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Year 2023, Volume: 11 Issue: 3, 409 - 421, 29.09.2023

Abstract

References

  • [1] Bozkurt E, Sarikoc A. (2008). Can The Virtual Laboratory Replace the Traditional Laboratory in Physics Education? Selcuk University, Faculty of Ahmet Keles Education Journal, 25:89-100 (in Turkish)
  • [2] Tiwari R, Singh K. (2011). Virtualization Of Engineering Discipline Experiments For An Internet-Based Remote Laboratory. Australasian Journal of Educational Technology, 27(4), 671-692
  • [3] Eckhoff EC, et al. (2002). Interactive Virtual Laboratory for Experience with a Smart Bridge Test. American Society for Engineering Education Annual Conference & Exposition; June 16-17 Montreal, Canada
  • [4] Budhu M. (2002). Virtual Laboratories For Engineering Education. International Conference on Engineering Education; Manchester, UK
  • [5] Rauf H.L., Shareef S.S., Ukabi E., (2019). Understanding the Relationship between Construction Courses and Design in Architectural Education. International Journal of Recent Technology and Engineering (IJRTE), ISSN: 2277-3878, Volume-8 Issue-3, September 2019
  • [6] Elfakki AO., Sghaier S., Alotaibi AA. (2023). An Efficient System Based on Experimental Laboratory in 3D Virtual Environment for Students with Learning Disabilities. Electronics February-2023, 12, 989. https://doi.org/10.3390/electronics12040989
  • [7] Kiraz A., Kubat C., Özbek YY., Uygun Ö., Eski H. (2014). A Web-Based Virtual Experiment in Material Science: Tensile Test Laboratory Application. Acta Physica Polonica A Journal, Vol (2)125:310-312, DOI: 10.12693/APhysPolA.125.310
  • [8] Falcó O., Ávilab RL., Tijsc B., Lopes CS. (2018). Modelling and simulation methodology for unidirectional composite laminates in a Virtual Test Lab framework. Composite Structures, 190 (2018) 137–159, https://doi.org/10.1016/j.compstruct.2018.02.016
  • [9] Zadeh LA. (1965). Fuzzy Sets, Information and Control. 8 (3):338-353
  • [10] Jang JSR, Sun CT. (1995). Neuro-Fuzzy Modeling and Control. Proceedings of the IEEE, 83 (3), 378-406
  • [11] Arabacıoğlu BC. (2005). Using Fuzzy Inference System for Architectural Space Analysis. Applied Soft Computing, 10 (3), 926-937
  • [12] Arabacıoğlu FP, Arabacıoğlu BC. (2011). Using Adaptive Neuro-Fuzzy Inference System (Anfis) On Design Studio Grade Estimation For Instructors’ Evaluation Performance Analyses, Advances in Fuzzy Sets and Systems, 9 (2) 93-110
  • [13] Arabacıoğlu FP, Arabacıoğlu BC. (2013). Design Studio Evaluation Discussions in Digital Age. The International Journal of Science Commerce and Humanities, 1 (8) 86-97
  • [14] Zadeh LA. (1994). Fuzzy Logic, Neural Networks And Soft Computing. Communications of the ACM, 37 (3):77-84
  • [15] Ross TJ. (2010). Development of Membership Functions. Fuzzy Logic with Engineering Applications, Third Edition, 174-210
  • [16] Dutta S. (1993). New faster Kernighan-Lin-type graph-partitioning algorithms. Proc. IEEE/ACM International Conference on CAD.
  • [17] Jang JSR. (1993). ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Trans. on Systems, Man, and Cybernetics, 23 (3), 665-685
  • [18] Kamali R, Binesh AR. (2013). A Comparison Of Neural Networks And Adaptive Neuro-Fuzzy Inference Systems For The Prediction Of Water Diffusion Through Carbon Nanotubes, Microfluidics and Nanofluidics, 14 (3-4), 575-581
  • [19] Palani S, Natarajan U, Chellamalai M. (2013). On-line prediction of micro-turning multi-response variables by machine vision system using adaptive neuro-fuzzy inference system (ANFIS), Mach. Vis. Appl. 24(1): 19-32
  • [20] Eckelman CA. (1998). The Withdrawal Strength of Screws From A Commercially Available Medium Density Fiberboard. For Prod, J, 38: 21–24
  • [21] American Society for Testing and Materials (2006). ASTM D1037-12, Standard Test Methods for Evaluating Properties of Wood Base Fiber and Particle Panel Materials, ASTM, Philadelphia Vol 04.10
  • [22] Gates JC. (2009). Screw withdrawal strength in 9wood’s assemblies, Test Evaluation Report. Oregon Wood Innovation Center, 9 Wood Inc. Oregon.
  • [23] Altınok M, Şemsettin D. (2010). Effect of the Natural Environment Condition (Winter Season's) To Screwholding Performance of Some Wooden Types. Journal of Polytechnic, 13(4): 305-311 (in Turkish)
  • [24] Leśniak A, Plebankiewicz E. (2014). Modeling the Decision-Making Process Concerning Participation in Construction Bidding. Journal of Management in Engineering, 31(2), 04014032
