Injection molds, composed of components such as upper and lower cores, mold bases, pins, and cooling channels, serve as the primary tooling for manufacturing plastic products. Despite the often simple geometry of molded products, the configuration and design of mold components remain highly complex, making the technical expertise and accumulated know-how of mold designers essential. However, the mold industry is facing increasing difficulties due to the discontinuation of academic programs dedicated to mold design, the aging of experienced designers, and the lack of incoming skilled personnel. To address these challenges, research on automating mold design has continued, and recent advancements in artificial intelligence (AI) have accelerated efforts to internalize expert knowledge through a variety of computational approaches. In this study, we conducted foundational research aimed at constructing a DT-AX platform capable of handling multiple domains by implementing and modularizing diverse processes within a digital-twin (DT) environment and integrating AI modules specialized for each process. Given the input dimensions of a bottle-cap model (diameter and height), the simplified outer dimensions of a core mold were predicted and subsequently used to generate a 3D model. The resulting STEP file was verified to be compatible with commercial CAD and simulation software. Overall, the results demonstrate the feasibility of implementing an automated mold-design module within a digital-twin environment. Future work will focus on diversifying design variables and increasing geometric complexity to develop modules that more closely approximate real-world mold design.

Molding is the root industry of the manufacturing as a means to mass-produce developed prototypes. Molds are typically divided into injection molds and press mold industries. Injection molds produce the products by injection of molten plastic into a mold, and press molds are molded and bended plate. The ejection system, such as eject pins, is used to separate the manufactured products from the mold, which involves a number of hole operations. Location, diameter and depth of holes are often tabulated and managed collectively when designing 2D drawings. The design efficiency was realized by applying CATIA Automation to the 3D model and bringing in the data of the holes in the Excel data.

Recently, the engineering designer of injection mold has become more and more dependent on the CAE. In the design factors of injection mold, the shrinkage rate should be considered as one of the important performances to produce the reliable products. Therefore, the shrinkage rate can be mostly calculated by the MoldFlow and CATIA in the design process. However, it is not easy to predict the shrinkage rate of a plastic injection mold in its design process because the analysis can take minutes to hours, the high computational costs of performing the analysis limit their use in design optimization. In this study, the surrogate models based on the Taguchi Design in order to optimize the shrinkage rate of bevel gear injection mold is used instead of the original models, facilitating design optimal design.

Powder Injection Molding (PIM) has recently been recognized as an advanced manufacturing technology for low-cost mass production of metal or ceramic parts of complicated geometry. With this regards, design technology of dental scaler tip PIM mold, which has complex shape, with the help of computer-aided analysis for powder injection molding process was developed. Compter aided analysis results, such as filling pattern, weldline formation, and air vent position prediction were investigated and eventually showed good agreements with experimental results.