ASSESSING THE STRESS FIELDS IN AN INJECTION-MOULDED UNDERCUT GEOMETRY DURING EJECTION SUPPORTED BY NEURAL NETWORKS

作者：Florjanic Blaz^*; Bozic Uros; Zafosnik Bostjan

来源：Materiali in Tehnologije, 2014, 48(1): 125-130.

• thermoplastic polymers
• injection moulding
• finite-elements method
• design of experiments
• artificial neural networks

The continuous demand for cost optimization in the manufacturing of thermoplastic polymer products leads to the design trend of minimizing the number of assembly parts, which consequentially increases their geometrical complexity. This trend directly influences the manufacturing process of injection-moulded thermoplastic polymer parts. Parts designed in accordance with this design trend have many undercut-geometry features, which usually cannot be ejected from the mould without complex mould kinematics. A typical case of undercut geometry is represented by an annular snap joint, which can be released from the mould core by stripping it off. Stripping it off can be applied if the undercut geometry is deformed within the material's elasticity limits during ejection. When the stripping-off principle is used, an analysis of the stress field in the area of the product's undercut geometry should be carried out. Finite-element methods are commonly applied for determining the stress field. These methods offer a single point solution that requires lots of engineering effort and has to be repeated for any geometry modification. This study is focused on developing an artificial-neural-network response model that properly describes the relationships between the input factors (geometrical features) and the corresponding responses (maximum stress) in an undercut area. To overcome the necessity of carrying out numerical simulations for all the input-factor combinations the Taguchi design of experiment was used. Both the analysis of variance preformed within the Taguchi design of experiment and the artificial neural network model validation confirmed that the most influential geometrical input factor is the draft angle. For the artificial-neural-network model validation a virtual full-factorial design of experiment was used and the response surfaces were generated based on the obtained experimental results. Although the model solution is developed for a specific undercut geometry, the presented paper offers a generalized approach for assessing the stress field of the undercut-geometry features.

• 出版日期2014-2