整合多元统计控制图和机器学习,识别注塑成型中聚乳酸与玻璃纤维复合材料质量特性中的缺陷

IF 1.6 4区 工程技术 Q2 MATERIALS SCIENCE, TEXTILES
Bo-Shen Chen, Chang-Chiun Huang, Ting-Wei Liao, Chung-Feng Jeffrey Kuo
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引用次数: 0

摘要

在注塑成型加工过程中,需要调整复杂的加工参数以达到预期质量。一旦加工过程出现异常,就必须花费时间和人力进行故障诊断。本研究的重点是聚乳酸/玻璃纤维复合材料注塑成型加工参数的故障诊断。注塑成型加工参数包括熔体温度、注塑速度、保压压力、保压时间和冷却时间。质量包括拉伸强度、硬度、冲击强度和弯曲强度。当加工参数偏离最佳工艺条件时,多元统计控制图会监测质量下降情况。机器在最佳工艺条件下运行,生成正常样品,并选择相应的四个质量数据作为历史数据。Hotelling's T2 用于根据历史数据计算控制上限 (UCL),以检测异常样品。如果 T2 值超过 UCL,则相应样本被视为异常样本。然后,通过残差控制图得到异常样本的质量残差。它们被选为反向传播神经网络(BPNN)的特征值,用于识别异常处理参数。实验结果证明,BPNN 对单因素异常样本的识别率可达 100%。对于单/双因素混合样本,双因素分类的准确率可达 97.44%。该研究具有稳定性高、无损、精度高、成本低等优点,可在注塑行业广泛推广。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integration of the multivariate statistical control chart and machine learning to identify faults in the quality characteristics for polylactic acid with glass fiber composites in injection molding
Complex processing parameters need to be adjusted for expected qualities in injection molding processing. Once the process is abnormal, it is essential to spend time and human work on fault diagnosis. In this study, we focus on fault diagnosis of injection molding processing parameters for polylactic acid/glass fiber composites. The injection molding processing parameters include the melt temperature, injection speed, packing pressure, packing time, and cooling time. The qualities include tensile strength, hardness, impact strength, and flexure strength. When processing parameters deviate from the optimal process condition, the multivariate statistical control chart monitors downgraded qualities. The machine is operated at the optimal process conditions to generate normal samples and the corresponding four qualities of data are chosen as the historical data. Hotelling’s T2 is used to calculate the upper control limit (UCL) from the historical data to detect abnormal samples. If the T2 value exceeds the UCL, the corresponding sample is considered abnormal. Then, the residuals of qualities for abnormal samples are obtained by a residual control chart. They are chosen as the feature values for the backpropagation neural network (BPNN) to identify the abnormal processing parameters. The experimental results proved that the BPNN can achieve a 100% recognition rate for single-factor abnormal samples. For the single-/double-factor mixture, the accuracy rate of double-factor classification can reach 97.44%. This proposed study has the advantage of high stability, being non-destructive, high precision, and low cost, and can be widely promoted in injection molding industries.
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来源期刊
Textile Research Journal
Textile Research Journal 工程技术-材料科学:纺织
CiteScore
4.00
自引率
21.70%
发文量
309
审稿时长
1.5 months
期刊介绍: The Textile Research Journal is the leading peer reviewed Journal for textile research. It is devoted to the dissemination of fundamental, theoretical and applied scientific knowledge in materials, chemistry, manufacture and system sciences related to fibers, fibrous assemblies and textiles. The Journal serves authors and subscribers worldwide, and it is selective in accepting contributions on the basis of merit, novelty and originality.
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