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基于拉杆伸长的注塑成型充模-保压切换控制及成型质量预测

Tie-Bar Elongation Based Filling-To-Packing Switchover Control and Prediction of Injection Molding Quality.

作者信息

Chen Jian-Yu, Liu Chun-Ying, Huang Ming-Shyan

机构信息

Bachelor's Program of Precision System Design, Feng Chia University, 100, Wenhwa Rd., Seatwen, Taichung City 40724, Taiwan.

Department of Mechatronics Engineering, National Kaohsiung University of Science and Technology, 1 University Rd., Yanchao Dist., Kaohsiung City 824, Taiwan.

出版信息

Polymers (Basel). 2019 Jul 9;11(7):1168. doi: 10.3390/polym11071168.

DOI:10.3390/polym11071168
PMID:31323974
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6680528/
Abstract

Filling-to-packing switchover (also called V/P switchover) is critical for assuring injection molding quality. An improper V/P switchover setting may result in various defects of injection-molded parts, such as excessive residual stress, flash, short shot, and warpage, etc. To enhance a consistent molding quality, recent V/P switchover approaches adopt cavity pressure profiles requiring sensors embedded in mold cavities, which is invasive to mold cavities and more expensive. Instead of using cavity pressure sensors, by working with the most popular screw position switchover control, this study hereby proposes a novel approach of tuning V/P switchover timing using a tie-bar elongation profile. In this investigation, a dumbbell testing specimen mold is applied to verify the feasibility of the method proposed. The results show that the mold filling and packing stages can be observed along the tie-bar elongation profile, detected by mounting strain gauges on the tie bars. Also, the characteristics of the cavity pressure are similar to those of the tie-bar elongation profile under a proper clamping force condition. Moreover, the varying process parameter settings which include injection speed, V/P switchover point, and holding pressure, can be reflected in these profiles. By extracting their characteristics, the application of the V/P switchover is proved to be realistic. This research conducted an experiment to verify the proposed V/P switchover decision method based on the tie-bar elongation profile. The result showed that the fluctuation of the part's weight corresponding to a slight change of the barrel's temperature from 210 °C to 215 °C can be successfully controlled with this method. Besides, the maximum clamping force increment extracted from the tie-bar elongation profile was found to be a good indicator for online monitoring of the reground material variation.

摘要

充模到保压切换(也称为V/P切换)对于确保注塑成型质量至关重要。不当的V/P切换设置可能会导致注塑成型零件出现各种缺陷,如残余应力过大、飞边、缺料和翘曲等。为了提高成型质量的一致性,最近的V/P切换方法采用了需要在模腔内嵌入传感器的型腔压力曲线,但这对模腔具有侵入性且成本更高。本研究不使用型腔压力传感器,而是结合最常用的螺杆位置切换控制,提出了一种利用拉杆伸长曲线来调整V/P切换时机的新方法。在这项研究中,应用哑铃形测试样品模具来验证所提出方法的可行性。结果表明,通过在拉杆上安装应变片进行检测,可以沿着拉杆伸长曲线观察到模具的充模和保压阶段。此外,在适当的合模力条件下,型腔压力的特征与拉杆伸长曲线的特征相似。而且,包括注射速度、V/P切换点和保压压力在内的不同工艺参数设置都可以在这些曲线中体现出来。通过提取它们的特征,证明了V/P切换的应用是切实可行的。本研究进行了实验,以验证基于拉杆伸长曲线提出的V/P切换决策方法。结果表明,使用该方法可以成功控制料筒温度从210℃轻微变化到215℃时零件重量的波动。此外,从拉杆伸长曲线中提取的最大合模力增量被发现是在线监测回料变化的一个良好指标。

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本文引用的文献

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On-line measurement of clamping force for injection molding machine using ultrasonic technology.采用超声技术的注塑机合模力在线测量。
Ultrasonics. 2019 Jan;91:170-179. doi: 10.1016/j.ultras.2018.08.013. Epub 2018 Aug 17.
优化注塑工艺参数并基于喷嘴压力曲线和锁模力构建自适应过程控制系统。
Polymers (Basel). 2023 Jan 25;15(3):610. doi: 10.3390/polym15030610.
4
Injection Barrel/Nozzle/Mold-Cavity Scientific Real-Time Sensing and Molding Quality Monitoring for Different Polymer-Material Processes.用于不同聚合物材料工艺的注料筒/喷嘴/模具型腔科学实时感应和成型质量监测。
Sensors (Basel). 2022 Jun 24;22(13):4792. doi: 10.3390/s22134792.
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A Simple Method of Reducing Coolant Leakage for Direct Metal Printed Injection Mold with Conformal Cooling Channels Using General Process Parameters and Heat Treatment.一种使用通用工艺参数和热处理来减少具有随形冷却通道的直接金属打印注塑模具冷却液泄漏的简单方法。
Materials (Basel). 2021 Nov 27;14(23):7258. doi: 10.3390/ma14237258.
6
Quality Classification of Injection-Molded Components by Using Quality Indices, Grading, and Machine Learning.利用质量指标、分级和机器学习对注塑部件进行质量分类
Polymers (Basel). 2021 Jan 22;13(3):353. doi: 10.3390/polym13030353.
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Quality Prediction for Injection Molding by Using a Multilayer Perceptron Neural Network.基于多层感知器神经网络的注塑成型质量预测
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