Mechanical response of additively manufactured foam: A machine learning approach-Reference-Cited by-同舟云学术

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Mechanical response of additively manufactured foam: A machine learning approach

Published:2022-12 Issue: Volume:16 Page:100801
ISSN:2590-1230
Container-title:Results in Engineering
language:en
Short-container-title:Results in Engineering

Author:

Neelam Rajat,Kulkarni Shrirang Ambaji,Bharath H.S.,Powar Satvasheel^ORCID,Doddamani Mrityunjay

Publisher

Elsevier BV

Subject

General Engineering

Reference56 articles.

1. Dynamic mechanical analysis of 3D printed eco-friendly lightweight composite;Doddamani;Compos. Commun.,2020

2. Compressive behavior of fly ash based 3D printed syntactic foam composite;Patil;Mater. Lett.,2019

3. Fuzzy logic based expert system for prediction of tensile strength in Fused Filament Fabrication (FFF) process;Trivedi;Mater. Today Proc.,2021

4. Prediction of the tensile strength of polylactic acid fused deposition models using artificial neural network technique;Pazhamannil;Mater. Today Proc.,2021

5. Experimental optimization of fused deposition modelling processing parameters: a design-for-manufacturing approach;Qattawi;Procedia Manuf.,2017

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1. Machine learning investigation of polylactic acid bead foam extrusion;Journal of Applied Polymer Science;2024-05-13

2. Multi-scale energy homogenization for 3D printed microstructures with a Diritchlet boundary condition relaxation under plastic deformation;Results in Engineering;2024-03

3. Development and characterization of short glass fiber reinforced-waste plastic composite filaments for 3D printing applications;Heliyon;2023-11

4. A High-Generalizability Machine Learning Framework for Analyzing the Homogenized Properties of Short Fiber-Reinforced Polymer Composites;Polymers;2023-09-30

5. Predicting light-matter interaction in semi-transparent elliptical packed beds using hybrid deep learning (HDL) approach;Results in Engineering;2023-09

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