hosein amirabadi

The abrasive flow machining technique uses a self-deforming tool, an abrasive laden media that is passed back and forth in the passage geometry of the hollow workpiece with the assistance of two hydraulically operated cylinders placed opposite to each other. The material is removed by abrasion generating finer surfaces in the area where flow is restricted. But this method has a low material removal rate. As the time advances, various variants of AFM have been developed by different researchers to increase the productivity and improve the surface finish. Thus a combination of AFM and its process variants were developed to increase material removal rate and surface finish. This article provides a comprehensive review of recent developments in the process variants of AFM and the respective media.

The most prominent feature of sheet material forming process is an elastic recovery phenomenon during unloading which leads to springback and side wall curl. Therefore, evaluation of springback and side wall curl is mandatory for production of precise products. In this research , the effects of friction coefficient, sheet thickness, yield strength of sheet and blank holder force on the springback and side wall curl in U bending of DP600 dual phase steel sheets were investigated. These investigations were done by computer simulation. The simulations were done by ABAQUS finite element software and then, results were compared with experimental results. The finite element results have been validated with experimental results. MINITAB, a statistical software, was used to analyze finite element results. With the use of MINITAB, equations were obtained to predict springback and side wall curl radius by friction coefficient , sheet thickness , yield strength and blank holder force.

High pressure hybrid jet assisted machining is an efficient method to improve the machining conditions. The significant advantage of this method is concentration of high pressure fluid on machining zone and reduction in tool-chip interface. In this research, a collection of pumps and water jet pump was used in order to supply a high pressure jet. A specific tool holder was designed and manufactured. Experiments were designed to investigate the process parameters such as jet pressure, cutting speed, feed rate and depth of cut. During the experiments, the cutting forces and surface roughness were measured. Results showed the existence of the optimum conditions of the minimum cutting force and surface roughness in hybrid jet assisted machining. Thus, the optimization of process parameters is necessary in order to use hybrid machining more efficiently. A neural network with suitable topology was utilized which was trained by genetic algorithm to obtain the predictive model. Ultimately, genetic algorithm was applied to optimize process parameters. Results showed the ability of employed algorithm to predict the optimum parameters with considerable accuracy.