Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

Wyszukujesz frazę "Nguyen, Nhu-Tung" wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
Multi-objective optimization of the cylindrical grinding process of SCM440 steel using preference selection index method
Autorzy:
Tien, Dung Hoang
Trung, Do Duc
Thien, Nguyen Van
Nguyen, Nhu-Tung
Powiązania:
https://bibliotekanauki.pl/articles/1833757.pdf
Data publikacji:
2021
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
MRR
Taguchi PSI method
surface roughness
cylindrical grinding process
Opis:
This paper presents a study to ensure the minimum values of Ra and Rz, and the maximum value of MRR when external cylindrical grinding by the PSI method. The experiments were performed according to the orthogonal Taguchi L9 matrix with the input parameters including workpiece speed, feed rate, and depth of cut in the conventional grinding machine. Analysis of experimental results by Pareto chart showed that the feed rate and the depth of cut most influence on Ra and Rz, respectively. Feed rate and depth of cut all have a great influence on MRR. Meanwhile, the workpiece speed has a negligible effect on all three output parameters. The research results showed that to obtain the minimum values of Ra and Rz, and maximum of MRR, the workpiece speed, feed rate, and depth of cut were 400 rev/min 37.7 mm/min, 0.09 mm/rev, and 0.02 mm, respectively.
Źródło:
Journal of Machine Engineering; 2021, 21, 3; 110--123
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A hybridization of machine learning and NSGA-II for multi-objective optimization of surface roughness and cutting force in AISI 4340 alloy steel turning
Autorzy:
Nguyen, Anh-Tu
Nguyen, Van-Hai
Le, Tien-Thinh
Nguyen, Nhu-Tung
Powiązania:
https://bibliotekanauki.pl/articles/2200263.pdf
Data publikacji:
2023
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
multi-objective optimisation
machine learning
AISI 4340
NSGA-II
ANN
Opis:
This work focuses on optimizing process parameters in turning AISI 4340 alloy steel. A hybridization of Machine Learning (ML) algorithms and a Non-Dominated Sorting Genetic Algorithm (NSGA-II) is applied to find the Pareto solution. The objective functions are a simultaneous minimum of average surface roughness (Ra) and cutting force under the cutting parameter constraints of cutting speed, feed rate, depth of cut, and tool nose radius in a range of 50–375 m/min, 0.02–0.25 mm/rev, 0.1–1.5 mm, and 0.4–0.8 mm, respectively. The present study uses five ML models – namely SVR, CAT, RFR, GBR, and ANN – to predict Ra and cutting force. Results indicate that ANN offers the best predictive performance in respect of all accuracy metrics: root-mean-squared-error (RMSE), mean-absolute-error (MAE), and coefficient of determination (R2). In addition, a hybridization of NSGA-II and ANN is implemented to find the optimal solutions for machining parameters, which lie on the Pareto front. The results of this multi-objective optimization indicate that Ra lies in a range between 1.032 and 1.048 μm, and cutting force was found to range between 7.981 and 8.277 kgf for the five selected Pareto solutions. In the set of non-dominated keys, none of the individual solutions is superior to any of the others, so it is the manufacturer's decision which dataset to select. Results summarize the value range in the Pareto solutions generated by NSGA-II: cutting speeds between 72.92 and 75.11 m/min, a feed rate of 0.02 mm/rev, a depth of cut between 0.62 and 0.79 mm, and a tool nose radius of 0.4 mm, are recommended. Following that, experimental validations were finally conducted to verify the optimization procedure.
Źródło:
Journal of Machine Engineering; 2023, 23, 1; 133--153
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

    Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies