Prediction of Process Parameters That Affecting on Surface Roughness in Multi-Point Forming Process Using ANOVA Algorithm

• Main Authors: Tahseen Fadhil Abbas, Karem Mohsen Younis, Khalida kadhim Mansor
• Format: Article
• Language: English
• Published: Al-Khwarizmi College of Engineering – University of Baghdad 2019-05-01
• Series: Al-Khawarizmi Engineering Journal
• Online Access: http://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/547

  Multipoint forming process is an engineering concept which means that the working surface of the punch and die is produced as hemispherical ends of individual active elements (called pins), where each pin can be independently, vertically displaced using a geometrically reconfigurable die. Several different products can be made without changing tools saved precious production time. Also, the manufacturing of very expensive rigid dies is reduced, and a lot of expenses are saved. But the most important aspects of using such types of equipment are the flexibility of the tooling. This paper presents an experimental investigation of the effect of three main parameters which are blank holder, rubber thickness and forming speed that affect the surface integrity for brass (Cu Zn 65-35) with 0.71 mm thickness. This paper focuses on the development of prediction models for estimation of the product quality. Using Analysis of Variance (ANOVA), surface roughness has been modeled. In the development of this predictive model, blank holder, rubber thickness and forming speed have been considered as model parameters. The mean surface roughness (Ra) is used as response parameter to predict the surface roughness of multipoint forming parts. The data required has been generated, compared and evaluated to the proposed models obtained from experiments. Taguchi algorithm was used to predict the forming parameters (blank holder, rubber thickness and forming speed) on product roughness in forming process of Brass (Cu Zn 65-35) based on orthogonal array of L9 and finally ANOVA was used to find the optimum parameters that have effect on the product quality.