In this article, Low-Pressure Injection Molding (LPIM) method was investigated as a method for forming zirconia (zirconium oxide) parts. It is one of the methods used for manufacturing engineering ceramics with complex shapes & high dimensional accuracy. In this method, a binder composition (paraffin & industrial waxes) is used as the plasticizing agent to shape zirconia particles. Different parameters such as temperature, pressure & injection time, mold temperature, etc. have been found to be effective in shaping the ceramic parts based on this method. Throughout this research, these parameters were examined, & their optimal values were obtained. The optimal injection temperature was in the range of 80-90 Celsius degree, injection pressure in the range of 3-5 bar, & injection time in the range of 10-15 seconds to form a crucible with the size of 130×85×75 mm (height×internal diameter×external diameter). This study also examined the FESEM images of the microstructure of parts in the injected, debonded, & sintered bodies. According to the findings, the zirconia crucible in this article shows notable similarity with the crucible made by Zircoa company with the code 3001 in terms of physical properties such as bulk density & apparent porosity.

Low pressure injection molding (LPIM) method in this paper is investigated as a method of forming alumina parts. This is one of the methods of making engineering ceramics with complex shapes and high dimensional accuracy. In this method, a binder system (paraffin wax + carnuba wax) was used as a plastic agent for easy formation of alumina particles. Different parameters such as feedstock temperature, injection pressure, injection time, mold temperature, etc. are effective in shaping ceramic parts by LPIM. The study of these parameters and the selection of their optimal value are discussed in this article. The optimum injection temperature was in the range of 90-90 ° C, the optimum pressure was in the range of 4-6 bar, the injection time was in the range of 10-15 seconds for a cylindrical shape with dimensions of 20×125 mm (D × H).

In this article, Low-Pressure Injection Molding (LPIM) method was investigated as a method for forming zirconia (zirconium oxide) parts. It is one of the methods used for manufacturing engineering ceramics with complex shapes and high dimensional accuracy. In this method, a binder composition (paraffin waxe) is used as the plasticizing agent to shape zirconia particles. Optimizing the volume percentage of solid load helps to make parts without defects. For this purpose, in this research, two ways of investigating the behavior of flow behavior and debonding of zirconia feedstocks were used to select the optimal percentage of solid load. This optimization was done between feedstocks containing 52, 53, and 54 Vol% of solid load and at temperatures of 70, 80, and 90 °C. The feedstock containing 54% solid load has better flow behavior and less sensitivity to shear, as well as better debonding behavior than the other two feedstocks, and it was possible to make the part without defects through this feedstock.

Metal injection molding (MIM) is a novel process classified in powder metallurgy. This process can produce complex metallic parts with high rate of production and consists of four stages, including mixing, injection, debinding, and sintering, where the properties of the final part highly depends on the parameters of each of these stages. In this study, the parameters of injection pressure, injection and mold temperature, holding pressure, holding time, injection speed, and cooling time on the density, strength, and hardness of the final MIM compact have been investigated. By the design of experiments and response surface methodology (RSM) method, 50 samples have been injected using different parameters. In order to measure the density, tensile strength, ad hardness of the samples, the debinding and sintering procedures have been done on the injected samples. The results show that the injection pressure, injection temperature, and mold temperature have the highest effect on the strength and density of the final part, respectively, and on the other hand, holding pressure, holding time, and cooling time have a negligible effect. Within the measured properties, density and strength are more affected by the injection parameters compared to hardness. Finally, the optimum injection parameters for samples made of 4605 low alloy steel include injection pressure of 133 bar, injection temperature of 158oC, mold temperature of 60oC, the holding pressure of 70 bar, holding time of 8 second, injection speed of 112 mm/min, and cooling cycle of 17 second.

