Page 39 - eBook_Proceedings of the International Conference on Digital Manufacturing V2

Collaborative Study on Weight Optimisation of Lubricant Bottles under
Stacking Condition using Finite Element Analysis and Machine Learning

de Queiroz, J. P. T. P., Silveira, A. C. de F., de Camargo, F. V., &
Vaccari, M. (2018). A brief review on palletized products
transportation technology. FME Transactions, 46, 101–107.
De Iuliis, M., Miceli, E., & Castaldo, P. (2024). Machine learning
modelling of structural response for different seismic signal
characteristics: A parametric analysis. Applied Soft
Computing, 164, 112026.
Demircioğlu, U., Bakır, H., & Çakır, M. T. (2024). Machine
learning analysis of data obtained by finite element method:
A new approach in structural design. Physica Scripta, 99.
DiRaddo, R. W., & Garcia‐Rejon, A. (1994). Experimental and
theoretical investigation of the inflation of variable thickness
parisons. Polymer Engineering & Science, 34(13), 1080-
1089.
Gao, Y. (2012). Research of plastic extrusion blow molding
parison controllers. In Applied Mechanics and Materials (pp.
1294–1297).
Guo, K., Yang, Z., Yu, C. H., & Buehler, M. J. (2021). Artificial
intelligence and machine learning in design of mechanical
materials. Materials Horizons, 8, 1153–1172.
Hashemi, A., Jang, J., & Beheshti, J. (2023). A machine learning-
based surrogate finite element model for estimating dynamic
response of mechanical systems. IEEE Access, 11, 54509–
54525.
Hu, Q., Sha, W., Li, Y., & Wang, Y. (2012). Structural
optimisation and lightweight design of PET bottle based on
Abaqus. In Advanced Materials Research (pp. 558–563).
Huang, H. H., Chen, L. W., Lu, W. H., Lin, W. C., & Chen, Y. C.
(2018). Design and simulation analysis of lightweight HDPE
milk bottle. Polymers and Polymer Composites, 26(1), 91-
98.
Hussain, A., Sakhaei, A. H., & Shafiee, M. (2024). Machine
learning-based constitutive modelling for material non-
linearity: A review. Mechanics of Advanced Materials and
Structures.
Kadiyala, A., & Kumar, A. (2018). Applications of python to
evaluate the performance of bagging methods.
Environmental Progress and Sustainable Energy, 37, 1555–
1559.

23