References
- Frank, B., Corrugated Box Compression-A Literature Survey. Packaging Technology and Science 2014. 27(2): 105.
- Kellicutt, K.Q., & Landt, E. F, Basic design data for the use of fiberboard in shipping containers. Fibre Containers, 1951. 36(12): 62-80.
- Batelka, J.J. and C.N. Smith, Package compression model. Project 3746, final report to the Containerboard and Kraft Paper Group of the American Forest and Paper Association. 1993.
- Nordstrand, T., Analysis and testing of corrugated board panels into the post-buckling regime. Composite Structures, 2004. 63(2): 189-199. https://doi.org/10.1016/S0263-8223(03)00155-7
- Zhou, Y.H., B.Q. Zhong, and R.H. Guo. The multiple linear regression model on compression strength of corrugated boxes. in Applied Mechanics and Materials. 2012. Trans Tech Publ.
- Urbanik, T.J. and B. Frank, Box compression analysis of world-wide data spanning 46 years. Wood and fiber science, 2006: 399-416.
- Maltenfort, G., Revision of top to bottom compression equations for doublewall corrugated. Paperboard Packaging 48 (11),
- Coffin, D.W., Some observations towards improved predictive models for box compression strength. TAPPI J, 2015. 14: 537-545. https://doi.org/10.32964/TJ14.8.537
- McKee, R., J. Gander, and J. Wachuta, Compression strength formula for corrugated boxes. Paperboard packaging, 1963. 48(8): 149-159.
- Frank, B. and K. Kruger, Assessing variation in package modeling. TAPPI JOURNAL, 2021. 20(4): 231-238. https://doi.org/10.32964/TJ20.4.231
- Park, J., et al., Finite element-based simulation for edgewise compression behavior of corrugated paperboard for packaging of agricultural products. Applied Sciences, 2020. 10(19): 6716.
- Park, J., S. Chang, and H.M. Jung, Numerical prediction of equivalent mechanical properties of corrugated paperboard by 3D finite element analysis. Applied Sciences, 2020. 10(22): 7973.
- Park, J.-M., T.-Y. Park, and H.-M. Jung, Prediction of Deflection Due to Multistage Loading of a Corrugated Package. Applied Sciences, 2023. 13(7): 4236.
- Molina, E. and L. Horvath, Development of a Gaussian Process Model as a Surrogate to Study Load Bridging Performance in Racked Pallets. Applied Sciences, 2021. 11(24): 11865.
- Haj-Ali, R., et al., Refined nonlinear finite element models for corrugated fiberboards. Composite Structures, 2009. 87(4): 321-333. https://doi.org/10.1016/j.compstruct.2008.02.001
- Hua, G., et al., Experimental and numerical analysis of the edge effect for corrugated and honeycomb fiberboard. Strength of Materials, 2017. 49(1): 188-197. https://doi.org/10.1007/s11223-017-9857-5
- Garbowski, T., T. Gajewski, and J.K. Grabski, Estimation of the compressive strength of corrugated cardboard boxes with various perforations. Energies, 2021. 14(4): 1095.
- Jimenez-Caballero, M., et al. Design of different types of corrugated board packages using finite element tools. in SIMULIA customer conference. 2009.
- Garbowski, T., T. Gajewski, and J.K. Grabski, Estimation of the compressive strength of corrugated cardboard boxes with various openings. Energies, 2020. 14(1): 155.
- Han, J. and J.M. Park, Finite element analysis of vent/hand hole designs for corrugated fibreboard boxes. Packaging Technology and Science: An International Journal, 2007. 20(1): 39-47. https://doi.org/10.1002/pts.741
- Biancolini, M. and C. Brutti, Numerical and experimental investigation of the strength of corrugated board packages. Packaging Technology and Science: An International Journal, 2003. 16(2): 47-60. https://doi.org/10.1002/pts.609
- Garbowski, T., et al., Crushing of single-walled corrugated board during converting: Experimental and numerical study. Energies, 2021. 14(11): 3203.
- Marin, G., et al., Experimental and finite element simulated box compression tests on paperboard packages at different moisture levels. Packaging Technology and Science, 2021. 34(4): 229-243. https://doi.org/10.1002/pts.2554
- Kobayashi, T., Numerical Simulation for Compressive Strength of Corrugated Fiberboard Box. JAPAN TAPPI JOURNAL, 2019. 73(8): 793-800. https://doi.org/10.2524/jtappij.73.793
- Fadiji, T., et al., The efficacy of finite element analysis (FEA) as a design tool for food packaging: A review. Biosystems Engineering, 2018. 174: 20-40. https://doi.org/10.1016/j.biosystemseng.2018.06.015
- Kleene, S.C., Representation of events in nerve nets and finite automata. Automata studies, 1956. 34: 3-41. https://doi.org/10.1515/9781400882618-002
- Abiodun, O.I., et al., State-of-the-art in artificial neural network applications: A survey. Heliyon, 2018. 4(11): e00938.
- Mitic, V. Benefits of artificial intelligence and machine learning in marketing. in Sinteza 2019-International scientific conference on information technology and data related research. 2019. Singidunum University.
- Sun, A.Y. and B.R. Scanlon, How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions. Environmental Research Letters, 2019. 14(7): 073001.
- Pi, Y., Machine learning in governments: Benefits, challenges and future directions. JeDEM-eJournal of eDemocracy and Open Government, 2021. 13(1): 203-219. https://doi.org/10.29379/jedem.v13i1.625
- Kalogirou, S.A., Artificial neural networks in renewable energy systems applications: a review. Renewable and sustainable energy reviews, 2001. 5(4): 373-401. https://doi.org/10.1016/S1364-0321(01)00006-5
- Aylak, B.L., et al., Application of machine learning methods for pallet loading problem. Applied Sciences, 2021. 11(18): 8304.
- Garbowski T, Knitter-Piatkowska A, Grabski JK. Estimation of the Edge Crush Resistance of Corrugated Board Using Artificial Intelligence. Materials, 2023, 16, 1631.
- Lisboa, P.J., A review of evidence of health benefit from artificial neural networks in medical intervention. Neural networks, 2002. 15(1): 11-39. https://doi.org/10.1016/S0893-6080(01)00111-3
- Smith, J., Advances in neural networks and potential for their application to steel metallurgy. Materials Science and Technology, 2020. 36(17): 1805-1819. https://doi.org/10.1080/02670836.2020.1839206
- Sheikhtaheri, A., F. Sadoughi, and Z. Hashemi Dehaghi, Developing and using expert systems and neural networks in medicine: a review on benefits and challenges. Journal of medical systems, 2014. 38: 1-6. https://doi.org/10.1007/s10916-014-0110-5
- Adamopoulos, S., et al., Predicting the properties of corrugated base papers using multiple linear regression and artificial neural networks. Drewno: prace naukowe, doniesienia, komunikaty, 2016. 59.
- Malasri, S., P. Rayapati, and D. Kondeti, Predicting Corrugated Box Compression Strength Using an Artificial Neural Network. International Journal, 2016. 4(1): 169-176. https://doi.org/10.23953/cloud.ijapt.21
- Archaviboonyobul, T., et al., An analysis of the influence of hand hole and ventilation hole design on compressive strength of corrugated fiberboard boxes by an artificial neural network model. Packaging Technology and Science, 2020. 33(4-5): 171-181. https://doi.org/10.1002/pts.2495
- Goodfellow, I., Y. Bengio, and A. Courville, Deep learning. 2016: MIT press.
- Roodschild, M., J. Gotay Sardinas, and A. Will, A new approach for the vanishing gradient problem on sigmoid activation. Progress in Artificial Intelligence, 2020. 9(4): 351-360. https://doi.org/10.1007/s13748-020-00218-y
- Kubat, M. and M. Kubat, Artificial neural networks. An Introduction to Machine Learning, 2021: 117-143.
- Hang, H.M. and Y.M. Chou, Chapter 5 - Motion Estimation for Image Sequence Compression. This work was supported in part by the NSC grant 83-0408-E009012, in Handbook of Visual Communications, H.-M. Hang and J.W. Woods, Editors. 1995, Academic Press: San Diego. 147-188. ISBN: 9780123230508.
- Hyndman, Rob J. Moving Averages. International Encyclopedia of Statistical Science. 2nd ed.; Editors: Lovric, Miodrag. Publisher: Springer. (2011); 866-869.