El Ghadoui Mohamed, Mouchtachi Ahmed, Majdoul Redouane
Laboratory of Structural Engineering, Processes, Intelligent Systems and Informatique in ENSAM, Hassan 2 University, Casablanca, Morocco.
Laboratory of Complex Cyber Physical Systems in ENSAM, Hassan 2 University, Casablanca, Morocco.
Sci Rep. 2023 Dec 9;13(1):21817. doi: 10.1038/s41598-023-48679-0.
This study presents a novel hybrid optimization approach for intelligent manufacturing in plastic injection molding (PIM). It focuses on globally optimizing process parameters to ensure high-quality products while reducing cycle time, material waste, and energy consumption. The method combines a backpropagation neural network (BPNN) with a genetic algorithm (GA) and employs a multi-objective optimization model based on design of experiments (DoE). A BP artificial neural network captures the relationship between optimization goals and process parameters. Leveraging the genetic algorithm, it effectively optimizes process parameters for achieving global optimization goals. The case study involves a polypropylene product, considering dimensional deviation, weight, cycle time, and energy consumption during the PIM cycle. Design variables include melt temperature, injection velocity, injection pressure, commutation position, holding pressure, holding time, and cooling time. The results demonstrate that this approach efficiently adjusts process parameters to meet quality standards, significantly reducing raw material consumption (2%), cycle time (12%), and energy consumption (16%). This offers substantial benefits for companies in highly competitive markets demanding swift adoption of smart production methods.
本研究提出了一种用于塑料注塑成型（PIM）智能制造的新型混合优化方法。它专注于全局优化工艺参数，以确保产品质量高，同时减少周期时间、材料浪费和能源消耗。该方法将反向传播神经网络（BPNN）与遗传算法（GA）相结合，并采用基于实验设计（DoE）的多目标优化模型。BP人工神经网络捕捉优化目标与工艺参数之间的关系。利用遗传算法，它有效地优化工艺参数以实现全局优化目标。案例研究涉及一种聚丙烯产品，考虑了PIM周期中的尺寸偏差、重量、周期时间和能源消耗。设计变量包括熔体温度、注射速度、注射压力、换向位置、保压压力、保压时间和冷却时间。结果表明，这种方法能有效地调整工艺参数以满足质量标准，显著降低原材料消耗（2%）、周期时间（12%）和能源消耗（16%）。这为处于竞争激烈市场中、需要迅速采用智能生产方法的公司带来了巨大益处。
