Optimization Of Injection Molding Parameters Research Articles

Multi-Objective Optimization of Injection Molding Parameters for Manufacturing Thin-Walled Composite Connector Terminals.

The rapid development of new energy vehicles demands significant improvements in connector structures and performance standards. Wire harness connectors, crucial for linking various electrical components, face challenges due to their small size and thin-walled structure, which can lead to dimensional shrinkage and warping during injection molding. To address these issues, this study optimizes the injection molding process by fine-tuning parameters such as melt temperature, mold temperature, injection time, holding pressure/time, and cooling time. By integrating the Taguchi method with grey relational analysis, the study enhances the molding process for thin-walled composite connectors. This combined approach provides a comprehensive framework for optimizing multiple quality objectives and improving the overall performance of injection-molded composite components.

Optimization of Injection Molding Parameters for HDPE/TiO₂ Nanocomposites Fabrication with Multiple Performance Characteristics Using the Taguchi Method and Grey Relational Analysis.

The current study presents an investigation on the optimization of injection molding parameters of HDPE/TiO2 nanocomposites using grey relational analysis with the Taguchi method. Four control factors, including filler concentration (i.e., TiO2), barrel temperature, residence time and holding time, were chosen at three different levels of each. Mechanical properties, such as yield strength, Young’s modulus and elongation, were selected as the performance targets. Nine experimental runs were carried out based on the Taguchi L9 orthogonal array, and the data were processed according to the grey relational steps. The optimal process parameters were found based on the average responses of the grey relational grades, and the ideal operating conditions were found to be a filler concentration of 5 wt % TiO2, a barrel temperature of 225 °C, a residence time of 30 min and a holding time of 20 s. Moreover, analysis of variance (ANOVA) has also been applied to identify the most significant factor, and the percentage of TiO2 nanoparticles was found to have the most significant effect on the properties of the HDPE/TiO2 nanocomposites fabricated through the injection molding process.

Optimization of Injection Molding Parameters by Data Mining Method in PIM Process

Data Mining is a method that can be used to analyze large amount of data and produce useful information. In this study, clustering which is one of data mining tasks is used to clustered machine based on the injection moulding data. This paper is the first documented results on the optimization of Injection Moulding via Data Mining. Powder injection moulding is a process to produce near net shape with intricate part in mass production. This work focus on the optimization of injection molding process with combination of fine, coarse and bimodal water atomized SS 316L powder particles. The parameters involved in the optimization are injection pressure, injection temperature, mould temperature, holding pressure, injection rate, holding time, powder loading, cooling time and particle size. These variables are based on the defect score, green density and green strength. The key influencer report shows that the most influencing factors are injection rate, holding pressure as well as mould temperature where defect score lower than 2.4 can be achieved. The density higher than 5.34g/cm3 is also influenced most by the mould temperature. The result also shows that the optimize condition can be achieved by using bimodal particle. Injection rate and mould temperature gives the highest impact on the defect score and green strength value. While highest green density is significantly affected by powder loading and injection pressure.

Evaluation and Optimization of Injection Molding Parameters Using Taguchi's Experimental Design and Moldflow Analysis

Evaluation and Optimization of Injection Molding Parameters Using Taguchi's Experimental Design and Moldflow Analysis

Evaluation and Optimization of Injection Molding Parameters Using Taguchi's Experimental Design and Moldflow Analysis

The dimension contraction of an injection molding product is determined by mold design and injection operation procedures. The operating parameters that determine the contraction include the cooling waterway disposition, runner size, plastic material temperature, maintaining pressure, maintaining time, mold temperature, and cycle time. In this research, the Taguchi’s experimental design is combined with Modlflow of computer aided engineering software for simulation to evaluate the influence of parameters and derive an optimal combination of the above dominant parameters in the design and the operation of injection molding. The Taguchi method suggests minimum experimental trials for optimal design and operating parameter optimizing. The combined methodology was validated with experiments resulting in minimum dimension contraction as expected.