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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:

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

Showing 1-25 of 28 citing articles:

Machine learning and deep learning based predictive quality in manufacturing: a systematic review
Hasan Tercan, Tobias Meisen
Journal of Intelligent Manufacturing (2022) Vol. 33, Iss. 7, pp. 1879-1905
Open Access | Times Cited: 173
Deep reinforcement learning in smart manufacturing: A review and prospects
Chengxi Li, Pai Zheng, Yue Yin, et al.
CIRP journal of manufacturing science and technology (2022) Vol. 40, pp. 75-101
Closed Access | Times Cited: 148
Deep reinforcement learning in production systems: a systematic literature review
Marcel Panzer, Benedict Bender
International Journal of Production Research (2021) Vol. 60, Iss. 13, pp. 4316-4341
Open Access | Times Cited: 143
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
Multi-objective optimization of the textile manufacturing process using deep-Q-network based multi-agent reinforcement learning
Zhenglei He, Kim Phuc Tran, Sébastien Thomassey, et al.
Journal of Manufacturing Systems (2021) Vol. 62, pp. 939-949
Open Access | Times Cited: 55
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
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 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 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
The Effectiveness of Using Augmented Reality on the Geometry Thinking Ability of Junior High School Students
Heni Pujiastuti, Rudi Haryadi
Procedia Computer Science (2024) Vol. 234, pp. 1738-1745
Open Access | Times Cited: 3
A new design guideline development strategy for aluminium alloy corners formed through cold and hot stamping processes
Hamid Reza Attar, Nan Li, Alistair Foster
Materials & Design (2021) Vol. 207, pp. 109856-109856
Open Access | Times Cited: 19
Machine learning and simulation-based surrogate modeling for improved process chain operation
André Hürkamp, Sebastian Gellrich, Antal Dér, et al.
The International Journal of Advanced Manufacturing Technology (2021) Vol. 117, Iss. 7-8, pp. 2297-2307
Open Access | Times Cited: 16
Preforming of non-crimp fabrics with distributed magnetic clamping and Bayesian optimisation
Rajan Jagpal, Evangelos Evangelou, Richard Butler, et al.
Journal of Composite Materials (2022) Vol. 56, Iss. 18, pp. 2835-2854
Open Access | Times Cited: 9
Optimisation of intra-ply stitch removal for improved formability of biaxial non-crimp fabrics
Shuai Chen, Adam M. Joesbury, F. Richard Yu, et al.
Composites Part B Engineering (2021) Vol. 229, pp. 109464-109464
Open Access | Times Cited: 12
A novel heuristic approach to detect induced forming defects using point cloud scans
Muhammad Shahrukh Saeed, Sheharyar Faisal, Boris Eisenbart, et al.
Proceedings of the Design Society (2024) Vol. 4, pp. 723-734
Open Access | Times Cited: 1
Springback prediction for sheet metal cold stamping using convolutional neural networks
Lei Zhu, Nan Li
(2023), pp. 38-38
Closed Access | Times Cited: 3
Kinematic draping simulation optimization of a composite B-pillar geometry using particle swarm optimization
Ricardo Fitas, Stefan Hesseler, Santino Wist, et al.
Heliyon (2022) Vol. 8, Iss. 11, pp. e11525-e11525
Open Access | Times Cited: 5
Implicit Neural Representations of Sheet Stamping Geometries with Small-Scale Features
Hamid Reza Attar, Alistair Foster, Nan Li
Engineering Applications of Artificial Intelligence (2023) Vol. 123, pp. 106482-106482
Open Access | Times Cited: 2
Deep neural networks as surrogate models for time-efficient manufacturing process optimisation
Clemens Zimmerling, Patrick Schindler, Julian Seuffert, et al.
ESAFORM 2021 (2021)
Open Access | Times Cited: 5
Profit-Driven Methodology for Servo Press Motion Selection under Material Variability
Nozomu Okuda, Luke Mohr, Hyun Ok Kim, et al.
Applied Sciences (2021) Vol. 11, Iss. 20, pp. 9530-9530
Open Access | Times Cited: 5
Combining simulation methods and machine learning for efficient forming process development
Clemens Zimmerling, Luise Kärger
Elsevier eBooks (2024), pp. 577-605
Closed Access
Increasing the prediction quality of artificial neural network–based optimisation approaches for the sheet moulding compound compression moulding process by means of targeted expansion of training data
Christian Hopmann, Marie Hadenfeldt, Jonas Neuhaus, et al.
The International Journal of Advanced Manufacturing Technology (2024)
Open Access
Forming process optimisation for variable geometries by machine learning – Convergence analysis and assessment
Clemens Zimmerling
Materials research proceedings (2023)
Open Access | Times Cited: 1
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