Optimization of Core Shift for Injection Mould based on ANFIS-FWA
Authors: Wang Quan , Zheng Yue , Wang Xiaodong , Chen Tuo 
Published: December 2024

Background:

In most of the deep-cavity shell plastic products injection moulds, such as
test tubes, syringes, and other medical devices, the slender core structure is essential. The uneven
pressure of the polymer melt acting on the core during the filling process easily causes core deformation,
resulting in core shift, the wall thickness inequality of the parts, and a series of problems,
which then affect the final shape and mechanical properties of the plastic parts. The type of deep
cavity component is prone to core shift defects during injection molding, resulting in uneven wall
thickness of the syringe sleeve and inability to achieve perfect fit with the syringe core rod. Reducing
the core shift of the component is the key issue of this article.

Background:

In most of the deep-cavity shell plastic products injection moulds, such as
test tubes, syringes, and other medical devices, the slender core structure is essential. The uneven
pressure of the polymer melt acting on the core during the filling process easily causes core deformation,
resulting in core shift, the wall thickness inequality of the parts, and a series of problems,
which then affect the final shape and mechanical properties of the plastic parts. The type of deep
cavity component is prone to core shift defects during injection molding, resulting in uneven wall
thickness of the syringe sleeve and inability to achieve perfect fit with the syringe core rod. Reducing
the core shift of the component is the key issue of this article.

Objective:

This study aims to combine injection moulding CAE with neural networks and optimization
algorithms to reduce core shift, improve molding quality and dimensional accuracy, extend mold
life, establish a regression model for predicting core shift, shorten product development time, and
improve production efficiency.

Methods:

Propose an injection moulding process parameter optimization method based on Adaptive
Neural Network Fuzzy Inference System (ANFIS) and Rireworks Algorithm (FWA). Taking a certain
medical syringe as the research object, the Taguchi experiment was designed with mold temperature,
melt temperature, injection time, injection pressure, packing pressure, and packing time as
optimization variables. A simulation was conducted using Moldflow software, and the signal-tonoise
ratio was used to analyze the significance and trend of the influence of process parameters on
the plastic core shift. A predictive regression model is constructed based on ANFIS to express the
nonlinear function relationship between process parameters and core shift. FWA is used to conduct
global optimization based on the ANFIS model to determine the optimal combination of injection
molding process parameters.

Results:

Through simulation verification, the errors between the FIS prediction model and the
ANFIS prediction model were compared with the actual simulation values. The ANFIS prediction
model had the highest accuracy, and the core shift of plastic parts based on the ANFIS-FWA combination
optimization method decreased by 27.6% compared to before optimization. It was effective in
improving the molding quality of plastic parts.

result:

Through simulation verification, the errors between the FIS prediction model and the ANFIS prediction model were compared with the actual simulation values. The ANFIS prediction model had the highest accuracy, and the core shift of plastic parts based on the ANFIS-FWA combination optimization method decreased by 27.6% compared to before optimization. It was effective to improve the molding quality of plastic parts.

Conclusion:

The core shift prediction model established based on ANFIS can accurately predict the
core shift of the product, greatly reducing the lengthy simulation time of the computer and shortening
the product development cycle. The ANFIS-FWA coupling method can find the best combination of
process parameters among multiple injection molding process parameters, achieve the improvement
of core shift defects, and provide new ideas for optimizing the core shift of the same type of product.