Torque-based adaptive temperature control in friction stir welding: a feasibility study

Bachmann, Andreas; Roehler, M.; Pieczona, Sebastian J.; Keßler, Manuel; Zaeh, Michael F.

doi:10.1007/s11740-018-0798-z

Cited by 5 publications

(7 citation statements)

References 21 publications

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“…The temperature T was the output of the dynamic observer gain and results therefore from the torque model error. To calculate the spindle torque Ms , torque losses inside the motor have to be considered yielding the spindle torque model [18]:…”

Section: Digital Twinmentioning

confidence: 99%

“…The digital twin is primarily based on physical laws of the FSW process. In contrast to the approach suggested by Bachmann et al [18], the applicability to different welding tasks is therefore facilitated. The digital twin was implemented and validated through welding experiments with an industrial robot.…”

Section: Introductionmentioning

confidence: 97%

“…Therefore, several approaches for controlling the welding temperature during FSW have been postulated over the years [6][7][8][9][10][11][12][13][14][15][16][17]. Studies have shown that joint defects, such as flash formation, can be reduced or even prevented [6,8,18] and the layer thickness of intermetallic compounds in dissimilar joints can be adjusted by temperature control [19]. Furthermore, mechanical properties such as the ultimate tensile strength (UTS) of the weld are primarily affected by the welding temperature [4,12].…”

Section: Introductionmentioning

confidence: 99%

“…For this reason, Bachmann et al [18] proposed an alternative to determine the welding temperature. Here, a regression model correlating the welding temperature with the process torque and the rate of the revolutions per minute (RPM-rate) was successfully implemented as a soft sensor (see [23]) in an FSW machine and used for temperature-controlled welding.…”

Section: Introductionmentioning

confidence: 99%

“…Thereby, a maximum error of 15 K was achieved. Even though this approach proved to be promising for controlling the temperature without requiring additional temperature measuring equipment, Bachmann et al [18] acknowledge that the empirical approach is only valid for one specific tool geometry and aluminum alloy. Nevertheless, the existence of a physical correlation between the welding temperature and the process torque was proven and used successfully for state feedback control.…”

Section: Introductionmentioning

confidence: 99%

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Torque-Based Temperature Control in Friction Stir Welding by Using a Digital Twin

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Friction stir welding (FSW) is an innovative solid-state welding technology that produces high quality joints and is widely used in the aerospace industry. Previous studies have revealed welding temperature to be a decisive factor for joint quality. Consequently, several temperature control systems for FSW have been developed. These output feedback control systems usually require delicate and expensive temperature measuring equipment, which reduces their suitability for industrial practice. This paper presents a novel state feedback system of the welding temperature to remedy this shortcoming. The system uses a physical model of the FSW process (digital twin) for the determination of the welding temperature signal from the process torque signal. The digital twin is based on a multi-input nonlinear time invariant model, which is fed with the torque signal from the spindle motor. A model-based L1 adaptive controller was employed for its robustness with respect to model inaccuracies and fast adaption to fluctuations in the controlled system. The experimental validation of the feedback control system showed improved weld quality compared to welded joints produced without temperature control. The achieved control accuracies depended on the results of the temperature calculation. Control deviations of less than 10 K could be achieved for certain welding parameters, and even for a work piece geometry, which deliberately caused heat accumulation.

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Section: Digital Twinmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%