Comparison of regression, ANN, ANFIS, and ChatGPT prediction of turning cutting force

References

• Akgün, Mahir, Barış Özlü, and Fuat Kara. 2023. “Effect of PVD-TiN and CVD-Al2O3 Coatings on Cutting Force, Surface Roughness, Cutting Power, and Temperature in Hard Turning of AISI H13 Steel.” Journal of Materials Engineering and Performance 32 (3): 1390–1401.
   Web of Science ®Google Scholar
• Alajmi, Mahdi S, and Abdullah M Almeshal. 2020. “Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method.” Materials 13 (13): 2986.
   PubMed Web of Science ®Google Scholar
• Aldin, Sami Shams, and Hatice Sözer. 2022. “Comparing the Accuracy of ANN and ANFIS Models for Predicting the Thermal Data.” Journal of Construction Engineering, Management & Innovation 5 (2): 119–139.
   Google Scholar
• Alhassan, Mohammad, Layla Amaireh, Haythem Bany Salameh, Mohannad Alhafnawi, and Nour Betoush. 2022. “The Emerging Engineering Applications of Artificial Neural Networks: A Visionary Study.” Paper Presented at the 2022 International Arab Conference on Information Technology (ACIT).
   Google Scholar
• Ali, Shafahat, Assem Alshibi, Abdelrahman Nasreldin, and Salman Pervaiz. 2023. “Artifical Intelligence Inspired Approach to Numerically Investigate Chip Morphology in Machining AISI630.” International Journal on Interactive Design and Manufacturing (IJIDeM), 1–18.
   Google Scholar
• Altan Özbek, Nursel, Onur Özbek, Fuat Kara, and Hamit Saruhan. 2022. “Effect of Eco-Friendly Minimum Quantity Lubrication in Hard Machining of Vanadis 10: A High Strength Steel.” Steel Research International 93 (7): 2100587.
   Web of Science ®Google Scholar
• Aydin, Kutay. 2023. “Investigating Cutting Force and Cutting Power When Turning AA6082-T4 Alloy at Cutting Depths Smaller Than Tool Nose Radius.” Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 26 (4): 972–982.
   Google Scholar
• Aydın, Kutay, Şükran Katmer, Arif Gök, and Ulvi Şeker. 2021. “Experimental and Statistical Investigation of the Machining Performance of Wave Form end Mills on AISI 316L Stainless Steel.” Journal of the Faculty of Engineering and Architecture of Gazi University 36 (4): 2225–2238.
   Web of Science ®Google Scholar
• Bahrini, Aram, Mohammadsadra Khamoshifar, Hossein Abbasimehr, Robert J Riggs, Maryam Esmaeili, Rastin Mastali Majdabadkohne, and Morteza Pasehvar. 2023. “ChatGPT: Applications, opportunities, and threats.” Paper presented at the 2023 Systems and Information Engineering Design Symposium (SIEDS).
   Google Scholar
• Biswas, Som S. 2023. “Potential use of Chat Gpt in Global Warming.” Annals of Biomedical Engineering 51 (6): 1126–1127.
   PubMed Web of Science ®Google Scholar
• Chatterjee, Suman, Siba Sankar Mahapatra, Vijay Bharadwaj, Brahma N Upadhyay, and Khushvinder S Bindra. 2021. “Prediction of Quality Characteristics of Laser Drilled Holes Using Artificial Intelligence Techniques.” Engineering with Computers 37: 1181–1204.
   Web of Science ®Google Scholar
• Cheng, Kunming, Qiang Guo, Yongbin He, Yanqiu Lu, Shuqin Gu, and Haiyang Wu. 2023. “Exploring the Potential of GPT-4 in Biomedical Engineering: The Dawn of a New Era.” Annals of Biomedical Engineering, 1–9.
   Web of Science ®Google Scholar
• Ciftci, Ibrahim, and Hüseyin Gökçe. 2019. “Optimisation of Cutting Tool and Cutting Parameters in Machining of Molybdenum Alloys Through the Taguchi Method.” Journal of the Faculty of Engineering and Architecture of Gazi University 34 (1): 201–213.
