Lee Junhan, Yang Dongcheol, Yoon Kyunghwan, Kim Jongsun
Department of Mechanical Engineering, Dankook University, Yongin 16890, Korea.
Molding & Metal Forming R&D Department, Korea Institute of Industrial Technology, Bucheon 14442, Korea.
Polymers (Basel). 2022 Apr 23;14(9):1724. doi: 10.3390/polym14091724.
Artificial neural network (ANN) is a representative technique for identifying relationships that contain complex nonlinearities. However, few studies have analyzed the ANN's ability to represent nonlinear or linear relationships between input and output parameters in injection molding. The melt temperature, mold temperature, injection speed, packing pressure, packing time, and cooling time were chosen as input parameters, and the mass, diameter, and height of the injection molded product as output parameters to construct an ANN model and its prediction performance was compared with those of linear regression and second-order polynomial regression. Following the preliminary experiment results, the learning data sets were divided into two groups, i.e., one showed linear relation between the mass of the final product and the range of packing time (linear relation group), and the other showed clear nonlinear relation (nonlinear relation group). The predicted results of ANN were relatively better than those of linear regression and second-order polynomial for both linear and nonlinear relation groups in our specific data sets of the present study.
人工神经网络（ANN）是一种用于识别包含复杂非线性关系的代表性技术。然而，很少有研究分析人工神经网络在注塑成型中表示输入和输出参数之间非线性或线性关系的能力。选择熔体温度、模具温度、注射速度、保压压力、保压时间和冷却时间作为输入参数，将注塑产品的质量、直径和高度作为输出参数，构建人工神经网络模型，并将其预测性能与线性回归和二阶多项式回归的预测性能进行比较。根据初步实验结果，将学习数据集分为两组，即一组显示最终产品质量与保压时间范围之间存在线性关系（线性关系组），另一组显示明显的非线性关系（非线性关系组）。在本研究的特定数据集中，对于线性和非线性关系组，人工神经网络的预测结果相对优于线性回归和二阶多项式回归的预测结果。
