Statistical Analysis and Optimization of Variables Affecting the End Diameter of AISI 304 Steel Tube Produced by Flaring Process
Author(s):
Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:
The flaring of the ends of thin-walled tubes is a subset of single-point incremental forming. In this research, the experimental study and statistical analysis of the variables affecting the end diameter of formed tubes were considered. In the present paper, the design of the experiment was done based on the response surface methodology. In order to form the end of the AISI 304 steel tube and perform the statistical analysis, process input variables including tool diameter, tool angular step, tool vertical step, and type of lubricant were selected. Then the effect of input variables on the tube end diameter was analyzed. Also, the tube end diameter function was extracted from the input variables in terms of linear, interactive, and quadratic expressions, and its competency and adequacy were confirmed. The analysis of variance results showed that the expressions of the tool vertical step, tool diameter, and the interactive effect of the product of tool diameter and tool angular step are the most effective expressions on the tube end diameter. Finally, the optimal combination of process input variables to achieve the maximum tube end diameter was determined using the desirability method, and by running the verification test, the correctness of the regression equation to predict the tube end diameter was confirmed.
Keywords:
Language:
Persian
Published:
Amirkabir Journal Mechanical Engineering, Volume:54 Issue: 12, 2023
Pages:
2861 to 2876
https://www.magiran.com/p2724315
سامانه نویسندگان
اطلاعات نویسنده(گان) توسط ایشان ثبت و تکمیل شدهاست. برای مشاهده مشخصات و فهرست همه مطالب، صفحه رزومه را ببینید.
مقالات دیگری از این نویسنده (گان)
-
Statistical Modeling and Optimization of Variables Affecting Surface Hardness and Corrosion Resistance of 316L Stainless Steel in Ultrasonic Shot Peening Process Using Desirability Approach
A. Omidi Hashjin, M. Vahdati *, R. Abedini
Iranian Journal of Materials Forming, Winter 2025 -
Optimization of multilayer perceptron neural network structure for simulating the effect of input variables on the spring-back phenomenon in the ultrasonic vibration assisted single point incremental forming
*, Seyyed Mojtaba Varedi-Koulaei
Journal of Mechanical Engineering, Autumn 2024