@article{ULLRICH202440,
title = {AI-based optimisation of total machining performance: A review},
journal = {CIRP Journal of Manufacturing Science and Technology},
volume = {50},
pages = {40-54},
year = {2024},
issn = {1755-5817},
doi = {https://doi.org/10.1016/j.cirpj.2024.01.012},
url = {https://www.sciencedirect.com/science/article/pii/S1755581724000105},
author = {Katrin Ullrich and Magnus {von Elling} and Kevin Gutzeit and Martin Dix and Matthias Weigold and Jan C. Aurich and Rafael Wertheim and I.S. Jawahir and Hassan Ghadbeigi},
keywords = {Artificial intelligence, Multi-objective optimisation, Machining performance}
}

@article{liu2025prediction,
  title={Prediction of cutting force via machine learning: state of the art, challenges and potentials},
  author={Liu, Meng and Xie, Hui and Pan, Wencheng and Ding, Songlin and Li, Guangxian},
  journal={Journal of Intelligent Manufacturing},
  volume={36},
  number={2},
  pages={703--764},
  year={2025},
  publisher={Springer}
}


@article{LAAKSO2024333,
title = {Hybrid FE-ML model for turning of 42CrMo4 steel},
journal = {CIRP Journal of Manufacturing Science and Technology},
volume = {55},
pages = {333-346},
year = {2024},
issn = {1755-5817},
doi = {https://doi.org/10.1016/j.cirpj.2024.10.003},
url = {https://www.sciencedirect.com/science/article/pii/S1755581724001597},
author = {Sampsa Vili Antero Laakso and Andrey Mityakov and Tom Niinimäki and Kandice Suane Barros Ribeiro and Wallace Moreira Bessa},
keywords = {Hybrid modelling, FEM, CEL, Machine learning, Deep neural networks, Machining, Turning, Cutting force, Simulation, 42CrMo4 steel, Cubic boron nitride},
abstract = {Metal cutting processes contribute significant share of the added value of industrial products. The need for machining has grown exponentially with increasing demands for quality and accuracy, and despite of more than a century of research in the field, there are no reliable and accurate models that describe all the physical phenomena needed to optimize the machining processes. The scientific community has begun to explore hybrid methods instead of expanding the capabilities of individual modelling schemes, which has been more efficient than efficacious direction. Following this trend, we propose a hybrid finite element — machine learning method (FEML) for modelling metal cutting. The advantages of the FEML method are reduced need for experimental data, reduced computational time and improved prediction accuracy. This paper describes the FEML model, which uses a Coupled Eulerian Lagrangian (CEL) formulation and deep neural networks (DNN) from the TensorFlow Python library. The machining experiments include forces, chip morphology and surface roughness. The experimental data was divided into training dataset and validation dataset to confirm the model predictions outside the experimental data range. The hybrid FEML model outperformed the DNN and FEM models independently, by reducing the computational time, improving the average prediction error from 23% to 13% and reduced the need for experimental data by half.}
}

@Article{jmmp8030107,
AUTHOR = {Reeber, Tim and Wolf, Jan and Möhring, Hans-Christian},
TITLE = {A Data-Driven Approach for Cutting Force Prediction in FEM Machining Simulations Using Gradient Boosted Machines},
JOURNAL = {Journal of Manufacturing and Materials Processing},
VOLUME = {8},
YEAR = {2024},
NUMBER = {3},
ARTICLE-NUMBER = {107},
URL = {https://www.mdpi.com/2504-4494/8/3/107},
ISSN = {2504-4494},
DOI = {10.3390/jmmp8030107}
}

@article{charalampous2021prediction,
  title={Prediction of cutting forces in milling using machine learning algorithms and finite element analysis},
  author={Charalampous, Paschalis},
  journal={Journal of Materials Engineering and Performance},
  volume={30},
  number={3},
  pages={2002--2013},
  year={2021},
  publisher={Springer}
}

@book{groover2010fundamentals,
  author    = {Groover, Mikell P.},
  title     = {Fundamentals of Modern Manufacturing: Materials, Processes, and Systems},
  year      = {2010},
  publisher = {Wiley \& Sons Canada, Limited, John},
  url       = {https://futureingscientist.files.wordpress.com/2014/01/fundamentals-of-modern-manufacturing-4th-edition-by-mikell-p-groover.pdf},
  note      = {Accessed on January 13, 2022}
}

