scholarly journals The influence of technological parameters on surface roughness during turning and roughness prediction using artificial neutral networks

Mechanik ◽  
2018 ◽  
Vol 91 (10) ◽  
pp. 898-900 ◽  
Author(s):  
Ireneusz Zagórski ◽  
Monika Kulisz ◽  
Tomasz Warda
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Aluminum Alloy ◽  
Roughness Parameter ◽  
Cutting Speed ◽  
Roughness Parameters ◽  
Technological Parameters ◽  
Surface Roughness Parameters ◽  
Roughness Prediction

The purpose of this investigation was to determine whether and to what extent the technological parameters of turning (feed, cutting speed) affect selected surface roughness parameters of aluminum alloy EN-AW 7075 (AlZn5.5MgCu). The principal findings indicate a significant impact of feed and show on the surface roughness and simultaneously show that cutting speed has no effect on the value of surface roughness parameters under investigation. An artificial neural network was employed to evaluate the prediction of surface roughness parameter Rz in turning.

Considerations Regarding Some Surface State Parameters Characterized by a more Restricted Use

2015 ◽  
Vol 809-810 ◽  
pp. 93-98
Author(s):  
Ionuţ Urzică ◽  
Ciprian Râznic ◽  
Mihai Apostol ◽  
Corina Mihaela Pavăl ◽  
Mihai Boca ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Roughness Parameter ◽  
Cutting Speed ◽  
Roughness Parameters ◽  
Turning Process ◽  
Nose Radius ◽  
Machined Surface ◽  
Surface Roughness Parameter ◽  
Surface Roughness Parameters ◽  
Restricted Use

Frequently, on the drawings of mechanical parts, only indications concerning the surface roughness parameter Ra and, relatively rarely, the surface roughness parameter Rz are included. However, the study of the machined surface roughness highlights the necessity to use yet other surface roughness parameters, in order to have a clearer image on the state of the machined surface. Some other surface roughness parameters possible to be used and presenting importance, without the parameters Ra and Rz, were highlighted. One took into consideration the possibility of measuring parameters Rsk and Rmr by means of the available surface roughness testers. Experimental researches of turning by applying the method of full factorial experiment were developed. As input factors in turning process, the cutting speed, the feed rate and the tool nose radius were used. The experimental results were mathematically processed, being determined empirical mathematical models that highlight the influence of certain input factors of turning process on the values of some surface roughness parameters characterized by a more restricted use

Surface Roughness Prediction in Turning of Free Machining Steel 1215 by Artificial Neural Network

2011 ◽  
Vol 188 ◽  
pp. 535-541
Author(s):  
Xiao Jiang Cai ◽  
Z.Q. Liu ◽  
Q.C. Wang ◽  
Shu Han ◽  
Qing Long An ◽  
...  
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Mean Squared Error ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Ann Model ◽  
Roughness Prediction ◽  
Artificial Neural

Surface roughness is a significant aspect of the surface integrity concept. It is efficient to predict the surface roughness in advance by a prediction model. In this study, artificial neural network is used to model the surface roughness in turning of free machining steel 1215. The inputs considered in the prediction ANN model were cutting speed, feed rate and depth of cut, and the output was Ra. Several feed-forward neural networks with different architectures were compared in terms of prediction accuracy, and then the best prediction model, a 3-4-1-1 ANN was capable of predicting Ra with a mean squared error 5.46%, was presented.

Optimization and Prediction of Machining Responses Using Response Surface Methodology and Adaptive Neural Network by Wire Electric Discharge Machining of Alloy-X

2021 ◽  
Vol 1026 ◽  
pp. 28-38
Author(s):  
I. Vishal Manoj ◽  
S. Narendranath ◽  
Alokesh Pramanik
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Response Surface ◽  
Electric Discharge ◽  
Cutting Speed ◽  
Electric Discharge Machining ◽  
Surface Method ◽  
Wire Electric Discharge Machining ◽  
Pulse On Time

Wire electric discharge machining non-contact machining process based on spark erosion technique. It can machine difficult-to-cut materials with excellent precision. In this paper Alloy-X, a nickel-based superalloy was machined at different machining parameters. Input parameters like pulse on time, pulse off time, servo voltage and wire feed were employed for the machining. Response parameters like cutting speed and surface roughness were analyzed from the L25 orthogonal experiments. It was noted that the pulse on time and servo voltage were the most influential parameters. Both cutting speed and surface roughness increased on increase in pulse on time and decrease in servo voltage. Grey relation analysis was performed to get the optimal parametric setting. Response surface method and artificial neural network predictors were used in the prediction of cutting speed and surface roughness. It was found that among the two predictors artificial neural network was accurate than response surface method.

