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首页> 外文期刊>Journal of Applied Polymer Science >Design Optimization of Extrusion-Blow-Molded Parts Using Prediction-Reliability-Guided Search of Evolving Network Modeling
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Design Optimization of Extrusion-Blow-Molded Parts Using Prediction-Reliability-Guided Search of Evolving Network Modeling

机译：基于预测-可靠性指导的演化网络建模搜索对吹塑成型零件进行设计优化

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Industries often adopt a two-stage design for blow-molded parts. The part thickness distribution is first determined by structural analysis to satisfy loading requirements, and this is followed by programming of the die-gap opening to realize the thickness distribution. This study proposes a soft-computing-based optimization scheme integrating part design and molding process control to search for the die-gap programming of the molding process with minimum part weight while satisfying performance constraints. Finite element analysis tools are applied to simulate the extrusion-blow-molding process and structural analysis. To reduce the number of simulations, the proposed scheme first establishes a neural network (NN) model from a small experimental design to simulate the system response, and it searches for the model optimum with a genetic algorithm (GA). Because the prediction generality of an NN from small training samples will be limited, this work proposes fuzzy reasoning for the prediction reliability of the model to guide the GA search for a quasi-optimum. The verification of the optimum is added to retrain the model, and the process iterates until convergence is reached. The iteration automatically distributes additional samples in the most probable space of the design optimum for the evolving model and improves the sampling efficiency. A high-density polyethylene bottle design is presented to illustrate the application and for comparison with the Taguchi method and a simple iteration of NN and GA. The proposed scheme outperforms the other two and provides a feasible optimum from a robust convergence.
机译：工业界通常对吹模零件采用两阶段设计。首先通过结构分析确定零件的厚度分布，以满足载荷要求，然后对模具间隙进行编程以实现厚度分布。这项研究提出了一种基于软计算的优化方案，该方案将零件设计和成型工艺控制相结合，以在满足性能约束的同时，以最小的零件重量搜索成型工艺的模间隙编程。应用有限元分析工具来模拟挤出吹塑成型过程和结构分析。为了减少仿真次数，所提出的方案首先从一个小型实验设计中建立了一个神经网络（NN）模型，以仿真系统响应，然后使用遗传算法（GA）搜索最优模型。由于从少量训练样本中预测NN的通用性将受到限制，因此本文针对模型的预测可靠性提出了模糊推理，以指导GA搜索拟最佳算法。添加了最佳验证以重新训练模型，并且过程反复进行，直到达到收敛为止。迭代会自动将额外的样本分配到设计中最适合进化模型的最可能空间中，从而提高采样效率。提出了一种高密度聚乙烯瓶设计以说明其应用，并与Taguchi方法以及NN和GA的简单迭代进行比较。所提出的方案优于其他两个方案，并从稳健的收敛中提供了可行的最佳方案。

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