@misc{fortunebusiness2023metal,
  author       = {{Fortune Business Insights}},
  title        = {Metal Cutting Tools Market Report},
  year         = {2023},
  month        = {May 5},
  url          = {https://www.fortunebusinessinsights.com/industry-reports/metal-cutting-tools-market-101751},
  note         = {Accessed on September 5, 2022}
}

@phdthesis{barbosa2014,
  author    = {Barbosa, P. A.},
  title     = {Study of the Mechanical Behavior in the Machining of Stainless Steels (in portuguese)},
  school    = {Polytechnic School of the University of São Paulo},
  year      = {2014},
  address   = {São Paulo},
  type      = {Ph.D. Thesis}
}

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  author    = {Yen, Y.-C. and Söhner, J. and Lilly, B. and Altan, T.},
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  journal   = {Journal of Materials Processing Technology},
  pages     = {82--91},
  year      = {2004}
}

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  author    = {Lee, E. H. and Shaffer, B. W.},
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}

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  author    = {Merchant, M. E.},
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  author    = {Merchant, M.},
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  author = {Xue, T. and Gac, Z. and Liao, S. and Cao, J.},
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}

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  booktitle = {Proceedings of the 37th International Conference on Machine Learning},
  year = {2020},
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  address = {Viena, Austria}
}

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  author = {Yusup, N. and Zain, A. M. and Hashim, S. Z.},
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@article{wang2022,
  author = {Wang, L. Z.},
  title = {Optimal control of renewable energy in buildings using the machine learning method},
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  year = {2022},
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}

@inproceedings{preez2019,
  author = {Preez, A. O.},
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  booktitle = {16th Global Conference on Sustainable Manufacturing - Sustainable Manufacturing for Global Circular Economy},
  pages = {810--817},
  year = {2019},
  publisher = {Elsevier G.V.}
}

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  author = {Norouzi, A. and others},
  title = {Machine Learning Integrated with Model Predictive Control for Imitative Optimal Control of Compression Ignition Engines},
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  pages = {19--26},
  year = {2022}
}

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  title = {Two decades of blackbox optimization applications},
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  volume = {100011},
  year = {2021},
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}

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  author = {Liao, T. and Jiang, F. and Guo, B. and Wang, F.},
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}

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  year = {2024}
}

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  journal = {CIRP Annals - Manufacturing Technology},
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}

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}

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@inproceedings{titu2021,
  author = {Titu, N. A. and Baucum, M. and No, T. and Trotsky, M. and Karandikar, J. and Schmitz, T. and Khojandia, A.},
  title = {Estimating Johnson–Cook Material Parameters using Neural Networks},
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  year = {2021},
  publisher = {Elsevier},
  address = {Ohio, USA}
}

@article{murugesan2019,
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}

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@article{ammari2023,
  author = {Ammari, B. J.},
  title = {Linear model decision trees as surrogates in optimization of engineering applications},
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}

@article{turja2024,
  author = {Turja, A. H.},
  title = {Multi-objective performance optimization \& thermodynamic analysis of solar powered supercritical CO2 power cycles using machine learning methods \& genetic algorithm},
  journal = {Energy and AI},
  year = {2024},
  volume = {100327},
  doi = {10.1016/j.egyai.2024.100327}
}

@article{kim2024,
  author = {Kim, S. M.},
  title = {Multi-objective optimization and inverse design of complementary field-effect transistor using combined approach of machine learning and non-dominated sorting genetic algorithms for next-generation semiconductor devices},
  journal = {Engineering Applications of Artificial Intelligence},
  year = {2024},
  volume = {109064},
  doi = {10.1016/j.engappai.2024.109064}
}

@article{hao2022,
  author = {Hao, X. Z.},
  title = {Prediction of f-CaO content in cement clinker: A novel prediction method based on LightGBM and Bayesian optimization},
  journal = {Chemometrics and Intelligent Laboratory Systems},
  year = {2022},
  volume = {104461},
  doi = {10.1016/j.chemolab.2022.104461}
}

