This paper suggests a methodology based on a neuroevolutionary approach to optimize the use of material in blow molding applications. This approach aims at determining the optimal thickness distribution for a certain blow molded product as a function of its geometry. Multiobjective search is performed by neuroevolution to reflect the conflicting nature of the design problem and to capture some possible trade-offs. During the search, each design alternative is evaluated through a finite element analysis. The coordinates of the mesh elements are the inputs to an artificial neural network whose output determines the thickness for the corresponding location. The proposed approach is applied to the design of an industrial bottle. The results reveal the validity and usefulness of the proposed technique, which was able to distribute the material along the most critical regions to obtain adequate mechanical properties. The approach is general and can be applied to products with different geometries.

Injection blow molding process is widely used in the industry to produce plastic parts. One of the main challenges in optimizing this process is to find the best manufacturing thickness profiles which provides the desirable mechanical properties to the final part with minimal material usage. This paper proposes a methodology based on a neuroevolutionary approach to optimize this process. This approach focuses on finding the optimal thickness distribution for a given blow molded product as a function of its geometry. Neural networks are used to represent thickness distributions and an evolutionary multiobjective optimization algorithm is applied to evolve neural networks in order to find the best solutions, i.e., to obtain the best trade-off between material usage and mechanical properties. Each solution is evaluated through finite element analysis simulation considering the design of an industrial bottle. The results showed that the proposed technique was able to find good solutions where the material was distributed along the most critical regions to maintain adequate mechanical properties. This approach is general and can also be applied to different geometries.

Injection stretch blow molding is a very important thermoplastic processing technique producing hollow containers with mechanical performance. One of the main challenges in optimizing this process consists in finding the best thickness profile for each part in order to achieve the desired mechanical properties with less material use. In a previous study, a new methodology based on a neuroevolutionary multiobjective optimization approach was proposed to enhance the entire process, which considers that the process is optimized by phases, starting by the end. In that initial study only the final phase of the process was addressed, where the best thickness profile for an industrial bottle was found in order to satisfy the required mechanical properties with less material use. In the present study, the focus is the second stage of the optimization methodology, concerning the blowing phase of injection blow molding process. The optimal results obtained in the first phase are used as the optimal thickness profile for the bottle with the goal to find the best preform thickness profile which produces the desired bottle. The same procedures are used and the results shown that the methodology was successfully applied to its second phase.

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