Earticle

초록

영어

Though box compression strength (BCS) is commonly used as a performance criterion for shipping containers, estimating BCS remains a challenge. In this study, artificial neural networks (ANN) are implemented as a new tool, with a focus on building up ANN architectures for BCS estimation. An Artificial Neural Network (ANN) model can be constructed by adjusting four modeling factors: hidden neuron numbers, epochs, number of modeling cycles, and number of data points. The four factors interact with each other to influence model accuracy and can be optimized by minimizing model’s Mean Squared Error (MSE). Using both data from the literature and “synthetic” data based on the McKee equation, we find that model estimation accuracy remains limited due to the uncertainty in both the input parameters and the ANN process itself. The population size to build an ANN model has been identified based on different data sets. This study provides a methodology guide for future research exploring the applicability of ANN to address problems and answer questions in the corrugated industry.

목차

Abstract
Introduction
1. Artificial Neural Networks (ANN)
2. Artificial Neural Networks (ANN) and McKee Date Set
3. Artificial Neural Networks (ANN) and An Idealized Data Set
4. Artificial Neural Networks (ANN) and A Data Set with Variation
Conclusions
References

키워드

저자

• By Juan Gu [ School of Packaging, Michigan State University, East Lansing, MI 48824-1223, USA ]
• Benjamin Frank [ Packaging Corporation of America, Lake Forest, IL, 60045, USA ]
• Euihark Lee [ School of Packaging, Michigan State University, East Lansing, MI 48824-1223, USA ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국포장학회지 [Journal of Korea Society of Packaging Science & Technology]

• 간기

  연3회

• pISSN

  1226-0207

• 수록기간

  1994~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 530 DDC 620