  • [25] Uran S, Jezernik K. (2008). Virtual Laboratory for Creative Control Design Experiments. IEEE Transactions on Education, 51(1): 69-75
  • [26] Keller HE, Keller EE. (2005). Making Real Virtual Labs. The Science Education Review, 4(1)
  • [27] Bui RT, et al. (2000). Model-Based Control for Industrial Processes Using a Virtual Laboratory. Intelligent Problem Solving: Methodologies and approaches Proceedings/13th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, June, New Orleans, Louisiana, USA, 1821: 671-680
  • [28] Okereke MI, et al. (2014). Virtual Testing of Advanced Composites. Cellular Materials And Biomaterials: A review, Composites: Part B, 60(2014): 637-662
  • [29] Huang X. (2013). Diaphragm Stiffness In Wood-Frame Construction. Master Thesis, The University of British Columbia, Applied Science, BC Canada.
  • [30] Budakçı M, Akkuş M. (2013). Modeling The Resistance of the Veneer Adhesion Strength on Some Wood Based Panels by Artificial Neural Networks. Journal of Polytechnic, 14(1): 63-71 (in Turkish)
  • [31] Dobrzański LA, Honysz R. (2007). Materials Science Virtual Laboratory As An Example Of The Computer Aid In Materials Engineering. Journal of Achievements in Materials and Manufacturing Engineering, 24(2): 219-222 [32] Akin E. (2006). The Investigation of Notched Tensile Test With Finite Element Method. Master Thesis, Sakarya University, Machine Engineering Department, Sakarya, Turkey, (in Turkish)
  • [33] Oh HJ, et al. (2004). What virtual reality can offer to the furniture industry. Journal of Textile And Apparel, Technology And Management, 4(1): 1-17
  • [34] Wang H. (2000). Creating Virtual Wood Particulate Composites. Thesis of Doctor of Philosophy, The University of Maine, Forest Resources, China.
  • [35] Logar V, et al. (2014). Using A Fuzzy Black-Box Model To Estimate The Indoor Illuminance In Buildings. Energy and Buildings, 70: 343–351
  • [36] Abd El-rahman, et al. (2013). Implementation of neural network for monitoring and prediction of surface roughness in a virtual end milling process of a CNC vertical milling machine. Journal of Engineering and Technology Research, 5(4): 63-78
  • [37] Hasim N, Mohd Aras MA. (2012). Intelligent Room Temperature Controller System Using MATLAB Fuzzy Logic Toolbox. International Journal of Science and Research (IJSR), 3(6): 1748-1753
  • [38] Kubat C, Kiraz A. (2012). The Modeling Of Tensile Test In Virtual Laboratory Design Using Artificial Intelligence. Journal of the Faculty of Engineering and Architecture of Gazi University, 27(1): 205-209 (in Turkish)
  • [39] Elmas Ç, Akcayol MA. (2004). Virtual Electrical Machinery Laboratory: A Fuzzy Logic Controller for Induction Motor Drives. International Journal of Engineering Education, 20(2): 226-2333
  • [40] Erkal B. (2009). Design and Simulink Simulation of Neuro-Fuzzy Control of A Magnetic Suspension System For Educational Purposes. 5th International Symposium on Advanced Technologies, May 13-15, Karabük, Turkey, (in Turkish)
  • [41] Iliadis LS, et al. (2008). Application of fuzzy T-norms towards a new Artificial Neural Networks’ evaluation framework: A case from wood industry. Information Sciences, 178: 3828–3839
  • [42] Kurt A. (2003). Flexible Manufacturing System Design Using Simulation-Artificial Neural Network. Journal of Faculty of Engineering and Architecture, Gazi University, 18(2): 31-38 (in Turkish)
  • [43] Ding MY., Hu YK., Kang ZH., Feng YJ., (2021). Teaching with virtual simulation: Is it helpful? 6th International Conference for Design Education Researches, 772-779, China, https://doi.org/10.21606/drs_lxd2021.07.183
  • [44] Deriba F., Saqr M., Tukiainen M., (2023). Exploring Barriers and Challenges to Accessibility in Virtual Laboratories: A Preliminary Review. Proceedings of the Technology-Enhanced Learning in Laboratories CEUR workshop (TELL 2023), April 27
  • [45] Elmoazen R., Saqr M., Khalil M., Wasson B., (2023). Learning Analytics In Virtual Laboratories:A Systematic Literature Review of Empirical Research. Smart Learning Environments, 10:23, https://doi.org/10.1186/s40561-023-00244-y
  • [46] Shadbad F., Bahr G., Luse A., Hammer B., (2023). Inclusion of Gamification Elements in the Context of Virtual Lab Environments to Increase Educational Value. AIS Transactions on Human-Computer Interaction, Volume 15, Issue 2:224-246

