Title of article
Statistical Analysis and Optimization of Factors Affecting the Surface Roughness in the UVaSPIF Process Using Response Surface Methodology
Author/Authors
Vahdati، Mehdi نويسنده Department of Mechanical Engineering, University of Tehran, Tehran, Iran , , Mahdavinejad، Ramezanali نويسنده School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran Mahdavinejad, Ramezanali , Amini، Saeid نويسنده Department of Mechanical Engineering, University of Kashan, Kashan, Iran , , Moradi، Mahmoud Reza نويسنده ,
Issue Information
فصلنامه با شماره پیاپی 9 سال 2015
Pages
14
From page
15
To page
28
Abstract
Ultrasonic vibration assisted single point incremental forming (UVaSPIF) is based on localized plastic deformation in a sheet metal blank. It consists to deform gradually and locally the sheet metal using vibrating hemispherical-head tool controlled by a CNC milling machine. The ultrasonic excitation of forming tool reduces the vertical component of forming force. In addition, application of ultrasonic vibration reduces the surface roughness of the specimen. Surface roughness is one of the quantitative and qualitative parameters, which is used to assess the quality of the final product. In the present paper, a statistical analysis and optimization of effective factors on this parameter is performed in the UVaSPIF. For this purpose, response surface methodology (RSM) is selected as the experiment design technique. The controllable factors such as vertical step size, sheet thickness, tool diameter, wall inclination angle, and feed rate is specified as input variables of the process. The obtained results from analysis of variance (ANOVA) and regression analysis of experimental data confirm the accuracy of mathematical model. Furthermore, it is shown that the linear, quadratic, and interactional terms of the variables are effective on the surface roughness parameter. To optimize the surface roughness parameter, the finest conditions of the experiment are determined using desirability method, and statistical optimization is subsequently verified by conducting the confirmation test.
Journal title
Journal of Advanced Materials and Processing
Serial Year
2015
Journal title
Journal of Advanced Materials and Processing
Record number
2143431
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