   Web of Science ®Google Scholar
• Dedeakayoğulları, Hakan, Alaattin Kaçal, and Kübra Keser. 2022. “Modeling and Prediction of Surface Roughness at the Drilling of SLM-Ti6Al4V Parts Manufactured with Pre-Hole with Optimized ANN and ANFIS.” Measurement 203: 112029.
   Web of Science ®Google Scholar
• Erkan, Ömer, Birhan Işık, Adem Çiçek, and Fuat Kara. 2013. “Prediction of Damage Factor in End Milling of Glass Fibre Reinforced Plastic Composites Using Artificial Neural Network.” Applied Composite Materials 20: 517–536.
   Web of Science ®Google Scholar
• Gürbüz, Hüseyin, Ulvi Şeker, and Fırat Kafkas. 2020. “Effects of Cutting Tool Forms on the Surface Integrity in Turning of AISI 316L Stainless Steel.” Journal of the Faculty of Engineering and Architecture of Gazi University 35 (1): 225–240.
   Web of Science ®Google Scholar
• Kalla, Dinesh, and Nathan Smith. 2023. “Study and Analysis of Chat GPT and its Impact on Different Fields of Study.” International Journal of Innovative Science and Research Technology 8 (3).
   Google Scholar
• Kara, Fuat, Kubilay Aslantas, and Adem Çiçek. 2015. “ANN and Multiple Regression Method-Based Modelling of Cutting Forces in Orthogonal Machining of AISI 316L Stainless Steel.” Neural Computing and Applications 26: 237–250.
   Web of Science ®Google Scholar
• Kara, Fuat, Furgan Bayraktar, Furkan Savaş, and Onur Özbek. 2023. “Experimental and Statistical Investigation of the Effect of Coating Type on Surface Roughness, Cutting Temperature, Vibration and Noise in Turning of Mold Steel.” Journal of Materials and Manufacturing 2 (1): 31–43.
   Google Scholar
• Li, Anhai, Jian Zang, and Jun Zhao. 2020. “Effect of Cutting Parameters and Tool Rake Angle on the Chip Formation and Adiabatic Shear Characteristics in Machining Ti-6Al-4V Titanium Alloy.” The International Journal of Advanced Manufacturing Technology 107: 3077–3091.
   Web of Science ®Google Scholar
• Lorentzon, John, Niklas Järvstråt, and B. L. Josefson. 2009. “Modelling Chip Formation of Alloy 718.” Journal of Materials Processing Technology 209 (10): 4645–4653.
   Web of Science ®Google Scholar
• Lund, Brady D, and Ting Wang. 2023. “Chatting About ChatGPT: How may AI and GPT Impact Academia and Libraries?” Library Hi Tech News 40 (3): 26–29.
   Google Scholar
• MacKay, David JC. 1992. “Bayesian Interpolation.” Neural Computation 4 (3): 415–447.
   Web of Science ®Google Scholar
• Manoj, I. V., H. Soni, S. Narendranath, P. M. Mashinini, and F. Kara. 2022. “Examination of Machining Parameters and Prediction of Cutting Velocity and Surface Roughness Using RSM and ANN Using WEDM of Altemp HX.” Advances in Materials Science and Engineering 2022: 1–9.
   Web of Science ®Google Scholar
• Nas, Engin, and Fuat Kara. 2022. “Optimization of EDM Machinability of Hastelloy C22 Super Alloys.” Machines 10 (12): 1131.
   Web of Science ®Google Scholar
• OpenAI. 2023. “Chat GPT.” Accessed 04.06.2023. https://openai.com/chatgpt.
   Google Scholar
• Özbek, Onur, and Hamit Saruhan. 2020. “The Effect of Vibration and Cutting Zone Temperature on Surface Roughness and Tool Wear in eco-Friendly MQL Turning of AISI D2.” Journal of Materials Research and Technology 9 (3): 2762–2772.
   Google Scholar
• Ozel, T., I. Llanos, J. Soriano, and P.-J. Arrazola. 2011. “3D Finite Element Modelling of Chip Formation Process for Machining Inconel 718: Comparison of FE Software Predictions.” Machining Science and Technology 15 (1): 21–46.
   Web of Science ®Google Scholar
• Ozlu, Baris, and Levent Ugur. 2021. “Optimization of Cutting Forces on Turning of Ti-6Al-4V Alloy by 3D FEM Simulation Analysis.” Journal of Engineering Research and Applied Science 10 (2): 1789–1795.
   Google Scholar
• Patel, Apurva, and Joshua D Summers. 2023. “Novice Engineer vs. Machine: Comparing Artificial Neural Network Predictions with Student Estimates of Market Price Using Function Structure Models.” Journal of Engineering Design 34 (11): 941–962.
   Web of Science ®Google Scholar
• Pimenov, Danil Yu, Andres Bustillo, Szymon Wojciechowski, Vishal S Sharma, Munish K Gupta, and Mustafa Kuntoğlu. 2023. “Artificial Intelligence Systems for Tool Condition Monitoring in Machining: Analysis and Critical Review.” Journal of Intelligent Manufacturing 34 (5): 2079–2121.
   Web of Science ®Google Scholar
• Rangilal, B., N. Bharat, P. S. C. Bose, and C. S. P. Rao. 2023. “Optimization of Machining Behaviour of Monel 400 Super Alloy Using ANN and GA Technique.” NanoWorld J 9 (S1): S96–S100.
   Google Scholar
• Rao, Thella Babu. 2023. “Prediction of EDMed Micro-Hole Quality Characteristics Using Hybrid bio-Inspired Machine Learning-Based Predictive Approaches.” International Journal on Interactive Design and Manufacturing (IJIDeM) 17 (2): 747–764.
   Google Scholar
• Rathore, Bharati. 2023. “Future of AI & Generation Alpha: ChatGPT Beyond Boundaries.” Eduzone : International Peer Reviewed/Refereed Academic Multidisciplinary Journal 12 (1): 63–68.
   Google Scholar
• Sada, S. O., and S. C. Ikpeseni. 2021. “Evaluation of ANN and ANFIS Modeling Ability in the Prediction of AISI 1050 Steel Machining Performance.” Heliyon 7 (2).
   PubMed Web of Science ®Google Scholar
• Sanjay, Chintakindi, Ali Alsamhan, and Mustufa Haider Abidi. 2022. “Multi Response Optimization of Machining Parameters for an Annealed Monel K 500 Alloy in Drilling Using Machine Learning Techniques and ANN.” Journal of Intelligent & Fuzzy Systems 42 (6): 5605–5625.
   Web of Science ®Google Scholar
• Saravanan, A., and J. Jerald. 2019. “Ontological Model-Based Optimal Determination of Geometric Tolerances in an Assembly Using the Hybridised Neural Network and Genetic Algorithm.” Journal of Engineering Design 30 (4-5): 180–198.
   Web of Science ®Google Scholar
• Selvakumar, Joyson, Sakthivel Sakthivel, Jerome Arul Praveen, and Samuel Raj. 2023. “Effect of Edge Radius on Forces, Tool Wear and Surface Integrity Under Edge Radius Dominated Tool-Chip Contact Conditions.” Proceedings of The Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture.
   Web of Science ®Google Scholar
• Thangarasu, S. K., T. Mohanraj, K. Devendran, M. Rajalakshmi, Subrata Chowdhury, and Saravanakumar Gurusamy. 2023. “Prediction of Flank Wear During Turning of EN8 Steel with Cutting Force Signals Using a Deep Learning Approach.” Mathematical Problems in Engineering 2023.
   Google Scholar
• Venkata Rao, K., and P. B. G. S. N. Murthy. 2018. “Modeling and Optimization of Tool Vibration and Surface Roughness in Boring of Steel Using RSM, ANN and SVM.” Journal of Intelligent Manufacturing 29 (7): 1533–1543.
   Web of Science ®Google Scholar
• Xu, Dongdong, Liang Ding, Yang Liu, Jinming Zhou, and Zhirong Liao. 2021. “Investigation of the Influence of Tool Rake Angles on Machining of Inconel 718.” Journal of Manufacturing and Materials Processing 5 (3): 100.
   Google Scholar
• Yıldız, Aytaç, Levent Uğur, and İsmail Enes Parlak. 2022. “Optimization of the Cutting Parameters Affecting the Turning of AISI 52100 Bearing Steel Using the Box-Behnken Experimental Design Method.” Applied Sciences 13 (1): 3.
   Google Scholar