@article{alshboul2024,
  author = {Alshboul, O. ,.-S.},
  title = {A comparative study of LightGBM, XGBoost, and GEP models in shear strength management of SFRC-SBWS},
  journal = {Structures},
  year = {2024},
  volume = {106009},
  doi = {10.1016/j.istruc.2024.106009}
}

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  author = {Mišić, V.},
  title = {Optimization of Tree Ensembles},
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}

@article{mistry2020,
  author = {Mistry, M. L.},
  title = {Mixed-Integer Convex Nonlinear Optimization with Gradient-Boosted Trees Embedded},
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  year = {2020},
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  doi = {10.1287/ijoc.2020.0946}
}

@article{thebelt2021,
  author = {Thebelt, A. K.-M.},
  title = {ENTMOOT: A framework for optimization over ensemble tree models},
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}

@inproceedings{bitsch2022,
  author = {Bitsch, G. S.},
  title = {Selection of optimal machine learning algorithm for autonomous guided vehicle’s control in a smart manufacturing environment},
  booktitle = {55th CIRP Conference on Manufacturing Systems},
  pages = {1409-1414},
  year = {2022},
  publisher = {Elsevier B.V.},
  address = {Reutlingen, Germany}
}

@article{morosohk2024,
  author = {Morosohk, S. W.},
  title = {Optimal control of the electron temperature profile in DIII-D using machine learning surrogate models},
  journal = {Fusion Engineering and Design},
  year = {2024},
  pages = {114615},
  doi = {10.1016/j.fusengdes.2024.114615}
}

@inproceedings{briceno2022,
  author = {Briceno-Mena, L. A.},
  title = {Machine Learning-Based Surrogate Models and Transfer Learning for Derivative Free Optimization of HTPEM Fuel Cells},
  booktitle = {PROCEEDINGS OF THE 32nd European Symposium on Computer Aided Process Engineering},
  pages = {1537-1542},
  year = {2022},
  publisher = {Elsevier B.V.},
  address = {Toulouse, France}
}

@inproceedings{weinan2022,
  author = {Weinan, E. H.},
  title = {Empowering Optimal Control with Machine Learning: A Perspective from Model Predictive Control},
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  pages = {121-126},
  year = {2022},
  publisher = {Elsevier}
}

@article{song2024,
  author = {Song, Y. C.},
  title = {Digital twins based on machine learning for optimal control of chemical looping hydrogen generation processes},
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  year = {2024},
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}

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@INPROCEEDINGS{xiubling2009,
  author={Xiubing Jing and Dawei Zhang and Zhongzhou Wang and Guobin Li},
  booktitle={2009 International Conference on Mechatronics and Automation}, 
  title={Investigations of tool geometry in ultraprecision cutting: A FEM simulation approach}, 
  year={2009},
  volume={},
  number={},
  pages={5099-5104},
  keywords={Geometry;Solid modeling;FEM;Rake angle;Clearance angle;Tool edge radius},
  doi={10.1109/ICMA.2009.5246149}}

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  author = {Rodrigues, A. R. and Coelho, R. T.},
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  author = {Orlando, D. and Rodriguez, N. and Consalter, L. A.},
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}

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  volume = {13},
  number = {19},
  pages = {4242},
  year = {2020}
}

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  year = {2020}
}

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@article{Singh2007,
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title = {Pulsed electrodeposition and characterization of HAp-Ta2O5 composite coating on NiTi for orthopedic applications},
journal = {Journal of Materials Research and Technology},
volume = {37},
pages = {1-11},
year = {2025},
issn = {2238-7854},
doi = {https://doi.org/10.1016/j.jmrt.2025.05.254},
url = {https://www.sciencedirect.com/science/article/pii/S2238785425014103},
author = {Sima {Mohammadi Kahnamouei} and Mir Saman Safavi and Jafar Khalil-Allaf}
}

@article{blank2020pymoo,
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  pages        = {89497--89509},
  year         = {2020},
  doi          = {10.1109/ACCESS.2020.2990567},
  publisher    = {IEEE}
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