Challenges and opportunities in additive manufacturing polymer technology: a review based on optimization perspective

S Raja, A John Rajan - Advances in Polymer Technology, 2023 - Wiley Online Library
In the emerging modern technology of additive manufacturing, the need for optimization can
be found in literature in many places. Additive manufacturing (AM) is making an object layer …

Machine learning to enhance sustainable plastics: A review

C Guarda, J Caseiro, A Pires - Journal of Cleaner Production, 2024 - Elsevier
Plastic pollution requires advances in the production, use, and recovery of plastics to
minimize environmental and human-health impacts. Machine Learning (ML) has been …

Machine learning-driven prediction of tensile strength in 3D-printed PLA parts

MH Nikzad, M Heidari-Rarani, R Rasti… - Expert Systems with …, 2025 - Elsevier
Additive manufacturing (AM) has become a transformative technology in modern production,
enabling complex geometric designs with minimal material waste. A significant aspect of …

Machine learning-based predictive model for tensile and flexural strength of 3D-printed concrete

A Ali, RD Riaz, UJ Malik, SB Abbas, M Usman… - Materials, 2023 - mdpi.com
The additive manufacturing of concrete, also known as 3D-printed concrete, is produced
layer by layer using a 3D printer. The three-dimensional printing of concrete offers several …

A novel systematically optimized tabular neural network (TabNet) algorithm for predicting the tensile modulus of additively manufactured PLA parts

MH Nikzad, M Heidari-Rarani, R Rasti - Materials Today Communications, 2024 - Elsevier
Assessing the elastic modulus of 3D-printed polylactic acid (PLA) components is essential
for understanding their stiffness and load capacity, which are crucial for predicting product …

[HTML][HTML] Optimization of 4D/3D printing via machine learning: A systematic review

YA Alli, H Anuar, MR Manshor, CE Okafor… - Hybrid Advances, 2024 - Elsevier
This systematic review explores the integration of 4D/3D printing technologies with machine
learning, shaping a new era of manufacturing innovation. The analysis covers a wide range …

Machine learning algorithms to optimize the properties of bio-based poly (butylene succinate-co-butylene adipate) nanocomposites with carbon nanotubes

E Champa-Bujaico, AM Díez-Pascual… - Industrial Crops and …, 2024 - Elsevier
Abstract Poly [(butylene succinate)-co-adipate](PBSA)-based materials are gathering much
attention in the packaging industry, agriculture, and other fields owed to their …

[HTML][HTML] A Review of AI for optimization of 3D Printing of Sustainable Polymers and Composites

M Hassan, M Misra, GW Taylor, AK Mohanty - Composites Part C: Open …, 2024 - Elsevier
In recent years, 3D printing has experienced significant growth in the manufacturing sector
due to its ability to produce intricate and customized components. The advent of Industry 4.0 …

Machine learning-assisted prediction modeling for anisotropic flexural strength variations in fused filament fabrication of graphene reinforced poly-lactic acid …

T Raj, A Tiwary, A Jain, GS Sharma… - Progress in Additive …, 2024 - Springer
The objective of this study is to conduct a comparative analysis of various machine learning
algorithms on the flexural properties of graphene-reinforced poly-lactic acid fabricated …

Three-dimensional printing quality inspection based on transfer learning with convolutional neural networks

CJ Yang, WK Huang, KP Lin - Sensors, 2023 - mdpi.com
Fused deposition modeling (FDM) is a form of additive manufacturing where three-
dimensional (3D) models are created by depositing melted thermoplastic polymer filaments …