420924061923305f12ab5720240928075027380 beie:beie director@blueeyesintelligence.orgWEB-FORM International Journal of Recent Technology and Engineering (IJRTE) IJRTE 22773878 10.35940/ijrte.2277-3878 https://www.ijrte.org/ 09 30 2024 13 3 College of Engineering and Physical Sciences, University of Guelph, Guelph, Canada. Matthew Jansen https://orcid.org/0009-0006-3000-8814 Ibrahim Deiab https://orcid.org/0000-0003-0596-3802 College of Engineering and Physical Sciences, University of Guelph, Guelph, Canada. Machining high-temperature alloys such as Nimonic PE16 demands precise control of machining parameters to achieve desired outcomes while minimizing tool wear and optimizing surface finish. In this study, we propose using machine learning regression models combined with synthetic data and response surface methodology strategies to optimize machining parameters for PE16. We aim to develop a predictive model that accurately estimates optimal cutting speeds and feed rates based on key output parameters, including cutting forces and surface roughness. Our methodology involves collecting experimental data from controlled machining tests conducted on PE16 samples under varying conditions. We used the datasets to train and validate regression models to establish correlations between input parameters and machining outcomes. The performance of each model is evaluated based on metrics such as mean absolute error and coefficient of determination. These metrics show relationships within the data and can determine a model’s success. The proposed machine learning framework offers a data-driven approach to optimize machining processes for PE16, facilitating enhanced efficiency, productivity, and quality in nuclear and other high-performance applications. Our findings contribute to understanding machining dynamics in challenging materials and provide valuable insights for intelligent machining systems. 09 30 2024 1 6 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true International Journal of Recent Technology and Engineering (IJRTE) Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) Based on my understanding, this article has no conflicts of interest. Yes, I have revived financial assistance for this article. The National Science and Engineering Research Council of Canada (NSERC) supports this work. The data provided in this article is exempt from the requirement for ethical approval or participant consent. The adequate resources of this article are publicly accessible. The authorship of this article is contributed equally to all participating individuals. 10.35940/ijrte.C8124.13030924 https://www.ijrte.org/portfolio-item/C812413030924/