Autodesk Moldflow Insight (AMI) is a Computer-Aided Engineering (CAE) tool used to predict many molding phenomena such as pressure, filling pattern, cooling pattern, and deflection of injection molded parts. The purpose of this study is to validate AMI for use with plastic connector housings and determine the key factors in improving simulation accuracy. To validate AMI, a 3D Computer-Aided Design (CAD) model of a hypothetical, simplified connector housing with round circuit holes was created in Creo software. Then, a test mold was built using the simplified connector housing geometry with round core pins for the part circuit holes. The mold was outfitted with pressure sensors to monitor the exact pressures achieved in both the runner system and cavity. Using this mold, the validation part was manufactured more than fifteen times using an identical combination of material and processing conditions. Pressure sensors placed in the mold capture the exact pressures achieved in the part and runner system during molding. The averaged pressure profiles for each sensor are later compared against results from the simulation software to determine accuracy of the simulation program. Computer simulation models of the validation part were created with a mesh density of eight layers through the thickness of the model, which is appropriate for connector housings. The averaged values of the fifteen identically molded parts are then compared to the simulation results. This study results in an improved method for simulating the pressure profiles during plastic injection molding using refined process parameter definitions.

In the present study, the effective parameters of injection stage of the metal injection molding process (MIM) is investigated in order to make a sound industrial part from the AISI-4605 low-alloy steel. In order to design of experiments and also to optimize different parameters of injection, statistical method of response surface method (RSM) and five-variable Box-Behnken Design (BBD) have been used assuming a quadratic model. Using this software, 46 experiments were designed. Then the samples are made and their density is measured. After that, the importance of the injection parameters as well as the interaction between each parameter have been investigated using analysis of variance (ANOVA). Finally, this research has shown that the injection temperature of 155°C, the injection speed of 80 mm/s, the holding pressure of 83 bar, the holding time of 9 seconds and the injection pressure of 132 bar led to the maximum density of the green body, which is equivalent to 4.892 g/cm2. Finally, after producing a new sample using the optimal parameters a piece with a density of 4.9 g/cm2 was obtained, and it was observed that the results were very close to the predicted value, which presented the model has good accuracy in prediction.

Extrusion, injection molding, calendaring, and thermoforming are just a few of the processing techniques utilized in the plastics industry. Gas-assist injection molding plays a crucial role in this equipment. The advancement of standard injection molding is represented by this apparatus. Parts with thin wall thickness and hollow sections can be manufactured using this method. Use this strategy to save between 30% and 35% of the material. In this procedure, gas is first introduced after a brief shot of material is injected. The remaining portion of the cavity is filled in by injecting gas. The hollow section is described in detail by the gas core. In gas-assist injection molding, the geometry of the gas channel design is also very important. There is a wide variety of materials available for polymeric applications. Different polyamide materials, including polypropylene, polycarbonate, high-impact polystyrene, and polyethylene terephthalate (PET), can be processed using gas-assisted injection molding. Talc-filled polypropylene has been chosen for this study's simulation and experimental work. For simulation purposes, Moldflow plastic insight is utilized. Moldflow plastic insight was used to simulate the tensile samples, and gas-assist injection molding was used to create the experimental models. Simulation and experimental results are used to measure the wall thickness and gas penetration depth. The validation was then checked by comparing these results.

Product quality for plastic injection molding process is highly related with the settings for its process parameters. Additionally, the product quality is not simply based on a single quality index, but multiple interrelated quality indices. To find the settings for the process parameters such that the multiple quality indices can be simultaneously optimized is becoming a research issue and is now known as finding the efficient frontier of the process parameters. This study considers three quality indices in the plastic injection molding: war page, shrinkage, and volumetric shrinkage at ejection. A digital camera thin cover is taken as an investigation example to show the method of finding the efficient frontier. Solidworks and Moldflow are utilized to create the part’s geometry and to simulate the injection molding process, respectively. Nine process parameters are considered in this research: injection time, injection pressure, packing time, packing pressure, cooling time, cooling temperature, mold open time, melt temperature, and mold temperature. Taguchi's orthogonal array L27 is applied to run the experiments, and analysis of variance is then used to find the significant process factors with the significant level 0.05. In the example case, four process factors are found significant. The four significant factors are further used to generate 34 experiments by complete experimental design. Each of the experiments is run in Moldflow. The collected experimental data with three quality indices and four process factors are further used to generate three multiple regression equations for the three quality indices, respectively. Then, the three multiple regression equations are applied to generate 1,225 theoretical datasets. Finally, data envelopment analysis is adopted to find the efficient frontier of the 1,225 theoretical datasets. The found datasets on the efficient frontier are with the optimal quality. The process parameters of the efficient frontier are further validated by Moldflow. This study demonstrates that the developed procedure has proved a useful optimization procedure that can be applied in practice to the injection molding process.