Experimental investigations on mechanical properties of multi-layered structure fabricated by GMAW-based WAAM of SS316L

Vora, Jay; Parmar, Heet; Chaudhari, Rakesh; Khanna, Sakshum; Doshi, Mikesh; Patel, Vivek

doi:10.1016/j.jmrt.2022.08.074

Cited by 56 publications

(16 citation statements)

References 30 publications

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“…The hardness measurement results corresponded to previous studies on 316L WAAM printed parts. Vora et al [10] investigated the mechanical properties of a multi-layered 316L structure manufactured by WAAM. Their observation was that the average hardness of the structure was approximately 180.8 HV.…”

Section: B Hardness and Surface Roughnessmentioning

confidence: 99%

Static Properties and Fatigue Strength of Wire Arc Additive Manufactured 316L

Rautio

Mäkikangas

Jaskari

et al. 2022

2022 7th National Scientific Conference on Applying New Technology in Green Buildings (ATiGB)

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The use of direct energy deposition (DED) has become much more common recently and in the future the DED technology will be used even more widely. Wire arc additive manufacturing (WAAM) technology has become much more widespread because it can be done cost-effectively with normal cold metal transfer (CMT) welding equipment. There is still a lack of information on the fatigue strength of parts made with different welding wires. This paper focuses on the mechanical properties of AISI 316L WAAM printed parts as well as bending fatigue strength. The average microhardness values were measured along the build direction. The optical microscope and EBSD detector were used to investigate the microstructure. The tensile strength and bending fatigue strength of the WAAM printed 316L were evaluated. The EBSD investigation revealed that due to high heat input of the WAAM process, the remelting of each consecutive deposited layer caused epitaxial grain growth in the structure, leading to coarse columnar grain structure. There were no visible pores in the microstructure of the WAAM 316L. The hardness of the WAAM 316L was homogenous in the build direction. Based on tensile strength tests, the strength in the deposition direction was greater than in the build direction which is explained by the anisotropy generated in the deposition. The results revealed that the fatigue limit of the WAAM 316L is comparable to that of 316L sheet metal. The fatigue strength of the WAAM 316L was significantly higher in the high cycle fatigue regime compared to the fatigue strength of the powder bed fusion (PBF-LB) printed 316L.

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Section: B Hardness and Surface Roughnessmentioning

confidence: 99%

Static Properties and Fatigue Strength of Wire Arc Additive Manufactured 316L

Rautio

Mäkikangas

Jaskari

et al. 2022

2022 7th National Scientific Conference on Applying New Technology in Green Buildings (ATiGB)

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“…The researchers identified how the tensile properties were similar to those of 1.25Cr-0.5Mo metal-cored wire [ 14 ]. Similarly, Vora et al [ 15 ] identified how the WAAM process also allows obtaining tensile properties close to the range of wrought SS 316 L.…”

Section: Introductionmentioning

confidence: 99%

Characterization of 5356 Aluminum Walls Produced by Wire Arc Additive Manufacturing (WAAM)

et al. 2023

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Wire arc additive manufacturing (WAAM) is renowned for its high deposition rate, enabling the production of large parts. However, the process has challenges such as porosity formation, residual stresses, and cracking when manufacturing aluminum parts. This study focuses on ana-lyzing the porosity of AA5356 walls manufactured using the WAAM process with the Fronius cold metal transfer system (Wels, Austria). The walls were machined to obtain specimens for tensile testing. The study used computed tomography and the tensile test to analyze the specimens’ porosity and its potential relation to tensile strength. The process parameters analyzed were travel speed, cooling time, and path strategy. In conclusion, increasing travel speed and cooling time significantly affects pore diameter due to the lower heat input to the weld zone. Porosity can be reduced when diminishing heat accumulation. The results indicate that an increase in travel speed produces a slight decrease in porosity. Specifically, the total pore volume diminishes from 0.42 to 0.36 mm3 when increasing the travel speed from 700 to 950 mm/min. The ultimate tensile strength and maximum elongation of the ‘back and forth’ strategy are slightly higher than those of the ‘go’ strategy. After tensile testing, the ultimate tensile strength and yield strength did not show any relation to the porosity measured by computed tomography. The percentage of the pore total volume over the measured volume was lower than 0.12% for all the scanned specimens.

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“…Various studies have reported the validation of the properties of the WAAM parts through experimental analysis, including tensile testing, hardness testing, and microstructural analysis. [27][28][29][30] In summary, the optimization of the process parameters is crucial to achieving the desired performance of the WAAM parts. ML-based approaches, such as ANN, [31,32] DT, [17] Fibonacci sequence-based optimization, [33] Shrimp and Goby association search algorithm, [34] and SVM, [23] have emerged as powerful tools for optimizing the process parameters and property prediction in WAAM compared to traditional methods.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Wire Arc Additive Manufacturing Process Parameters for Low‐Carbon Steel and Properties Prediction by Support Vector Regression Model

Barik,

Bhandari,

Mondal

2023

steel research int.

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This study aims to optimize the process parameters of wire arc additive manufacturing for ER70S6 steel and build a machine‐learning model to predict the properties of deposited specimens. Process parameters such as current, voltage, and travel speed are optimized considering other process parameters constant (gas flow rate, contact tip to the work distance, and preheat). The optimization is made using the response surface method and validated the properties by experimentation, including tensile testing and metallography. A support vector regression machine‐learning model is implemented to predict the material’s properties to substantiate the outcomes’ values in every possible combination for the given parameters. The study’s findings reveal a significant enhancement in specimen quality, marked by reduced irregularities and porosity, and a remarkable increase in ultimate tensile strength up to 40%, validated through the SVR model. This study sketches a valuable path that can be extended to predict properties of other material systems.This article is protected by copyright. All rights reserved.

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Experimental investigations on mechanical properties of multi-layered structure fabricated by GMAW-based WAAM of SS316L

Cited by 56 publications

References 30 publications

Static Properties and Fatigue Strength of Wire Arc Additive Manufactured 316L

Static Properties and Fatigue Strength of Wire Arc Additive Manufactured 316L

Characterization of 5356 Aluminum Walls Produced by Wire Arc Additive Manufacturing (WAAM)

Optimization of Wire Arc Additive Manufacturing Process Parameters for Low‐Carbon Steel and Properties Prediction by Support Vector Regression Model

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