 654 gilts of six breeds, which were subjected to performance testing and slaughter analysis at the Pig Performance Testing Station (SKURTCh). The collected data were used to train ANNs to estimate carcass meat percentage in young pigs. Training was performed using the Levenberg- Marquardt algorithm. Next, meatiness estimated by ANNs was compared with the results obtained using linear modelling. It is concluded that based on the fattening and slaughter performance test results of live pigs, artificial neural networks (SSN23) are significantly more accurate in estimating carcass meat percentage in young pigs compared to the three-variable linear regression model 1. The difference in meatiness estimation between SSN23 and the four-variable linear regression model 2 was statistically non-significant in most of the breeds except Duroc and Pietrain, where the meatiness of young animals was estimated more accurately by the linear regression model.

scholarly journals The use of artificial intelligence to model the geometric state of the surface after EDM machining of tool steels

2018 ◽  
Vol 19 (12) ◽  
pp. 570-574
Author(s):  
Halina Nieciąg ◽  
Rafał Kudelski ◽  
Krzysztof Zagórski
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Tool Steel ◽  
Learning Algorithms ◽  
Tool Steels ◽  
Geometrical Features ◽  
Artificial Neural ◽  
Edm Process ◽  
Geometrical State

The article presents the application of artificial intelligence methods in the form of artificial neural networks (SSN) for modeling the geometrical state of a product shaped in the EDM process. The SSN with different architecture and different learning algorithms were implemented. The models' quality and their effectiveness in predicting some geometrical features of tool steel products were examined.

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