Force measurement-based discontinuity detection during friction stir welding

Shrivastava, Amber; Zinn, Michael; Duffie, Neil A.; Ferrier, Nicola; Smith, Christopher B.; Pfefferkorn, Frank E.

doi:10.1016/j.jmapro.2017.01.007

Cited by 30 publications

(3 citation statements)

References 16 publications

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“…The periodic material flow, sticking and slipping contact condition at the tool-workpiece interface and banded structure in the SZ are consistent with the periodic waveforms of F x and F y [26,28,50]. Moreover, the high-frequency harmonics, such as A 2Fx , A 3Fx , A 2Fy and A 3Fy , are close related to the material flow and plastic deformation behavior during FSW [27,47,51,52]. The above respects are believed as the key factors in improving the accuracy of the force data-driven ML model for predicting the proportion of M-A constituents and impact toughness of SZ.…”

Section: Prediction Of M-a Constituent and Impact Toughnesssupporting

confidence: 72%

Prediction of M–A Constituents and Impact Toughness in Stir Zone of X80 Pipeline Steel Friction Stir Welds

Wang

et al. 2022

Acta Metall. Sin. (Engl. Lett.)

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This study explored a strategy for predicting the proportion of martensite-austenite (M-A) constituents and impact toughness of stir zone (SZ) on X80 pipeline steel joints welded by friction stir welding (FSW). It is found that the welding forces, including the traverse force (F x ), the lateral force (F y ) and the plunge force (F z ), are the key variables related to the change of welding parameters and influence remarkably the characteristics of M-A constituents and impact toughness of SZ. The impact toughness of SZ is commonly lower than that of the base material due to the formation of lath bainite and coarsening of austenite. The characteristics of M-A constituents in SZ are sensitive to the variation of welding parameters and respond well to the change of welding forces. The proportion of small island M-A constituents increases with the decrease in rotational speed and the increase in F z . The increase in the amount of island M-A constituents is beneficial to improve the impact toughness of SZ. Based on the above findings, a machine learning (ML) model for predicting the M-A constituents and impact toughness is constructed using the force features as the input data set. The force data-driven ML model can predict the M-A constituents and impact toughness precisely and exhibits higher accuracy than ML built with welding parameters. It is believed that the high accuracy is achieved because the force features include more details of FSW process, such as the heat generation, material flow, plastic deformation, and so on, which govern the microstructural evolution of SZ during FSW.

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Section: Prediction Of M-a Constituent and Impact Toughnesssupporting

confidence: 72%

Prediction of M–A Constituents and Impact Toughness in Stir Zone of X80 Pipeline Steel Friction Stir Welds

Wang

et al. 2022

Acta Metall. Sin. (Engl. Lett.)

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“…due to the frictional heat generation, that is, induced due to the contact between tool and workpiece. [12][13][14] A non-consumable friction stir-rotating tool has rotated, which follows the forging and extrusion process to attribute and plunge into the material with downward forces along the weld line. It was noticed that the TRS is the main influencing parameter among all input parameters [tool tilt angle (TTA), tool traverse speed (TTS), plunge depth (PD), tool geometry, and substrate] that enhanced the weld quality of material through generation of frictional heat.…”

Section: Introductionmentioning

confidence: 99%

Prediction of heat generation effect on force torque and mechanical properties at varying tool rotational speed in friction stir welding using Artificial Neural Network

Kumar

Triveni

Katiyar

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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Friction stir welding (FSW) has played a significant role in joining aerospace alloys. During this process, the tool rotational (TRS) speed has been found to significantly affect heat generation compared to other parameters. Therefore, the study has investigated the effect of heat generation on force-torque and mechanical properties at different tool rotational speeds (TRS) in the FSW process through experimentation followed by Artificial Neural Network (ANN) technique. Further, the influence of different TRS ranging between 600 and 1800 rpm with an increment of 400 rpm on considered responses; namely thermal weld cycle, microstructure, and grain distribution in nugget zone (NZ) for 2050-T84 Al-Cu-Li alloy plates, welded using FSW were also investigated. It is observed that the vertically downward force (Z-force), longitudinal force (X-force), and spindle torque (Sp. T) decrease with increasing TRS. It is also observed an increasing (up to 1400 rpm) and then decreasing trend for tensile strength and hardness of welded samples. Moreover, the generation of frictional heat and grain size in NZ is increased with increasing TRS from 600 to 1800 rpm. However, the scanning electron microscope (SEM) micrographs of all-welded samples revealed a ductile mode of tensile fracture. Furthermore, the obtained experimental results were validated using the ANN technique. A quite better agreement has been established among the predicted outcomes from ANN with experimental results.

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“…On the contrary, by increasing the welding speed, the welding force increases due to the less time available for the heat to diffuse and soften the material [18]. Furthermore, as far as the occurrence of defects is concerned, it has been suggested the correlation between defective joints and irregular evolution of in-plane forces [19,20]. Hence, a correlation between anomalies in the oscillatory nature of the in-plane forces and defects has been demonstrated [21].…”

Section: Introductionmentioning

confidence: 99%

Plastic behavior-dependent weldability of heat-treatable aluminum alloys in friction stir welding

Ambrosio

Wagner

Dessein

et al. 2021

Int J Adv Manuf Technol

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The quality of friction stir welding joints is intimately related to the correct mixing of the stirred material. The material flow is strongly dependent on the plastic behavior of the welded alloy. For this reason, the friction stir weldability depends on the structure, microstructure and chemical composition of the base material. In this work, in-plane forces and acoustic emission signals were monitored while welding two heat-treatable aluminum alloys. The forces evolutions suggested possible continuous and intermittent material flow during friction stir welding depending on the welding parameters. The differences observed in the in-plane forces were corroborated by acoustic emission, confirming the modification in the material flow phenomenology. Therefore, differences observed in aluminum alloys' friction stir weldability are due to the plastic behavior at high temperature and medium-high strain rate. The higher the deformability of the aluminum alloys, the wider the weldability window in friction stir welding because of continuous material flow in an extended range of process parameters.

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Force measurement-based discontinuity detection during friction stir welding

Cited by 30 publications

References 16 publications

Prediction of M–A Constituents and Impact Toughness in Stir Zone of X80 Pipeline Steel Friction Stir Welds

Prediction of M–A Constituents and Impact Toughness in Stir Zone of X80 Pipeline Steel Friction Stir Welds

Prediction of heat generation effect on force torque and mechanical properties at varying tool rotational speed in friction stir welding using Artificial Neural Network

Plastic behavior-dependent weldability of heat-treatable aluminum alloys in friction stir welding

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