Prediction and optimization of processing parameters in wire and arc-based additively manufacturing of 316L stainless steel

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dc.contributor.authorLe, Van Thao
dc.contributor.authorDoan, Quang Thanh
dc.contributor.authorMai, Dinh Si
dc.contributor.authorBui, Manh Cuong
dc.contributor.authorTran, Hoang Son
dc.contributor.authorTran, Xuan Van
dc.contributor.authorNguyen, Van Anh
dc.date.accessioned2022-08-17T11:00:34Z
dc.date.available2022-08-17T11:00:34Z
dc.date.issued2022-08-10
dc.description.abstractWire and arc-based additively manufacturing (WAAM) is a potential metallic additively manufacturing (AM) technologies for producing large-size metallic components. 316L is one of the most common stainless-steel grades used in WAAM. However, most of previous studies normally adopted process parameters for the WAAM process based on recommendations of welding wire manufacturers for traditional welding processes. In this article, we focus on predicting and optimizing process parameters for the WAAM process of 316L stainless steel. The experiment was designed by using Taguchi method and L16 orthogonal array. Three parameters, consisting of voltage (U), welding current (I), and travel speed (v), were considered as the input variables, and the responses are four geometrical characteristics of single weld beads, including width, height, penetration, and dilution of weld beads (WWB, HWB, PWB, and DWB, respectively). The effects of each input variable on the responses were determined through analysis of variance (ANOVA). The optimal process parameters were identified by using GRA (grey-relational analysis) and TOPSIS (techniques for order-preferences by similarity-to-ideal solution) methods. The obtained results show that the travel speed has the most important effect on WWB and HWB, while the voltage has the highest impact on PWB and DWD. Both GRA and TOPSIS methods give the same optimum process parameters, namely U = 22 V, I = 110 A, and v = 0.3 m/min, which are validated by confirmation experiments. The predicted models of WWB, HWB, PWB, and DWB were also demonstrated to be adequate for selecting the process parameters in specific applications.en_UK
dc.identifier.citationLe VT, Doan QT, Mai DS, et al., (2022) Prediction and optimization of processing parameters in wire and arc-based additively manufacturing of 316L stainless steel. Journal of the Brazilian Society of Mechanical Sciences and Engineering, Volume 44, Issue 9, September 2022, Article number 394en_UK
dc.identifier.issn1678-5878
dc.identifier.urihttps://doi.org/10.1007/s40430-022-03698-2
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18325
dc.language.isoenen_UK
dc.publisherSpringeren_UK
dc.subjectWAAMen_UK
dc.subjectStainless steelen_UK
dc.subjectOptimizationen_UK
dc.subjectProcess parameteren_UK
dc.subjectGRAen_UK
dc.subjectTOPSISen_UK
dc.titlePrediction and optimization of processing parameters in wire and arc-based additively manufacturing of 316L stainless steelen_UK
dc.typeArticleen_UK

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