Literature

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.

pdf Files

Using Artificial Intelligence in the Computer Aided Manufacturing of Friction Stir Welds. By Simon Smith, Mike Lewis and Stephan Kallee, 14 March 2022 (please click onto the image to open page 2)