Evaluation of Process Parameters in Machining of AA6063 Alloy

2018 ◽  
Vol 1148 ◽  
pp. 109-114
Author(s):  
M. Balaji ◽  
C.H. Nagaraju ◽  
V.U.S. Vara Prasad ◽  
R. Kalyani ◽  
B. Avinash
Keyword(s):  
Surface Roughness ◽  
Regression Analysis ◽  
Process Parameters ◽  
Single Channel ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Roughness Parameters ◽  
Surface Roughness Parameters ◽  
Aa6063 Alloy

The main aim of this work is to analyse the significance of cutting parameters on surface roughness and spindle vibrations while machining the AA6063 alloy. The turning experiments were carried out on a CNC lathe with a constant spindle speed of 1000rpm using carbide tool inserts coated with Tic. The cutting speed, feed rate and depth of cut are chosen as process parameters whose values are varied in between 73.51m/min to 94.24m/min, 0.02 to 0.04 mm/rev and 0.25 to 0.45 mm respectively. For each experiment, the surface roughness parameters and the amplitude plots have been noted for analysis. The output data include surface roughness parameters (Ra,Rq,Rz) measured using Talysurf and vibration parameter as vibration amplitude (mm/sec) at the front end of the spindle in transverse direction using single channel spectrum analyzer (FFT).With the collected data Regression analysis is also performed for finding the optimum parameters. The results show that significant variation of surface irregularities and vibration amplitudes were observed with cutting speed and feed. The optimum cutting speed and feed from the regression analysis were 77.0697m/min and 0.0253mm/rev. for the minimum output parameters. No significant effect of depth of cut on output parameters is identified.

EVALUATION OF SURFACE ROUGHNESS PRODUCED BY NANOCUTTING COPPER STRUCTURE USING A LEAST SQUARE MEAN METHOD

2019 ◽  
Vol 27 (01) ◽  
pp. 1950081 ◽  
Author(s):  
CHUNHUI JI ◽  
SHUANGQIU SUN ◽  
BIN LIN ◽  
TIANYI SUI
Keyword(s):  
Surface Roughness ◽  
Cutting Speed ◽  
Least Square ◽  
Depth Of Cut ◽  
Real Surface ◽  
Roughness Parameters ◽  
Machined Surface ◽  
Surface Roughness Parameters ◽  
3D Surface ◽  
3D Surface Roughness

This work performed molecular dynamic simulations to study the 2D profile and 3D surface topography in the nanometric cutting process. The least square mean method was used to model the evaluation criteria for the surface roughness at the nanometric scale. The result showed that the cutting speed was the most important factor influencing the spacing between the peaks, the sharpness of the peaks, and the randomness of the profile. The plastic deformation degree of the machined surface at the nanometric scale was significantly influenced by the cutting speed and depth of cut. The 2D and 3D surface roughness parameters exhibited a similar variation tendency, and the parameters Ra and Rq tended to increase gradually with an increase in the cutting speed and a decrease in the depth of cut. Finally, it is concluded that at the nanometric scale, the 3D surface roughness parameters could more accurately reflect the real surface characteristics than the 2D parameters.

The Prediction of Shot Peening’s Surface Roughness with Premixed Water Jet Based on Neural Network

2010 ◽  
Vol 136 ◽  
pp. 172-175
Author(s):  
Rui Hong Wang ◽  
Chong Wang ◽  
Xiao Mei Zhang
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Surface Roughness ◽  
Aluminum Alloy ◽  
Prediction Accuracy ◽  
Scanning Speed ◽  
Water Jet ◽  
Average Error ◽  
Affecting Factors ◽  
Roughness Prediction

Shot peening’s surface roughness is an important factor affecting the effect of shot-peening. The paper selects blasting pressure, scanning speed and target distance as affecting factors in the process parameters, the shot penning test which aims at 2A11 aluminum alloy materials through applying the premixed water jet, according to test data, the paper establishes mathematical model of shot peening’s surface roughness applying neural network, and applies this model to predict shot peening’s surface roughness. The results show that the training average error of this model is small, the predicted effect is good, it can meet the requirements of shot peening’s surface roughness prediction accuracy in the industrial production, it has greater practical value.

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