Literature
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Several papers have been published related to the topics of this project. Some of them are shown below in reverse chronological order, i.e. the newest are listed at the top:
- Akshansh Mishra and Anish Dasgupta: Optimization of the Mechanical Property of Friction Stir Welded Heat Treatable Aluminum Alloy by using Bio-Inspired Artificial Intelligence Algorithms. In: Frattura ed Integrità Strutturale, 62 (2022) 448-459; DOI: 10.3221/IGF-ESIS.62.31. Published: 22 September 2022.
- Mike Lewis and Simon D. Smith: A Process Modelling Approach to the Development of Lap Welding Procedures, The 13th International Seminar "Numerical Analysis of Weldability", September 2022.
- Onur Aydin and Elif Gültürk: A Novel Approach to Improve Tensile Strength of Al/Mg Hybrid Friction Stir Welding Joint by Stochastic Optimization. In: Yıl 2022, Cilt 2, Sayı 1, 31 - 42. Published: 30 June 2022.
- Raheem Al-Sabur, Hassanein I. Khalaf, Aleksandra Świerczyńska, Grzegorz Rogalski and Hesamoddin Aghajani Derazkola: Effects of Noncontact Shoulder Tool Velocities on Friction Stir Joining of Polyamide 6 (PA6). Materials 2022, 15(12), 4214; https://doi.org/10.3390/ma15124214. Published: 14 June 2022.
- Utkarsh Chadha, Senthil Kumaran Selvaraj, Neha Gunreddy, Sanjay Babu, Swapnil Mishra, Deepesh Padala, M.Shashank, Rhea Mary Mathew, S. Ram Kishore ,Shraddhanjali Panigrahi, R. Nagalakshmi, R. Lokesh Kumar, and Addisalem Adefris: Survey of Machine Learning in Friction Stir Welding, including Unresolved Issues and Future Research Directions. In: Material Design & Processing Communications, Volume 2022, Article ID 2568347. Published: 8 Jun 2022.
- P. Rabe, A. Schiebahn and U. Reisgen (ISF Welding and Joining Institute, RWTH Aachen University, Pontstraße 49, Aachen 52062, Germany): Deep learning approaches for force feedback based void defect detection in friction stir welding. Journal of Advanced Joining Processes, Volume 5, June 2022, 100087, 18 December 2021.
- Arnold Wright, Troy R. Munro and Yuri Hovanski: Evaluating Temperature Control in Friction Stir Welding for Industrial Applications. 19 November 2021.
- Roman Hartl, Andreas Bachmann, Jan Bernd Habedank, Thomas Semmand and Michael F. Zaeh: Process Monitoring in Friction Stir Welding Using Convolutional Neural Networks. Metals 2021, 11, 535. https://doi.org/10.3390/met11040535. 5 March 2021 and Supplementary material.
- Mike Lewis and Simon D. Smith: The Development of FSW Process Modelling for Use by Process Engineers. In: Yuri Hovanski, Yutaka Sato, Piyush Upadhyay, Anton A. Naumov and Nilesh Kumar (The Minerals, Metals & Materials Society 2021): Friction Stir Welding and Processing XI. 17 February 2021.
- Hartl, R.; Vieltorf, F.; Zaeh, M. F.: Correlations between the Surface Topography and Mechanical Properties of Friction Stir Welds. Metals 10 (7), 2020, p. 890, https://doi.org/10.3390/met10070890
- Sigl, M. E.; Bachmann, A.; Mair, T.; Zaeh, Michael F.: Torque-Based Temperature Control in Friction Stir Welding by Using a Digital Twin. Metals 10 (7), 2020, p. 914, https://doi.org/10.3390/met10070914
- R. Hartl, J. Hansjakob and M. F. Zaeh: Improving the surface quality of friction stir welds using reinforcement learning and Bayesian optimization. Int J Adv Manuf Technol 110, 3145–3167 (2020). https://doi.org/10.1007/s00170-020-05696-x.
- Bachmann, A., Gigl, T., Hugenschmidt, C. P., & Zaeh, M. F. (2019). Characterization of the microstructure in friction stir welds of EN AW-2219 using coincident Doppler-broadening spectroscopy. Materials Characterization, 149, p. 143 – 152, https://doi.org/10.1016/j.matchar.2019.01.016
- R. Hartl, J. Landgraf, J. Spahl, A. Bachmann and M. F. Zaeh: Automated visual inspection of friction stir welds: a deep learning approach. In: Proc. SPIE 11059, Multimodal Sensing: Technologies and Applications, 21 June 2019, https://doi.org/10.1117/12.2525947.
- Hartl, R.; Vieltorf, F.; Benker, M.; Zaeh, M. F.: Predicting the Ultimate Tensile Strength of Friction Stir Welds Using Gaussian Process Regression. Journal of Manufacturing and Materials Processing 4 (3), 2020, p. 75, https://doi.org/10.3390/jmmp4030075
- R Hartl, A Bachmann, S Liebl, A Zens and M F Zaeh: Automated surface inspection of friction stir welds by means of structured light projection. IOP Conf. Series: Materials Science and Engineering 480 (2019) 012035, https://doi.org/10.1088/1757-899X/480/1/012035.
- Yongxian Huang, Long Wan, Xiangchen Meng, Yuming Xie, Zongliang Lv and Li Zhou: Probe shape design for eliminating the defects of friction stir lap weldeddissimilar materials. DOI: 10.1016/j.jmapro.2018.08.026.
- Shubham Verma, Meenu Gupta and Joy Prakash Misra: Performance evaluation of friction stir welding using machine learning approaches. MethodsX, Volume 5, 2018, Pages 1048-1058, https://doi.org/10.1016/j.mex.2018.09.002.
- Janusz Pikuła, Krzysztof Kwieciński, Grzegorz Porembski and Adam Pietras: FEM Simulation of the FSW Process of Heat Exchanger Components. January 2016, https://dx.doi.org/10.17729/ebis.2016.3/3.
- Janusz Pikuła, Krzysztof Kwieciński, Grzegorz Porembski and Adam Pietras: FEM Simulation of Check Valve Ball FSW Process. January 2014.
- Amr Elbanhawy, E. Chevallier, K. Domin: Numerical Investigations of Friction Stir Welding of High Temperature Materials. NAFEMS World Congress, Salzburg, Austria, 9-12 June 2013.
- Dwight Burford, Enkhsaikhan Boldsaikhan and Adam Wiley: Early Detection of Volumetric Defects Using e-NDE during Friction Stir Welding. 9th International Friction Stir Welding Symposium. The Von Braun Center, Huntsville, AL. 15-17 May 2012 (see also https://www.semanticscholar.org/paper/Early-Detection-of-Volumetric-Defects-Using-e-NDE-Burford/...).
- Dr. Simon D. Smith and Dr. Rajii Sarawat: Accurate thermo-mechanical modelling of friction stir welding using simple material data and commercial software. 2009.
- Stephan Kallee, Dave Nicholas, Haydn Powell and John Lawrence: Knowledge-base software package for friction stir welding. In: The proceedings of the 7th INALCO conference which was held at TWI, Cambridge in April 1998 - Joints in Aluminium - INALCO '98: Seventh International Conference, Woodhead Publishing, 14 October 1999.