李光,要志雯.基于能量密度确定缓冲材料性能曲线的研究[J].包装工程,2020,41(23):103-108.
LI Guang,YAO Zhi-wen.Determining Performance Curves of Cushioning Material Based on Energy Density[J].Packaging Engineering,2020,41(23):103-108. |
| 基于能量密度确定缓冲材料性能曲线的研究 |
| Determining Performance Curves of Cushioning Material Based on Energy Density |
| 投稿时间：2020-05-22 |
| DOI：10.19554/j.cnki.1001-3563.2020.23.015 |
| 中文关键词: 发泡聚乙烯 缓冲曲线族 预测 能量密度 |
| 英文关键词: expandable polyethylene cushion curves family prediction energy density |
| 基金项目:天津市自然科学基金(17JCTPJC54900) |
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| 中文摘要: |
| 目的 研究发泡聚乙烯(EPE)缓冲曲线的简便预测方法，利用一条已知缓冲曲线作为预测工具曲线，基于能量密度对已知缓冲曲线进行处理，从而得到EPE缓冲曲线族。方法 根据能量守恒定律，通过得到该缓冲材料的能量密度-冲击应力曲线，绘制不同条件下的缓冲曲线。推导缓冲材料静应力、冲击应力、单位体积吸收能量之间的关系，提出预测缓冲材料冲击最大加速度，以及生成缓冲曲线族的方法。利用密度为24 kg/m3，材料厚度分别为25.4，38.1，50.8，76.2 mm的EPE，跌落高度分别为30.5，45.7，61.0 cm的出版缓冲曲线，以及密度为35 kg/m3，材料厚度为55 mm的EPE，跌落高度分别为46，66，76 cm的试验缓冲曲线，预测不同条件下的EPE缓冲曲线，并与出版缓冲曲线和试验缓冲曲线进行对比和误差分析。结果 选用适当条件范围(跌落高度及材料厚度)的缓冲曲线作为预测工具曲线预测缓冲曲线族，预测结果与已出版或试验缓冲曲线较为接近。结论 此方法可以有效减少试验次数，为快速获得缓冲曲线族提供了新思路。 |
| 英文摘要: |
| The work aims to study a simple prediction method of EPE cushion curve. A known cushion curve was used as a prediction tool, and the EPE cushion curve family was obtained by processing the known cushion curve based on the energy density. According to the energy conservation law, the energy density-shock stress curve of the cushion material was obtained to draw the cushion curves under different conditions. The relationship between static stress, shock stress and absorbed energy per unit volume of the cushion material was derived, and the method of predicting the maximum shock acceleration of the cushion material and generating its family of cushion curves was proposed. The published cushion curves of EPE with density of 24 kg/m3, material thicknesses of 25.4 mm, 38.1 mm, 50.8 mm, and 76.2 mm, and drop heights of 30.5 cm, 45.7 cm and 61.0 cm, and experimental cushion curves of EPE with density of 35 kg/m3, material thickness of 55 mm, and drop heights of 46 cm, 66 cm and 76 cm were used to predict the EPE cushion curves under different conditions. The comparative error analysis was done between predicted cushion curves and published cushion curves and experimental cushion curves. When the cushion curves within the appropriate condition range (drop height, material thickness) were selected as the prediction tool curve to predict the curve family, the prediction results are close to the published or experimental cushion curves. This method can effectively reduce the number of tests and provide a new idea for obtaining cushion curves family quickly. |
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