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Results after annealing show that there will be a reduction in surface roughness values by 19.61%, tool wear by 6.3%, and an increase in the metal removal rate by 14.98%.\u00a0The PCA results show that the feed is more significant than the depth of cut and speed. The higher value of the composite primary component will represent optimal factors such as speed of 80, feed of 0.2 and depth of cut of 0.3, and values of principal components like surface roughness (<jats:italic>\u03a8<\/jats:italic><jats:sub>1<\/jats:sub>\u2009=\u200964.5), tool wear (<jats:italic>\u03a8<\/jats:italic><jats:sub>2<\/jats:sub>\u2009=\u200922.3) and metal removal rate (<jats:italic>\u03a8<\/jats:italic><jats:sub>3<\/jats:sub>\u2009=\u200913.2). Hyper-parameter optimization represents speed is directly proportional to roughness, tool wear, and metal removal rate, while feed and depth of cut are inversely proportional. The optimization history plot will be steady, and the prediction accuracy will be 96.96%. Machine learning techniques are employed through the Python language using Google Colab. The estimated values from the decision tree method for surface roughness and tool wear predictions using the AdaBoost algorithm match well with actual values. As per MOPSO (multi-objective particle swarm optimization), the predicted responses are as follows; surface roughness (2.5 \u03bcm, 100, 02, 0.45), tool wear (0.31\u00a0mm, 40, 0.40, 0.60), and MRR (material removal rate) (5145 mm<jats:sup>3<\/jats:sup>\/min, 100, 0.4, 0.15). As validated by experimentation, there are small variations as the surface roughness varied by 1.56%, tool wear by 6.8%, and MRR by 2.57%.<\/jats:p>","DOI":"10.1007\/s44196-022-00070-z","type":"journal-article","created":{"date-parts":[[2022,3,21]],"date-time":"2022-03-21T08:02:23Z","timestamp":1647849743000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Annealing of Monel 400 Alloy Using Principal Component Analysis, Hyper-parameter Optimization, Machine Learning Techniques, and Multi-objective Particle Swarm Optimization"],"prefix":"10.1007","volume":"15","author":[{"given":"Sanjay","family":"Chintakindi","sequence":"first","affiliation":[]},{"given":"Ali","family":"Alsamhan","sequence":"additional","affiliation":[]},{"given":"Mustufa Haider","family":"Abidi","sequence":"additional","affiliation":[]},{"given":"Maduri Praveen","family":"Kumar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,21]]},"reference":[{"key":"70_CR1","doi-asserted-by":"publisher","first-page":"1719","DOI":"10.1016\/j.matdes.2006.03.003","volume":"28","author":"M Nalbant","year":"2007","unstructured":"Nalbant, M., Altin, A.: The effect of coating material and geometry of cutting tool and cutting speed on machinability properties of Inconel 718 super alloys. 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