A Performance Investigation of Different ANFIS Parameters On Screw Withdrawal Strength Virtual Laboratory Tests

Year 2023, Volume: 11 Issue: 3, 409 - 421, 29.09.2023

Abstract

Today’s computer technology offers large amount usage of virtual environment possibilities to us. By the usage of advanced computer technologies’ skills, mankind improved their life styles and can easily supply their demands. Certainly, the usage of this technology affects also educational systems and its instruments. Especially, students and researchers can easily get the data from the virtual environment and of course researchers and scientists can share data as well. This study is to investigate the advantages of computer technologies in the field of virtual laboratory usage that is important component of educational system. In this paper, screw withdrawal strength test was used to point out the usage of virtual laboratory. The method of study based on the testing data of a particleboard screw holding ability, which was obtained under various testing conditions. These testing conditions and the results of tests were used as an input and output data for creating correlations in Adaptive Neuro Fuzzy Inference System (ANFIS). Half of the inputs and outputs were used for training, the others used for virtual testing. Finally, obtained data were evaluated with the point of average training and testing errors, and questioned the role in architectural education.

References

  • [1] Bozkurt E, Sarikoc A. (2008). Can The Virtual Laboratory Replace the Traditional Laboratory in Physics Education? Selcuk University, Faculty of Ahmet Keles Education Journal, 25:89-100 (in Turkish)
  • [2] Tiwari R, Singh K. (2011). Virtualization Of Engineering Discipline Experiments For An Internet-Based Remote Laboratory. Australasian Journal of Educational Technology, 27(4), 671-692
  • [3] Eckhoff EC, et al. (2002). Interactive Virtual Laboratory for Experience with a Smart Bridge Test. American Society for Engineering Education Annual Conference & Exposition; June 16-17 Montreal, Canada
  • [4] Budhu M. (2002). Virtual Laboratories For Engineering Education. International Conference on Engineering Education; Manchester, UK
  • [5] Rauf H.L., Shareef S.S., Ukabi E., (2019). Understanding the Relationship between Construction Courses and Design in Architectural Education. International Journal of Recent Technology and Engineering (IJRTE), ISSN: 2277-3878, Volume-8 Issue-3, September 2019
  • [6] Elfakki AO., Sghaier S., Alotaibi AA. (2023). An Efficient System Based on Experimental Laboratory in 3D Virtual Environment for Students with Learning Disabilities. Electronics February-2023, 12, 989. https://doi.org/10.3390/electronics12040989
  • [7] Kiraz A., Kubat C., Özbek YY., Uygun Ö., Eski H. (2014). A Web-Based Virtual Experiment in Material Science: Tensile Test Laboratory Application. Acta Physica Polonica A Journal, Vol (2)125:310-312, DOI: 10.12693/APhysPolA.125.310
  • [8] Falcó O., Ávilab RL., Tijsc B., Lopes CS. (2018). Modelling and simulation methodology for unidirectional composite laminates in a Virtual Test Lab framework. Composite Structures, 190 (2018) 137–159, https://doi.org/10.1016/j.compstruct.2018.02.016
  • [9] Zadeh LA. (1965). Fuzzy Sets, Information and Control. 8 (3):338-353
  • [10] Jang JSR, Sun CT. (1995). Neuro-Fuzzy Modeling and Control. Proceedings of the IEEE, 83 (3), 378-406
  • [11] Arabacıoğlu BC. (2005). Using Fuzzy Inference System for Architectural Space Analysis. Applied Soft Computing, 10 (3), 926-937
