Some Methods of Research Results Approximation

2015 ◽  
Vol 783 ◽  
pp. 95-103 ◽  
Author(s):  
Tadeusz Mikolajczyk ◽  
Adrian Olaru
Keyword(s):  
Neural Networks ◽  
Surface Roughness ◽  
Regression Analysis ◽  
Cutting Edge ◽  
Research Facility ◽  
Research Results ◽  
Methods Of Approximation

The paper presents compare methods of approximation results of studies using regression analysis and neural networks. As a research facility used theoretical values of surface roughness based on theoretical values of surface roughness calculated as kinematics-geometric projection of the cutting edge on finish surface. Pointed to the limitations of the presented methods of research results approximation.

The Comparison of Surface Roughness Characteristics Achieved by the Machining with Conventional and Unconventional Geometry of Tools

2012 ◽  
Vol 622-623 ◽  
pp. 352-356 ◽  
Author(s):  
Peter Monka
Keyword(s):  
Surface Roughness ◽  
Regression Analysis ◽  
Cutting Tool ◽  
Cutting Speed ◽  
Surface Profile ◽  
Operation Time ◽  
Cutting Edge ◽  
Technological Operation ◽  
Profile Characteristics ◽  
Machining Productivity

The paper deals with the experiments realized by means of cutting tool with linear cutting edge not parallel with the axis of the workpiece in order to be observed the suitable values of surface roughness characteristics in dependency on the feed and cutting speed. During experiments were machined three types of steels. Acquired data were statistical processed by regression analysis. The results of the measurements show that the investigated cutting tool enables to secure the same values of surface profile characteristics of steels as a classical cutting tool at finishing with the significant increase of the feed per revolution. It directly influences length of the technological operation time which is several times shortened and so the machining productivity can increase.

scholarly journals Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Rahel Jedamski ◽  
Jérémy Epp
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Case Hardening ◽  
Artificial Neural ◽  
Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

Prediction of Surface Roughness in Magneto Rheological Abrasive Flow Finishing Process by Artificial Neural Networks and Regression Analysis

2015 ◽  
Vol 766-767 ◽  
pp. 1076-1084
Author(s):  
S. Kathiresan ◽  
K. Hariharan ◽  
B. Mohan
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Regression Analysis ◽  
Back Propagation ◽  
Hydraulic Pressure ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Magneto Rheological ◽  
Hidden Neurons ◽  
Abrasive Flow Finishing

In this study, to predict the surface roughness of stainless steel-304 in Magneto rheological Abrasive flow finishing (MRAFF) process, an artificial neural network (ANN) and regression models have been developed. In this models, the parameters such as hydraulic pressure, current to the electromagnet and number of cycles were taken as variables of the model.Taguchi’s technique has been used for designing the experiments in order to observe the different values of surface roughness . A neural network with feed forward with the help of back propagation was made up of 27 input neurons, 7 hidden neurons and one output neuron. The 6 sets of experiments were randomly selected from orthogonal array for training and residuals were used to analyze the performance. To check the validity of regression model and to determine the significant parameter affecting the surface roughness, Analysis of variance (ANOVA) andF-test were made. The numerical analysis depict that the current to the electromagnet was an paramount parameter on surface roughness.Key words: MRAFF, ANN, Regression analysis

ANALISIS FAKTOR YANG MEMPENGARUHI GAYA KEPEMIMPINAN PERGURUAN TINGGI SWASTA DI KOTA PONTIANAK

2020 ◽  
Vol 5 (1) ◽  
pp. 16-23
Author(s):  
Asti Lestary ◽  
Juliahir Barata
Keyword(s):  
Regression Analysis ◽  
Data Processing ◽  
Leadership Style ◽  
Factors Affecting ◽  
Research Results ◽  
Cultural Organization ◽  
Available Resources

Background this study that of pts 19 who institusinya have accredited, only seven institutions ( about 37% ) have accredited b ( that was good categorry ) wholly are located in the Pontianak.This condition is allegedly affected by several factors and the internal conditions institutions such as leadership style, the quantity and quality of teachers, availability of supporting deliver pengeloaan administration and order and institutions.Refer of a whole factors, the researchers believed that the authority factors affecting the institutions is leadership style. Although other supporting factors available and adequate, but a style of leadership that is not a right actually make available resources be made of no effect. A style of its own leadership determined by a variety of factors influence it. Methods used in this study is the quantitatif approach to technique data processing using regression analysis. The factors causing as variable 1 ) situation; 2) experience and vision of leader: 3) vision (the foundation of education ); 4), communication and 5 ) cultural organization and leadership style as variable bound. The research results show that simultaneously all of these issues having a level the influence of as much as 0,947 ( extremely powerful category with the influence of as much as 89,8 %.

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.

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