Indexing metadata

Predicting Corrugated Box Compression Strength Using an Artificial Neural Network


 
Dublin Core PKP Metadata Items Metadata for this Document
 
1. Title Title of document Predicting Corrugated Box Compression Strength Using an Artificial Neural Network
 
2. Creator Author's name, affiliation, country Siripong Malasri; Engineering Management Graduate Program, Christian Brothers University, 650 East Parkway South, Memphis, TN, USA
 
2. Creator Author's name, affiliation, country Prasanth Rayapati; Engineering Management Graduate Program, Christian Brothers University, 650 East Parkway South, Memphis, TN, USA
 
2. Creator Author's name, affiliation, country Divya Kondeti; Engineering Management Graduate Program, Christian Brothers University, 650 East Parkway South, Memphis, TN, USA
 
2. Creator Author's name, affiliation, country (doi: 10.23953/cloud.ijapt.21)
 
3. Subject Discipline(s)
 
3. Subject Keyword(s) Artificial Neural Network; Compression Strength; McKee Formula; Corrugated Boxes
 
4. Description Abstract

McKee formula has been widely used to predict the compression strength of corrugated boxes. An experimental verification, published in early 2015, showed the inaccuracy of the formula. McKee formula left out several important factors, including box height, temperature, and humidity. An artificial neural network, CBU-BOX1, was developed based on 74 cases of cubical RSC single-wall corrugated boxes from 3”x3”x3” to 36”x36”x36”. Box height, temperature and humidity data were included in the network development. CBU-BOX1 performance ranged from 0% to 26.3% error with an average error of 6.9% while McKee formula performance ranged from 0.6% to 149.3% error with an average error of 28.7%. However, CBU-BOX1 performance dropped significantly when it was used for rectangular boxes. Out from twelve test cases of rectangular boxes, the formula resulted in an average error of 25.7% while CBU-BOX1 resulted in 34.8%. Thus, the network is unacceptable for rectangular boxes. In this study, 67 more cases were added to the previous 74 cases. Out of 141 cases, 43 were rectangular boxes. CBU-BOX2 significantly outperformed McKee formula with an average error of 9.21% versus 30.79%.
 
5. Publisher Organizing agency, location
 
6. Contributor Sponsor(s)
 
7. Date (YYYY-MM-DD) 2016-01-29
 
8. Type Status & genre Peer-reviewed Article
 
8. Type Type
 
9. Format File format PDF
 
10. Identifier Uniform Resource Identifier http://technical.cloud-journals.com/index.php/IJAPT/article/view/Tech-555
 
10. Identifier Digital Object Identifier 10.23953/Tech-555
 
11. Source Journal/conference title; vol., no. (year) International Journal of Advanced Packaging Technology; Vol 4, No 1 (2016)
 
12. Language English=en en
 
14. Coverage Geo-spatial location, chronological period, research sample (gender, age, etc.)
 
15. Rights Copyright and permissions

Copyright Terms & Conditions

Authors who publish with this journal agree to the following terms:

a. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

b. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

c. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work

Cloud Publications reserves the right to amend/change the copyright policy; with/without notice.