FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385065293> ?p ?o ?g. }

• W4385065293 endingPage "421" @default.
• W4385065293 startingPage "380" @default.
• W4385065293 abstract "Titanium alloys are notoriously difficult to machine. They are used in the manufacture of various types of lightweight components. It is therefore important to improve their machinability and thus achieve sustainability in machining such alloys, by selecting appropriate influential factors: cutting parameters, tool material, geometric form, coolant types, and hybrid machining methods, to deliver efficient output. Nowadays, meta-heuristic algorithms effectively solve multi-objective optimization in machining problems instead of single-objective one. Along with that, the mathematical predictive models used for single-objective optimization are gradually being replaced by machine learning algorithms, which are highly robust and efficient in terms of prediction performance. Therefore, this work addresses the prediction and optimization of average surface roughness (Ra) and tool wear (VB) in Ti6Al4V alloy turning, using a WC tool coated by chemical vapor deposition (CVD) and physical vapor deposition (PVD), with dry machining. We apply a two-pronged approach combining machine learning (ML) and Non-Dominated Sorting Genetic Algorithm (NSGA-II), to model and optimize Ra and VB. The four ML models – Linear Regression (LIN), Support Vector Machine Regression (SVR), Extreme Gradient Boosting (XGB), and Artificial Neural Network (ANN) – are used to predict Ra and VB. The input variables of the turning process – feed rate, depth of cut, cutting speed, cutting time, and tool materials – are the major factors affecting surface quality and tool wear. By the error metrics such as root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2), ANN is found to offer the best predictive performance. An ML and NSGA-II-based approach is then employed for multi-objective optimization of cutting parameters in Ti6Al4V turning. Fifty Pareto solutions are identified in the range of Ra and VB between (1.332 and 1.441 µm) and (0.100 and 0.125 mm), respectively. In this work, the Pareto solutions are selected based on their ranked performances. This aligns with the decision criterion employed to select the most robust cutting parameters. The definitive optimal Ra and VB are obtained by formulating a robust decisive multi-criterion function which integrates performance, preferred decision criterion, and trustworthiness. Finally, this produces the optimal solution for Ra and VB − 1.439 µm and 0.100 mm, respectively. Experimental validation confirms that the final optimum solution is within the acceptable range." @default.
• W4385065293 created "2023-07-23" @default.
• W4385065293 creator A5024957412 @default.
• W4385065293 creator A5050986651 @default.
• W4385065293 creator A5071455766 @default.
• W4385065293 creator A5076782738 @default.
• W4385065293 creator A5089241007 @default.
• W4385065293 date "2023-07-04" @default.
• W4385065293 modified "2023-09-27" @default.
• W4385065293 title "Multi-objective optimization based on machine learning and non-dominated sorting genetic algorithm for surface roughness and tool wear in Ti₆Al₄V turning" @default.
• W4385065293 cites W17072261 @default.
• W4385065293 cites W1963536096 @default.
• W4385065293 cites W1966120440 @default.
• W4385065293 cites W1966990174 @default.
• W4385065293 cites W1967640926 @default.
• W4385065293 cites W1970360626 @default.
• W4385065293 cites W1978770342 @default.
• W4385065293 cites W1979049578 @default.
• W4385065293 cites W1979777795 @default.
• W4385065293 cites W1980679368 @default.
• W4385065293 cites W1985777615 @default.
• W4385065293 cites W1986070682 @default.
• W4385065293 cites W1987884944 @default.
• W4385065293 cites W1992894697 @default.
• W4385065293 cites W1995341919 @default.
• W4385065293 cites W1995436689 @default.
• W4385065293 cites W2003622768 @default.
• W4385065293 cites W2007789237 @default.
• W4385065293 cites W2028144650 @default.
• W4385065293 cites W2033612199 @default.
• W4385065293 cites W2051629899 @default.
• W4385065293 cites W2060306506 @default.
• W4385065293 cites W2060600711 @default.
• W4385065293 cites W2064891003 @default.
• W4385065293 cites W2066113561 @default.
• W4385065293 cites W2066996458 @default.
• W4385065293 cites W2078249260 @default.
• W4385065293 cites W2079503926 @default.
• W4385065293 cites W2080340369 @default.
• W4385065293 cites W2086823387 @default.
• W4385065293 cites W2088730795 @default.
• W4385065293 cites W2126105956 @default.
• W4385065293 cites W2129749232 @default.
• W4385065293 cites W2136463933 @default.
• W4385065293 cites W2136922672 @default.
• W4385065293 cites W2137034166 @default.
• W4385065293 cites W2139427956 @default.
• W4385065293 cites W2276742132 @default.
• W4385065293 cites W2322095984 @default.
• W4385065293 cites W245099413 @default.
• W4385065293 cites W2476141494 @default.
• W4385065293 cites W2480645619 @default.
• W4385065293 cites W2490247523 @default.
• W4385065293 cites W2524886302 @default.
• W4385065293 cites W2533491448 @default.
• W4385065293 cites W2547192801 @default.
• W4385065293 cites W2552806983 @default.
• W4385065293 cites W2772404947 @default.
• W4385065293 cites W2799380858 @default.
• W4385065293 cites W2801226017 @default.
• W4385065293 cites W2801469870 @default.
• W4385065293 cites W2908541468 @default.
• W4385065293 cites W2911455822 @default.
• W4385065293 cites W2952068402 @default.
• W4385065293 cites W2952297896 @default.
• W4385065293 cites W2953121811 @default.
• W4385065293 cites W2994669395 @default.
• W4385065293 cites W3017268209 @default.
• W4385065293 cites W3022199973 @default.
• W4385065293 cites W3022465998 @default.
• W4385065293 cites W3038959022 @default.
• W4385065293 cites W3039866585 @default.
• W4385065293 cites W3044339716 @default.
• W4385065293 cites W3088669176 @default.
• W4385065293 cites W3095807143 @default.
• W4385065293 cites W3097586586 @default.
• W4385065293 cites W3102476541 @default.
• W4385065293 cites W3114046055 @default.
• W4385065293 cites W3123920941 @default.
• W4385065293 cites W3135524811 @default.
• W4385065293 cites W3135609456 @default.
• W4385065293 cites W3151394883 @default.
• W4385065293 cites W3174058747 @default.
• W4385065293 cites W3185348343 @default.
• W4385065293 cites W3198484697 @default.
• W4385065293 cites W3201751510 @default.
• W4385065293 cites W3202661960 @default.
• W4385065293 cites W4200066165 @default.
• W4385065293 cites W4200150857 @default.
• W4385065293 cites W4210411380 @default.
• W4385065293 cites W4210660142 @default.
• W4385065293 cites W4239510810 @default.
• W4385065293 doi "https://doi.org/10.1080/10910344.2023.2235610" @default.
• W4385065293 hasPublicationYear "2023" @default.
• W4385065293 type Work @default.
• W4385065293 citedByCount "0" @default.
• W4385065293 crossrefType "journal-article" @default.
• W4385065293 hasAuthorship W4385065293A5024957412 @default.

• next