Physical modeling of EXTRUSION process has been studied to obtain qualitative and quantitative information. To observe the flow pattern during the process the PLASTICINE specimen was made with layers of different colors (dark and light). The strain distribution was obtained by measuring the thickness of the PLASTICINE layers and considering the axi-symmetric formulations. The stress distribution and subsequently the EXTRUSION load were computed according to the strain distribution. To model the real frictional effect several ring compression tests have been performed. In order to verify the validity of the modeling data, a similarity study between PLASTICINE and Al 2024-T4 was made. The load obtained by this technique was compared with the results of ANSYS modeling and slab method analysis.

An axi-symmetric hot closed die-forging process has been studied by physical modeling technique using the PLASTICINE. To observe the material flow pattern, layers of PLASTICINE with different colors were used. The normal direction to the layers was considered a principal direction. The strain distribution was obtained by measuring the thickness of the PLASTICINE layers. The stress distribution and the forging load were computed according to the strain distribution, respectively. To determine the effect of different lubricants on the PLASTICINE modeling, several ring compression tests were performed using Vaseline, soap suds, baby powder, facial tissue and plastic wrapping. In order to verify the validity of the modeling data, a similarity study between PLASTICINE and Ck45 was made. Considering the effect of strain path, the load obtained by this technique matched fairly well with the actual load.

In this paper, the artificial neural network based multi-objective optimization of twist EXTRUSION process is carried out. The target purpose functions are equivalent plastic strain, strain distribution and EXTRUSION force. The design variables are twistangle, friction factor and loading rate. The FEM model of the process is first created and used to create training cases for the ANN, and the well-trained ANN is used as a quick and exact model of the process. Then the optimization of the design variables is conducted by an integrated genetic algorithm and ANN modelto create a set of optimal solutions (Pareto front). Leveldiagrams are then used to select the best solution from the Pareto front. Finally the response surface methodology has been used to study the interaction between the design parameters. The obtained results show that the best range of twist angle is from 0.7 to 45 degree, friction factor from 0.65 to 0.7 and loading rate from 6.5 to 7 mm/s. Also variables with the largest effect on the process are twist angle and friction factor.

Degree of cure is one of the most important properties of prepregs, so that the change in degree of cure will cause the changing the of other properties such as, mechanical properties, tack and resin flow. In order to predict and monitor degree of cure, the quantitative relation with processing parameters such as line speed, oven temperature and resin content have to be determined. In this paper, the heat transfer equation governing epoxy-glass prepreg curing systems, together with the equation for the rate of epoxy resin curing is solved using numerical methods and computer programs written in the MAPLE software. The kinetic parameters of the curing reaction of epoxy resin are also evaluated by using isothermal differential scanning calorimetry data. According to results, initially, the temperature and the degree of cure will be reduced by increasing the amount of resin. However, releasing of the heat of curing increases both the temperature variation rate and the degree of curing. Moreover, the fiber types affect the degree of curing; the higher the density and specific heat capacity of the fibers, the less the temperature and degree of curing. Finally, increasing the oven temperature raised the temperature and degree of cure, as expected. On the other hand, increasing the line speed decreased the temperature and the degree of cure.

A mathematical model for the performance prediction of an industrial glass furnace with six ports on each side was developed. This model comprises of two main sub-models for the combustion chamber and GLASS-melting tank. The first sub-model consists of the models for the combustion and the heat transfer model including, radiation, convection and conduction. The fuel combustion in atmospheric pressure is assumed perfectly and without soot. Heat balance equations in the gas; glass and walls determine the rate of heat transfer to the glass surface. The second sub-model consists of the model for the batch melting. The temperature distribution in the glass tank is computed by using results of the combustion simulation and effective conduction coefficient of molten glass. The results of the combustion model can be used for the pollution prediction and optimization of the furnace parameters to decrease the gas pollutants in the furnace.