{"title":"加强可持续食品包装设计:预测通风瓦楞纸板强度的机器学习方法","authors":"","doi":"10.1016/j.biosystemseng.2024.08.012","DOIUrl":null,"url":null,"abstract":"<div><p>In the food packaging industry, ventilated corrugated paperboard boxes are crucial for sustainable transport of fresh products. While these boxes' ventilation holes advance air circulation, they also impact the material's compression or buckling strength. Variations in hole geometry and location affecting this strength are explored, considering the composite material, multi-layered structure. Traditional mechanical analyses, which often require simplifications, may not fully capture this complexity, leading to less accurate predictions of the paperboard's strength. To address these challenges, a machine learning (ML) approach was utilized, employing the Light Gradient Boosting Machine (LGBM) to develop a predictive model for the buckling strength of corrugated paperboard boxes with ventilation cutouts. This physics-informed ML model, trained on a compression dataset resulting from experimental tests for plates with a single cutout in three shapes and Finite Element Method (FEM) simulations for plates with various patterns of circular cutouts, provides highly accurate estimates of the plates' buckling strength. It achieved 91.7% accuracy on experimental data and 94.68% on FEM simulation data, showcasing its reliability. A new tool for predicting the buckling strength of corrugated paperboard is provided by this research, along with insights that can inform the design of more sustainable packaging solutions. Furthermore, the methodology and findings have broader applications, potentially benefiting sectors like aerospace and construction, where similar structural materials are used.</p></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":null,"pages":null},"PeriodicalIF":4.4000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancing sustainable food packaging design: A machine learning approach to predict ventilated corrugated paperboard strength\",\"authors\":\"\",\"doi\":\"10.1016/j.biosystemseng.2024.08.012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In the food packaging industry, ventilated corrugated paperboard boxes are crucial for sustainable transport of fresh products. While these boxes' ventilation holes advance air circulation, they also impact the material's compression or buckling strength. Variations in hole geometry and location affecting this strength are explored, considering the composite material, multi-layered structure. Traditional mechanical analyses, which often require simplifications, may not fully capture this complexity, leading to less accurate predictions of the paperboard's strength. To address these challenges, a machine learning (ML) approach was utilized, employing the Light Gradient Boosting Machine (LGBM) to develop a predictive model for the buckling strength of corrugated paperboard boxes with ventilation cutouts. This physics-informed ML model, trained on a compression dataset resulting from experimental tests for plates with a single cutout in three shapes and Finite Element Method (FEM) simulations for plates with various patterns of circular cutouts, provides highly accurate estimates of the plates' buckling strength. It achieved 91.7% accuracy on experimental data and 94.68% on FEM simulation data, showcasing its reliability. A new tool for predicting the buckling strength of corrugated paperboard is provided by this research, along with insights that can inform the design of more sustainable packaging solutions. Furthermore, the methodology and findings have broader applications, potentially benefiting sectors like aerospace and construction, where similar structural materials are used.</p></div>\",\"PeriodicalId\":9173,\"journal\":{\"name\":\"Biosystems Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2024-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biosystems Engineering\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1537511024001909\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURAL ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biosystems Engineering","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1537511024001909","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURAL ENGINEERING","Score":null,"Total":0}
引用次数: 0
摘要
在食品包装行业,通风瓦楞纸板箱对于新鲜产品的可持续运输至关重要。这些纸箱的通风孔在促进空气流通的同时,也会影响材料的抗压或抗弯强度。考虑到复合材料的多层结构,我们探讨了影响这种强度的孔几何形状和位置的变化。传统的机械分析通常需要简化,可能无法完全捕捉到这种复杂性,导致对纸板强度的预测不够准确。为了应对这些挑战,我们采用了一种机器学习(ML)方法,利用光梯度提升机(LGBM)开发了一种带通风口的瓦楞纸板箱屈曲强度预测模型。该物理信息 ML 模型是在对三种形状的单开口板材进行实验测试和对具有各种圆形开口图案的板材进行有限元法(FEM)模拟后得到的压缩数据集上进行训练的,可对板材的屈曲强度进行高精度估算。实验数据的准确率为 91.7%,有限元模拟数据的准确率为 94.68%,充分显示了其可靠性。这项研究为预测瓦楞纸板的屈曲强度提供了一种新工具,同时也为设计更具可持续性的包装解决方案提供了启示。此外,该方法和研究结果还具有更广泛的应用前景,有可能惠及航空航天和建筑等使用类似结构材料的行业。
Enhancing sustainable food packaging design: A machine learning approach to predict ventilated corrugated paperboard strength
In the food packaging industry, ventilated corrugated paperboard boxes are crucial for sustainable transport of fresh products. While these boxes' ventilation holes advance air circulation, they also impact the material's compression or buckling strength. Variations in hole geometry and location affecting this strength are explored, considering the composite material, multi-layered structure. Traditional mechanical analyses, which often require simplifications, may not fully capture this complexity, leading to less accurate predictions of the paperboard's strength. To address these challenges, a machine learning (ML) approach was utilized, employing the Light Gradient Boosting Machine (LGBM) to develop a predictive model for the buckling strength of corrugated paperboard boxes with ventilation cutouts. This physics-informed ML model, trained on a compression dataset resulting from experimental tests for plates with a single cutout in three shapes and Finite Element Method (FEM) simulations for plates with various patterns of circular cutouts, provides highly accurate estimates of the plates' buckling strength. It achieved 91.7% accuracy on experimental data and 94.68% on FEM simulation data, showcasing its reliability. A new tool for predicting the buckling strength of corrugated paperboard is provided by this research, along with insights that can inform the design of more sustainable packaging solutions. Furthermore, the methodology and findings have broader applications, potentially benefiting sectors like aerospace and construction, where similar structural materials are used.
期刊介绍:
Biosystems Engineering publishes research in engineering and the physical sciences that represent advances in understanding or modelling of the performance of biological systems for sustainable developments in land use and the environment, agriculture and amenity, bioproduction processes and the food chain. The subject matter of the journal reflects the wide range and interdisciplinary nature of research in engineering for biological systems.