  • [12] Arabacıoğlu FP, Arabacıoğlu BC. (2011). Using Adaptive Neuro-Fuzzy Inference System (Anfis) On Design Studio Grade Estimation For Instructors’ Evaluation Performance Analyses, Advances in Fuzzy Sets and Systems, 9 (2) 93-110
  • [13] Arabacıoğlu FP, Arabacıoğlu BC. (2013). Design Studio Evaluation Discussions in Digital Age. The International Journal of Science Commerce and Humanities, 1 (8) 86-97
  • [14] Zadeh LA. (1994). Fuzzy Logic, Neural Networks And Soft Computing. Communications of the ACM, 37 (3):77-84
  • [15] Ross TJ. (2010). Development of Membership Functions. Fuzzy Logic with Engineering Applications, Third Edition, 174-210
  • [16] Dutta S. (1993). New faster Kernighan-Lin-type graph-partitioning algorithms. Proc. IEEE/ACM International Conference on CAD.
  • [17] Jang JSR. (1993). ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Trans. on Systems, Man, and Cybernetics, 23 (3), 665-685
  • [18] Kamali R, Binesh AR. (2013). A Comparison Of Neural Networks And Adaptive Neuro-Fuzzy Inference Systems For The Prediction Of Water Diffusion Through Carbon Nanotubes, Microfluidics and Nanofluidics, 14 (3-4), 575-581
  • [19] Palani S, Natarajan U, Chellamalai M. (2013). On-line prediction of micro-turning multi-response variables by machine vision system using adaptive neuro-fuzzy inference system (ANFIS), Mach. Vis. Appl. 24(1): 19-32
  • [20] Eckelman CA. (1998). The Withdrawal Strength of Screws From A Commercially Available Medium Density Fiberboard. For Prod, J, 38: 21–24
  • [21] American Society for Testing and Materials (2006). ASTM D1037-12, Standard Test Methods for Evaluating Properties of Wood Base Fiber and Particle Panel Materials, ASTM, Philadelphia Vol 04.10
  • [22] Gates JC. (2009). Screw withdrawal strength in 9wood’s assemblies, Test Evaluation Report. Oregon Wood Innovation Center, 9 Wood Inc. Oregon.
  • [23] Altınok M, Şemsettin D. (2010). Effect of the Natural Environment Condition (Winter Season's) To Screwholding Performance of Some Wooden Types. Journal of Polytechnic, 13(4): 305-311 (in Turkish)
  • [24] Leśniak A, Plebankiewicz E. (2014). Modeling the Decision-Making Process Concerning Participation in Construction Bidding. Journal of Management in Engineering, 31(2), 04014032
  • [25] Uran S, Jezernik K. (2008). Virtual Laboratory for Creative Control Design Experiments. IEEE Transactions on Education, 51(1): 69-75
  • [26] Keller HE, Keller EE. (2005). Making Real Virtual Labs. The Science Education Review, 4(1)
  • [27] Bui RT, et al. (2000). Model-Based Control for Industrial Processes Using a Virtual Laboratory. Intelligent Problem Solving: Methodologies and approaches Proceedings/13th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, June, New Orleans, Louisiana, USA, 1821: 671-680
  • [28] Okereke MI, et al. (2014). Virtual Testing of Advanced Composites. Cellular Materials And Biomaterials: A review, Composites: Part B, 60(2014): 637-662
  • [29] Huang X. (2013). Diaphragm Stiffness In Wood-Frame Construction. Master Thesis, The University of British Columbia, Applied Science, BC Canada.
  • [30] Budakçı M, Akkuş M. (2013). Modeling The Resistance of the Veneer Adhesion Strength on Some Wood Based Panels by Artificial Neural Networks. Journal of Polytechnic, 14(1): 63-71 (in Turkish)
  • [31] Dobrzański LA, Honysz R. (2007). Materials Science Virtual Laboratory As An Example Of The Computer Aid In Materials Engineering. Journal of Achievements in Materials and Manufacturing Engineering, 24(2): 219-222 [32] Akin E. (2006). The Investigation of Notched Tensile Test With Finite Element Method. Master Thesis, Sakarya University, Machine Engineering Department, Sakarya, Turkey, (in Turkish)
