Composite Materials
S. Khalilpourazary; N. Payam
Abstract
Warpage and shrinkage control are important factors in proving the quality of thin-wall parts in injection modeling process. In the present paper, grey relational analysis was used in order to optimize these two parameters in manufacturing plastic bush of articulated garden tractor. The material used ...
Read More
Warpage and shrinkage control are important factors in proving the quality of thin-wall parts in injection modeling process. In the present paper, grey relational analysis was used in order to optimize these two parameters in manufacturing plastic bush of articulated garden tractor. The material used in the plastic bush is Derlin 500. The input parameters in the process were selected according to their effect on shrinkage and warpage values, melt temperature, mold temperature, injection rate, injection pressure, and packing pressure. Then, the Taguchi method was applied to design the experiments, and through the use of Mold Flow software injection molding process was simulated based on these experiments and the input parameters. Based on the results obtained from the simulation, the input parameters were analyzed in three levels using grey relational analysis. Then, analysis of variance and confirmation tests were carried out on the output of grey relational analysis to predict the optimum values of the input parameters and to calculate the dimensional changes of the plastic bush. Gaining these values, the plastic bush sample was manufactured, and its 3D point cloud model was generated by a scanner. At the end, by generating 3D solid model of the plastic bush its dimensional features were studied. The comparison of the warpage and shrinkage values between grey relational analysis and 3D CAD model indicates the precision of the method in controlling and measuring these two parameters.
Manufacturing Processes
S. Khalilpourazary; P. M. Kashtiban; N. Payam
Abstract
Nowadays, in order to reach minimum production cost in machining operations, various optimization methods have been proposed. Since turning operation has different parameters affecting the workpiece quality, it was selected as a complicated manufacturing method in this paper. To reach sufficient quality, ...
Read More
Nowadays, in order to reach minimum production cost in machining operations, various optimization methods have been proposed. Since turning operation has different parameters affecting the workpiece quality, it was selected as a complicated manufacturing method in this paper. To reach sufficient quality, all influencing parameters such as cutting speed, federate, depth of cut and tool rake angle were selected as input parameters. Furthermore, both surface roughness and tool life were considered as the objectives. Also, ST37 steel and M1 high speed steel (HSS) were selected as workpiece material and tool, respectively. Subsequently, grey relational analysis was performed to elicit optimal values for the mentioned input data. To achieve this goal, first, degree of freedom was calculated for the system and the same experiments were performed based on the target values and number of considered levels, leading to calculating grey relational generating, grey relational coefficient and grey relational grade. As the next step, the grey relational graph was sketched for each level. Finally, optimum values of the parameters were obtained for better surface roughness and tool life. It was shown that the presented method in the turning operation of ST37 led to high surface quality and tool life.
