Hybrid sparse convolutional neural networks for predicting manufacturability of visual defects of laser powder bed fusion processes
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Hybrid sparse convolutional neural networks for predicting manufacturability of visual defects of laser powder bed fusion processes
Authors
Keywords
Manufacturability, Machine learning, Additive manufacturing, Laser powder bed fusion
Journal
JOURNAL OF MANUFACTURING SYSTEMS
Volume -, Issue -, Pages -
Publisher
Elsevier BV
Online
2021-07-09
DOI
10.1016/j.jmsy.2021.07.002
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Toward in-situ flaw detection in laser powder bed fusion additive manufacturing through layerwise imagery and machine learning
- (2021) Zackary Snow et al. JOURNAL OF MANUFACTURING SYSTEMS
- Controlling the Properties of Additively Manufactured Cellular Structures Using Machine Learning Approaches
- (2020) Hany Hassanin et al. ADVANCED ENGINEERING MATERIALS
- An experimental methodology to analyse the structural behaviour of FDM parts with variable process parameters
- (2020) Steffany N. Cerda-Avila et al. RAPID PROTOTYPING JOURNAL
- Deep learning–based stress prediction for bottom-up SLA 3D printing process
- (2019) Aditya Khadilkar et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Deep learning-based tensile strength prediction in fused deposition modeling
- (2019) Jianjing Zhang et al. COMPUTERS IN INDUSTRY
- Image analysis-based closed loop quality control for additive manufacturing with fused filament fabrication
- (2019) Chenang Liu et al. JOURNAL OF MANUFACTURING SYSTEMS
- Prediction of surface roughness in extrusion-based additive manufacturing with machine learning
- (2019) Zhixiong Li et al. ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
- Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives
- (2019) Xinbo Qi et al. Engineering
- Metal additive manufacturing in the commercial aviation industry: A review
- (2019) Annamaria Gisario et al. JOURNAL OF MANUFACTURING SYSTEMS
- How to integrate additive manufacturing technologies into manufacturing systems successfully: A perspective from the commercial vehicle industry
- (2019) Li Yi et al. JOURNAL OF MANUFACTURING SYSTEMS
- Multiscale topology optimization using neural network surrogate models
- (2018) Daniel A. White et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Prediction of residual stress profile and optimization of surface conditions induced by laser shock peening process using artificial neural networks
- (2018) M. Ayeb et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Deep Reinforcement Learning: A Brief Survey
- (2017) Kai Arulkumaran et al. IEEE SIGNAL PROCESSING MAGAZINE
- Deep learning
- (2015) Yann LeCun et al. NATURE
- On design for additive manufacturing: evaluating geometrical limitations
- (2015) Guido A. O. Adam et al. RAPID PROTOTYPING JOURNAL
- Dynamic Simulation of Soft Multimaterial 3D-Printed Objects
- (2014) Jonathan Hiller et al. Soft Robotics
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started