  • [33] Oh HJ, et al. (2004). What virtual reality can offer to the furniture industry. Journal of Textile And Apparel, Technology And Management, 4(1): 1-17
  • [34] Wang H. (2000). Creating Virtual Wood Particulate Composites. Thesis of Doctor of Philosophy, The University of Maine, Forest Resources, China.
  • [35] Logar V, et al. (2014). Using A Fuzzy Black-Box Model To Estimate The Indoor Illuminance In Buildings. Energy and Buildings, 70: 343–351
  • [36] Abd El-rahman, et al. (2013). Implementation of neural network for monitoring and prediction of surface roughness in a virtual end milling process of a CNC vertical milling machine. Journal of Engineering and Technology Research, 5(4): 63-78
  • [37] Hasim N, Mohd Aras MA. (2012). Intelligent Room Temperature Controller System Using MATLAB Fuzzy Logic Toolbox. International Journal of Science and Research (IJSR), 3(6): 1748-1753
  • [38] Kubat C, Kiraz A. (2012). The Modeling Of Tensile Test In Virtual Laboratory Design Using Artificial Intelligence. Journal of the Faculty of Engineering and Architecture of Gazi University, 27(1): 205-209 (in Turkish)
  • [39] Elmas Ç, Akcayol MA. (2004). Virtual Electrical Machinery Laboratory: A Fuzzy Logic Controller for Induction Motor Drives. International Journal of Engineering Education, 20(2): 226-2333
  • [40] Erkal B. (2009). Design and Simulink Simulation of Neuro-Fuzzy Control of A Magnetic Suspension System For Educational Purposes. 5th International Symposium on Advanced Technologies, May 13-15, Karabük, Turkey, (in Turkish)
  • [41] Iliadis LS, et al. (2008). Application of fuzzy T-norms towards a new Artificial Neural Networks’ evaluation framework: A case from wood industry. Information Sciences, 178: 3828–3839
  • [42] Kurt A. (2003). Flexible Manufacturing System Design Using Simulation-Artificial Neural Network. Journal of Faculty of Engineering and Architecture, Gazi University, 18(2): 31-38 (in Turkish)
  • [43] Ding MY., Hu YK., Kang ZH., Feng YJ., (2021). Teaching with virtual simulation: Is it helpful? 6th International Conference for Design Education Researches, 772-779, China, https://doi.org/10.21606/drs_lxd2021.07.183
  • [44] Deriba F., Saqr M., Tukiainen M., (2023). Exploring Barriers and Challenges to Accessibility in Virtual Laboratories: A Preliminary Review. Proceedings of the Technology-Enhanced Learning in Laboratories CEUR workshop (TELL 2023), April 27
  • [45] Elmoazen R., Saqr M., Khalil M., Wasson B., (2023). Learning Analytics In Virtual Laboratories:A Systematic Literature Review of Empirical Research. Smart Learning Environments, 10:23, https://doi.org/10.1186/s40561-023-00244-y
  • [46] Shadbad F., Bahr G., Luse A., Hammer B., (2023). Inclusion of Gamification Elements in the Context of Virtual Lab Environments to Increase Educational Value. AIS Transactions on Human-Computer Interaction, Volume 15, Issue 2:224-246
There are 45 citations in total.

Details

Primary Language English
Subjects Materials and Technology in Architecture, Design Instruments and Technology
Journal Section Architecture
Authors

Esra Bayır 0000-0002-2298-8326

Mustafa Adil Kasapseçkin 0000-0002-0507-7985

Publication Date September 29, 2023
Submission Date June 10, 2023
Published in Issue Year 2023 Volume: 11 Issue: 3

Cite

APA Bayır, E., & Kasapseçkin, M. A. (2023). A Performance Investigation of Different ANFIS Parameters On Screw Withdrawal Strength Virtual Laboratory Tests. Gazi University Journal of Science Part B: Art Humanities Design and Planning, 11(3), 409-421.