Surface roughness analysis and optimization for the CNC milling process by the desirability function combined with the response surface methodology

2015 ◽  
Vol 57 (1) ◽  
pp. 64-71 ◽  
Author(s):  
Ugur Esme
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Desirability Function ◽  
Milling Process ◽  
Cnc Milling ◽  
Roughness Analysis

Analysis and Optimization of Surface Roughness and Microhardness for Roller Burnishing using Response Surface Methodology and Desirability Function Approach on Aluminum 63400

2019 ◽  
Vol 18 (03) ◽  
pp. 363-378
Author(s):  
Vijay Kurkute ◽  
Sandip Chavan
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Aluminum Alloy ◽  
Response Surface ◽  
Desirability Function ◽  
Roller Burnishing ◽  
Significant Control ◽  
Control Factors ◽  
Feed Force ◽  
Burnishing Process

In the present study, response surface methodology (RSM) has been used to optimize roller burnishing process for aluminum alloy 63400 grade. Single roller burnishing tool (carbide) is used to burnish round aluminum alloy. Experiments were performed with Box and Wilson Central Composite Design (CCD). The machining factors controlled during experimentation are speed, feed, force and number of tool passes. The response parameters are surface roughness and microhardness. The most significant control factors on the surface roughness and microhardness were determined by analysis of variance (ANOVA). A controllable process parameter is correlated with surface roughness and microhardness by mathematical model. A quadratic regression analysis is performed to compute the correlation coefficient between the experimental and predicted values. The optimum surface roughness and microhardness foreseen by the model is found to agree well with the results of the experiment. To find the optimum value of both the response, desirability approach was used. The input parameters with most desirability value are selected as the optimum solution. Hence, the most desirable burnished condition desirability value 0.872 is speed 37.9[Formula: see text]m/min, feed 0.5[Formula: see text]mm/rev, force 35.49[Formula: see text]N and number of tool passes four. Surface roughness obtained is 0.524[Formula: see text][Formula: see text] m and microhardness is 125.02[Formula: see text]HV. This is the optimum condition for minimum surface roughness and maximum microhardness. The optimum surface finish and microhardness predicted by the model are found to agree well with the results of the experiment.

Investigation of the Surface Quality when End–Ball–Milling Form Surfaces with Sequence of the Radii Ratio

2014 ◽  
Vol 474 ◽  
pp. 224-229 ◽  
Author(s):  
Jozef Beňo ◽  
Jozef Stahovec ◽  
Marek Vrabel'
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Statistical Analysis ◽  
Response Surface ◽  
Ball Milling ◽  
Surface Quality ◽  
Milling Process ◽  
Real Form ◽  
Cusp Formation

This paper deals with a methodology of identifying surface quality when endball milling form surfaces which are applied in tool making. This methodology was developed for the 3D milling process and it consists of following parts as design of the samples, surface milling and checking surfaces with different sequence of radii ratio. That sequence was used to model real form surfaces applied in tool making. Based on the defined measurability of the surface roughness, two milling strategies have been applied to produce radii sequence. In order to prevent undesired cusp formation in the 3D milling, the response surface methodology and statistical analysis are used to evaluate surface quality.

scholarly journals Determination of the Optimal Neural Network Transfer Function for Response Surface Methodology and Robust Design

2021 ◽  
Vol 11 (15) ◽  
pp. 6768
Author(s):  
Tuan-Ho Le ◽  
Hyeonae Jang ◽  
Sangmun Shin
Keyword(s):  
Response Surface Methodology ◽  
Transfer Function ◽  
Response Surface ◽  
Robust Design ◽  
Transfer Functions ◽  
Desirability Function ◽  
Likelihood Estimation ◽  
Statistical Simulation ◽  
Screening Procedure ◽  
Function Estimation

Response surface methodology (RSM) has been widely recognized as an essential estimation tool in many robust design studies investigating the second-order polynomial functional relationship between the responses of interest and their associated input variables. However, there is scope for improvement in the flexibility of estimation models and the accuracy of their results. Although many NN-based estimations and optimization approaches have been reported in the literature, a closed functional form is not readily available. To address this limitation, a maximum-likelihood estimation approach for an NN-based response function estimation (NRFE) is used to obtain the functional forms of the process mean and standard deviation. While the estimation results of most existing NN-based approaches depend primarily on their transfer functions, this approach often requires a screening procedure for various transfer functions. In this study, the proposed NRFE identifies a new screening procedure to obtain the best transfer function in an NN structure using a desirability function family while determining its associated weight parameters. A statistical simulation was performed to evaluate the efficiency of the proposed NRFE method. In this particular simulation, the proposed NRFE method provided significantly better results than conventional RSM. Finally, a numerical example is used for validating the proposed method.

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