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.

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 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 Modeling Cutting Forces in High-Speed Turning using Artificial Neural Networks

TecnoLógicas ◽  
2021 ◽  
Vol 24 (51) ◽  
pp. e1671
Author(s):  
Luis W. Hernández-González ◽  
Dagnier A. Curra-Sosa ◽  
Roberto Pérez-Rodríguez ◽  
Patricia D.C. Zambrano-Robledo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Consumption ◽  
Cutting Forces ◽  
High Speed ◽  
Cutting Tool ◽  
Cutting Speed ◽  
Machining Time ◽  
High Speed Turning ◽  
Artificial Neural

Cutting forces are very important variables in machining performance because they affect surface roughness, cutting tool life, and energy consumption. Reducing electrical energy consumption in manufacturing processes not only provides economic benefits to manufacturers but also improves their environmental performance. Many factors, such as cutting tool material, cutting speed, and machining time, have an impact on cutting forces and energy consumption. Recently, many studies have investigated the energy consumption of machine tools; however, only a few have examined high-speed turning of plain carbon steel. This paper seeks to analyze the effects of cutting tool materials and cutting speed on cutting forces and Specific Energy Consumption (SEC) during dry high-speed turning of AISI 1045 steel. For this purpose, cutting forces were experimentally measured and compared with estimates of predictive models developed using polynomial regression and artificial neural networks. The resulting models were evaluated based on two performance metrics: coefficient of determination and root mean square error. According to the results, the polynomial models did not reach 70 % in the representation of the variability of the data. The cutting speed and machining time associated with the highest and lowest SEC of CT5015-P10 and GC4225-P25 inserts were calculated. The lowest SEC values of these cutting tools were obtained at a medium cutting speed. Also, the SEC of the GC4225 insert was found to be higher than that of the CT5015 tool.

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