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Towards selective laser sintering of objects with customized mechanical properties based on ANFIS predictions

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Abstract

Recently, the adaptive network-based fuzzy inference system (ANFIS) has been used extensively in modeling of manufacturing processes to save both optimization time and manufacturing costs. ANFIS is a powerful iterative tool for optimizing non-linear and multivariable manufacturing operations. In the present study, ANFIS is used to predict the optimum manufacturing parameters in selective laser sintering (SLS) of cement-filled polyamide 12 (PA12) composite. For this purpose, a set of cement-filled PA12 test specimens is manufactured by SLS technique with 8 different values of laser power (4.5–8 Watt) and 8 different weight fractions of white cement (5 %–40 %). Mechanical characterization of cement-filled PA12 is carried out to evaluate the ultimate tensile strength (UTS), compressive strength, and flexural properties. The experimental data are then divided into two groups; one group for training the ANFIS model and the other group for checking the validity of the identified model. The built ANFIS model was validated experimentally and comparison with experimental results revealed mean relative errors of 2.92 %, 3.84 %, 4.75 %, and 3.31 % in the predictions of UTS, compressive strength, flexural modulus, and flexural yield strength, respectively.

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Acknowledgments

Manufacturing of test specimens and mechanical characterization were carried out at the Manufacturing Engineering Centre in Cardiff University (UK); the authors are grateful to the sincere efforts of Professor Duc Pham throughout this research work. And the authors would like to thank the Deanship of Scientific Research at Majmaah University for supporting this research under Project No. R-1441-145.

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Authors and Affiliations

  1. Department of Mechanical and Industrial Engineering, College of Engineering, Majmaah University, Al-Majmaah, Riyadh, 11952, Saudi Arabia

    Saleh A. Aldahash & Abdelrasoul M. Gadelmoula

  2. Department of Electromechanical Engineering, Abu Dhabi Polytechnic, Abu Dhabi, 111499, United Arab Emirates

    Shaaban A. Salman

  3. Department of Mechanical Engineering, Faculty of Engineering, Assiut University, Assiut, 71515, Egypt

    Shaaban A. Salman & Abdelrasoul M. Gadelmoula

Authors
  1. Saleh A. Aldahash
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  2. Shaaban A. Salman
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  3. Abdelrasoul M. Gadelmoula
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Correspondence to Abdelrasoul M. Gadelmoula.

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The authors declare that they have no conflict of interest.

Saleh Aldahash is an Assistant Professor in the Department of Mechanical and Industrial Engineering, Majmaah University, Saudi Arabia. He received his Ph.D. in Manufacturing Engineering from University of Cardiff, UK. His research interests include modern manufacturing, computer-integrated manufacturing, 3D printing and rapid prototyping, prosthetic rehabilitation. He has published couple of research articles related to these topics.

Shaaban Ali has completed his Ph.D. from the Australian Defense Force Academy, UNSW@ADFA, Australia. He worked as an Assistant Professor at the Mechanical Engineering Department, Assiut University. Currently he is a faculty member in Electromechanical Department, Abu Dhabi Polytechnic. His research involved intelligent adaptive control, UAV, identification, and control.

Abdelrasoul Gadelmoula is an Associate Professor at the Department of Mechanical and Industrial Engineering, Majmaah University, Saudi Arabia. He received his Ph.D. in Mechanical Engineering from Yonsei University, South Korea. Also, he received the JSPS fellowship at the University of Tokyo. His research interests include additive manufacturing, numerical simulations in fluid-structure interaction, flow-induced vibrations, and tribology. Dr. Gadelmoula has published many research articles in reputable journals related to these subjects.

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Aldahash, S.A., Salman, S.A. & Gadelmoula, A.M. Towards selective laser sintering of objects with customized mechanical properties based on ANFIS predictions. J Mech Sci Technol 34, 5075–5084 (2020). https://doi.org/10.1007/s12206-020-1111-6

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  • DOI: https://doi.org/10.1007/s12206-020-1111-6

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