Review: Optimizing Plastic Injection Processes for Enhanced Quality and Sustainable Manufacturing
Abstract
In the automotive world, plastic products are components that cannot be separated. Almost all automotive products use plastic because it is easy to produce, and the price is relatively cheap compared to other materials. For applications such as covers, the demand on plastic surface quality are higher than for different uses. Therefore, a lot of costs are incurred to achieve this quality. However, ongoing efforts have decreased the time and expense of developing plastic molds. Many researchers have conducted studies to improve the quality of these products. This review consolidates several research articles on optimizing plastic injection processes to reduce defects and improve product quality. Techniques such as Taguchi Method, Response Surface Methodology (RSM), Artificial Neural Networks (ANN), and Finite Element Method (FEM) were evaluated in this research. This review highlights the importance of process parameters such as melt temperature, injection pressure, and cooling time, as well as the role of digital simulation in designing efficient and sustainable molds. The results of the study show that in several studies, defects often occur in the product without carrying out the optimization process. Still, the Taguchi and ANOVA methods can reduce the weld line and sink after optimizing the process parameters, such as melting temperature, injection pressure, cooling time, and injection speed. Mark up to 30%. These findings highlight the potential of these techniques to significantly improve product quality and support more sustainable manufacturing practices in the plastic injection molding industry.
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DOI: http://dx.doi.org/10.22441/ijimeam.v7i1.31721
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