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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A machine learning assisted approach for textile formability assessment and design improvement of composite components
Clemens Zimmerling, Dominik Dörr, Frank Henning, et al.
Composites Part A Applied Science and Manufacturing (2019) Vol. 124, pp. 105459-105459
Open Access | Times Cited: 45

Showing 1-25 of 45 citing articles:

Prediction of maximum tensile stress in plain-weave composite laminates with interacting holes via stacked machine learning algorithms: A comparative study
Faramarz Bagherzadeh, Torkan Shafighfard, Raja Muhammad Awais Khan, et al.
Mechanical Systems and Signal Processing (2023) Vol. 195, pp. 110315-110315
Closed Access | Times Cited: 66
Machine learning for polymer composites process simulation – a review
Stefano Cassola, Miro Duhovic, Tim Schmidt, et al.
Composites Part B Engineering (2022) Vol. 246, pp. 110208-110208
Closed Access | Times Cited: 60
Machine learning-based heat deflection temperature prediction and effect analysis in polypropylene composites using catboost and shapley additive explanations
Chonghyo Joo, Hyundo Park, Jongkoo Lim, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 126, pp. 106873-106873
Closed Access | Times Cited: 32
Making costly manufacturing smart with transfer learning under limited data: A case study on composites autoclave processing
Milad Ramezankhani, Bryn Crawford, Apurva Narayan, et al.
Journal of Manufacturing Systems (2021) Vol. 59, pp. 345-354
Closed Access | Times Cited: 52
Classification of Textile Polymer Composites: Recent Trends and Challenges
Nesrine Amor, Muhammad Tayyab Noman, Michal Petrů
Polymers (2021) Vol. 13, Iss. 16, pp. 2592-2592
Open Access | Times Cited: 44
Optimisation of manufacturing process parameters for variable component geometries using reinforcement learning
Clemens Zimmerling, Christian Poppe, Oliver Stein, et al.
Materials & Design (2022) Vol. 214, pp. 110423-110423
Open Access | Times Cited: 28
Rapidly predicting the effect of tool geometry on the wrinkling of biaxial NCFs during composites manufacturing using a deep learning surrogate model
J.V. Viisainen, F. Richard Yu, A. Codolini, et al.
Composites Part B Engineering (2023) Vol. 253, pp. 110536-110536
Open Access | Times Cited: 19
Advancements of machine learning techniques in fiber-filled polymer composites: a review
R. Alagulakshmi, R. Ramalakshmi, V. Arumugaprabu, et al.
Polymer Bulletin (2025)
Closed Access
Application of deep residual networks to predict the effective properties of fiber-reinforced composites with voids
Mahdi Karimian, S.A. Hosseini Kordkheili
Advances in Mechanical Engineering (2025) Vol. 17, Iss. 1
Open Access
Towards the automation of woven fabric draping via reinforcement learning and Extended Position Based Dynamics
Patrick M. Blies, Sophia Keller, Ulrich Kuenzer, et al.
Journal of Manufacturing Processes (2025) Vol. 141, pp. 336-350
Open Access
Data-driven ergonomic risk assessment of complex hand-intensive manufacturing processes
A.S. Anjana Krishnan, Xingjian Yang, Utsav Seth, et al.
Communications Engineering (2025) Vol. 4, Iss. 1
Open Access
A comparison between robust design and digital twin approaches for Non-Crimp fabric (NCF) forming
Siyuan Chen, Adam J. Thompson, Tim Dodwell, et al.
Composites Part A Applied Science and Manufacturing (2025), pp. 108864-108864
Open Access
Composition optimization of a high-performance epoxy resin based on molecular dynamics and machine learning
Kai Jin, Hao Luo, Ziyu Wang, et al.
Materials & Design (2020) Vol. 194, pp. 108932-108932
Open Access | Times Cited: 46
Application of deep neural network learning in composites design
Yinli Wang, Constantinos Soutis, Daisuke Ando, et al.
European Journal of Materials (2022) Vol. 2, Iss. 1, pp. 117-170
Open Access | Times Cited: 26
Fast optimisation of the formability of dry fabric preforms: A Bayesian approach
Siyuan Chen, Adam J. Thompson, Tim Dodwell, et al.
Materials & Design (2023) Vol. 230, pp. 111986-111986
Open Access | Times Cited: 13
Parametric study on the effect of material properties, tool geometry, and tolerances on preform quality in wind turbine blade manufacturing
Peter H. Broberg, Esben Lindgaard, Adam J. Thompson, et al.
Composite Structures (2024) Vol. 344, pp. 118324-118324
Open Access | Times Cited: 4
A Review of Machine Learning for Progressive Damage Modelling of Fiber-Reinforced Composites
Jimbay Loh, Kirk Ming Yeoh, Karthikayen Raju, et al.
Applied Composite Materials (2024)
Closed Access | Times Cited: 4
A comprehensive review of characterization and simulation methods for thermo-stamping of 2D woven fabric reinforced thermoplastics
Youkun Gong, Zengrui Song, Huiming Ning, et al.
Composites Part B Engineering (2020) Vol. 203, pp. 108462-108462
Closed Access | Times Cited: 38
A Study on Using Image-Based Machine Learning Methods to Develop Surrogate Models of Stamp Forming Simulations
Haosu Zhou, Qingfeng Xu, Zhenguo Nie, et al.
Journal of Manufacturing Science and Engineering (2021) Vol. 144, Iss. 2
Open Access | Times Cited: 31
Process models: A cornerstone to composites 4.0
Jonathan P.-H. Belnoue, Stephen R. Hallett
Composites Part B Engineering (2024) Vol. 283, pp. 111621-111621
Open Access | Times Cited: 3
But how can I optimise my high-dimensional problem with only very little data? – A composite manufacturing application
Siyuan Chen, Adam J. Thompson, Tim Dodwell, et al.
International Journal of Solids and Structures (2024) Vol. 300, pp. 112941-112941
Open Access | Times Cited: 3
Prediction of fatigue life of laminated composites by integrating artificial neural network model and non-dominated sorting genetic algorithm
A.H. Mirzaei, Parisa Haghi, M.M. Shokrieh
International Journal of Fatigue (2024) Vol. 188, pp. 108528-108528
Closed Access | Times Cited: 3
Estimating Optimum Process Parameters in Textile Draping of Variable Part Geometries - A Reinforcement Learning Approach
Clemens Zimmerling, Christian Poppe, Luise Kärger
Procedia Manufacturing (2020) Vol. 47, pp. 847-854
Open Access | Times Cited: 28
Predicting the effect of voids generated during RTM on the low-velocity impact behaviour by machine learning-based surrogate models
Julen Mendikute, M. Baskaran, Iñigo Llavori, et al.
Composites Part B Engineering (2023) Vol. 260, pp. 110790-110790
Closed Access | Times Cited: 9
Preforming characteristics and defect mitigation strategies for multi-layered biaxial pillar-stitched non-crimp fabric
Ming Mei, Yujia He, Kai Wei, et al.
International Journal of Solids and Structures (2023) Vol. 267, pp. 112150-112150
Closed Access | Times Cited: 8
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