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http://dx.doi.org/10.25673/36639
Titel: | Analysis of CSN 12050 carbon steel in dry turning process for product sustainability optimization using taguchi technique |
Autor(en): | Wakjira, Melesse Workneh Altenbach, Holm Perumalla, Janaki Ramulu |
Erscheinungsdatum: | 2019 |
Art: | Artikel |
Sprache: | Englisch |
URN: | urn:nbn:de:gbv:ma9:1-1981185920-368712 |
Schlagwörter: | Taguchi technique Carbon Steel Dry turning process Product sustainability |
Zusammenfassung: | The aim of this research paper is to investigate the machinability of CSN 12050 carbon steel bars using carbide insert tool in order to utilize the optimum cutting parameters by employing Taguchi approach. Experiments have been performed under dry cutting condition using an optimization approach according to Taguchi’s 𝐿9(34) orthogonal arrays; signal-to-noise ratio tests are designed. Analysis of variance (ANOVA) was performed to determine the importance of machining parameters on thematerial removal rate (MRR).The results were analyzed using signal-to-noise ratios (S/N); 3D surface graphs, main effect graphs of mean, and predictive equations are employed to study the performance characteristics.The optimal parameters resulted as 𝐴3𝐵2𝐶3 (i.e., cutting speed 275 (m/min), depth of cut 0.35 (mm), and feed rate 0.25 (mm/rev), respectively). In the present study, there is an improvement of 5.22 dB at optimal cutting conditions for each significant MRR response parameters such as cutting speed, depth of cut, and feed rate.With these proposed optimal parameters, it is possible to optimize machinability for product sustainability. |
URI: | https://opendata.uni-halle.de//handle/1981185920/36871 http://dx.doi.org/10.25673/36639 |
Open-Access: | ![]() |
Nutzungslizenz: | ![]() |
Sponsor/Geldgeber: | DFG-Publikationsfonds 2019 |
Journal Titel: | Journal of Engineering |
Verlag: | Hindawi Publishing |
Verlagsort: | New York, NY |
Band: | 2019 |
Originalveröffentlichung: | 10.1155/2019/7150157 |
Seitenanfang: | 1 |
Seitenende: | 10 |
Enthalten in den Sammlungen: | Fakultät für Maschinenbau (OA) |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
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Wakjira et al._Analysis_2019.pdf | Zweitveröffentlichung | 2.21 MB | Adobe PDF | ![]() Öffnen/Anzeigen |