[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124338-en":3,"doc-seo-124338-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124338,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Performance evaluation of machine learning algorithms in predicting machining responses of superalloys","This study evaluates gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural networks (ANN) for predicting machining responses during the milling of Inconel 690, a superalloy valued for mechanical performance and oxidation resistance. Machining Inconel 690 is challenging due to toughness and work-hardening, which can cause rapid tool wear and poor surface finish. Unlike empirical trial-and-error optimization, the models capture nonlinear relationships between machining parameters and outcomes. Prediction quality is assessed using RMSE, R2, and MAPE, with GEP achieving the strongest accuracy and demonstrating its effectiveness for superalloy process optimization.","RESEARCH ARTICLE | OCTOBER 21 2024  \nPerformance evaluation of machine learning algorithms in predicting machining responses of superalloys  \nAbhijit Bhowmik  ; Raja Praveen K. N. ; Nilesh Bhosle; Kunal Gagneja; Zunirah Mohd Talib; Jasgurpreet Singh Chohan  ; Ahmed Alkhayyat; M. Janaki Ramudu; A. Johnson Santhosh 􀀤   \nAIP Advances 14, 105027 (2024)  \n[https://doi.org/10.1063/5.0235664](https://doi.org/10.1063/5.0235664)  \n􀀪  \nView Online  \n􀀮  \nExport Citation  \nArticles You May Be Interested In  \nPredictive modeling of MRR, TWR, and SR in spark-EDM of Al-4 .5Cu–SiC using ANN and GEP  \nAIP Advances (September 2024)  \nMinimum quantity blended bio-lubricants for sustainable machining of superalloy: An MCDM model-based study  \nAIP Advances (July 2024)  \nAdvances in superalloy technology of China  \nJ. Vac. Sci. Technol. A (July 1987)  \n28 October 2024 08:34:22  \nAIP Advances ARTICLE  \n[pubs.aip.org/aip/adv](pubs.aip.org/aip/adv)  \nPerformance evaluation of machine learning algorithms in predicting machining responses of superalloys  \n\n| Cite as: AIP Advances 14, 105027 (2024); doi: 10. 1063/5.0235664 Submitted: 29 August 2024 • Accepted: 24 September 2024 •\u003Cbr>Published Online: 21 October 2024 |  |  |  |\n| --- | --- | --- | --- |\n| Abhijit Bhowmik,1 , 2  Raja Praveen K. N.,3 Nilesh Bhosle,4 Kunal Gagneja,5 Zunirah Mohd Talib,6\u003Cbr>Jasgurpreet Singh Chohan,7 , 8  Ahmed Alkhayyat,9 M. Janaki Ramudu,10 and A. Johnson Santhosh11, a)  |  |  |  |\n| AFFILIATIONS\u003Cbr>1 Department of Mechanical Engineering, Dream Institute of Technology, Kolkata 700104, India |  |  |  |\n| 2 Centre of Research Impact and Outreach, Chitkara University, Rajpura, Punjab 140417, India |  |  |  |\n| 3 Department of Computer Science and Engineering, School of Engineering and Technology, JAIN (Deemed to be University), Bangalore, Karnataka, India |  |  |  |\n| 4 NIMS School of Computing Science and Artificial Intelligence, NIMS University Rajasthan, Jaipur, India |  |  |  |\n| 5 Department of Computer Science and Engineering, Chandigarh Engineering College, Chandigarh Group of Colleges-Jhanjeri, Mohali, Punjab 140307, India |  |  |  |\n| 6 Management and Science University, Shah Alam, Selangor, Malaysia |  |  |  |\n| 7 School of Mechanical Engineering, Rayat Bahra University, Kharar, Punjab 140103, India |  |  |  |\n| 8 Faculty of Engineering, Sohar University, P. O. Box 44, Sohar PCI 311, Oman |  |  |  |\n| 9 College of Technical Engineering, The Islamic University, Najaf, Iraq |  |  |  |\n| 10 Department of Computer Science and Engineering, Raghu Engineering College, Vishakhapatnam, Andhra Pradesh 531162, India |  |  |  |\n| 11 Faculty of Mechanical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia a)Author to whom correspondence should [be addressed: johnson.antony@ju.edu.et](be addressed: johnson.antony@ju.edu.et) |  |  |  |\n| ABSTRACT\u003Cbr>This study explores the application of machine learning algorithms—gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural networks (ANN)—to predict machining responses during the milling of Inconel 690, a superalloy known for its exceptional mechanical properties and oxidation resistance. Machining Inconel 690 presents significant challenges due to its toughness and work-hardening tendencies, which can lead to rapid tool wear and poor surface finish. Traditional optimization methods often rely on empirical models and trial-and-error approaches, which are time-consuming and costly. In contrast, machine learning techniques can effectively model complex, nonlinear relationships between machining parameters and performance outcomes, such as surface roughness, cutting force, and cutting temperature. This study employs statistical metrics, including Root mean square error (RMSE), coefficient of determination (R2 ), and mean absolute percentage error (MAPE), to determine the predictive performance of the models. The results show that the GEP model achieved an R2 rangin","cbCaibbShmpr2WuJ","https://ap.wps.com/l/cbCaibbShmpr2WuJ","pdf",6284630,1,15,"English","en",105,"# Abstract\n# Introduction\n# Methods and Models\n## GEP model\n## ANFIS model\n## ANN model\n# Performance Metrics\n## RMSE\n## R2\n## MAPE\n# Results and Discussion\n# Conclusion","[{\"question\":\"Which machine learning algorithms are used to predict machining responses of Inconel 690?\",\"answer\":\"The study applies gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural networks (ANN) to predict machining responses during milling.\"},{\"question\":\"Why is machining Inconel 690 considered difficult?\",\"answer\":\"Its toughness and work-hardening tendencies promote rapid tool wear and can lead to poor surface finish, making optimization more challenging.\"},{\"question\":\"How is predictive performance evaluated in the study?\",\"answer\":\"Model performance is measured with RMSE, coefficient of determination (R2), and mean absolute percentage error (MAPE) to quantify prediction accuracy.\"}]","Performance evaluation of machine learning algorithms in predicting machining responses of superalloys | 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