A review on sensor based monitoring and control of friction stir welding process and a roadmap to Industry 4.0

Mishra, Debasish; Roy, Rohan Basu; Dutta, Samik; Pal, Surjya K.; Chakravarty, Debashish

doi:10.1016/j.jmapro.2018.10.016

Cited by 128 publications

(72 citation statements)

References 150 publications

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“…For this purpose, the test accuracy for predicting the surface quality of the welds were determined by evaluating the force F z . In the first step the F z signal was evaluated, because according to Mishra et al, 3 the force and torque signals can be considered the best options for real time monitoring and control of the FSW process. The pre-processing of the F z signal for the FFNNs, the RNNs, and the CNNs had in common that the recorded raw signals were at first sampled with a frequency of 5.0 kHz.…”

Section: Pre-processing Of the Process Datamentioning

confidence: 99%

“…With regard to the surface topography (Case 2), the same conditions for a good surface topography as defined in equation 3 shows the results of the evaluation of the surface topography according to equation (3). In order to determine the balanced accuracy for the prediction of the topographical properties, the same investigations as for the prediction of the visual examination were performed.…”

Section: En Aw-6082 T6mentioning

confidence: 99%

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Prediction of the surface quality of friction stir welds by the analysis of process data using Artificial Neural Networks

Hartl

Praehofer

Zaeh

2020

Proceedings of the Institution of Mechanical Engineers, Part L:

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Friction stir welding is an advanced joining technology that is particularly suitable for aluminum alloys. Various studies have shown that welding quality depends significantly on the welding speed and the rotational speed of the tool. It is frequently possible to detect an unsuitable setting of these parameters by examining the resulting surface defects, such as increased flash formation or surface galling. In this work, Artificial Neural Networks were used to analyze process data in friction stir welding and predict the resulting quality of the weld surface. For this purpose, nine different variables were recorded during friction stir welding of EN AW-6082 T6 sheets: the forces and accelerations in three spatial directions, the spindle torque, and temperatures at the tool shoulder and tool probe. In Case 1, the welds were assigned to the classes good and defective on the basis of a human visual inspection of the weld surface. In Case 2, the welds were categorized into the two classes on the basis of a surface topography analysis. Subsequently, three different major Artificial Neural Network architectures were tested for their ability to predict the surface quality: Feed Forward Fully Connected Neural Networks, Recurrent Neural Networks and Convolutional Neural Networks. The highest classification accuracy was achieved when Convolutional Neural Networks were used. Thus, the evaluation of the force signal transverse to the welding direction yielded the highest accuracy of 99.1% for the prediction of the result of the human visual inspection. The result achieved for the prediction of the topography analysis was an accuracy of 87.4% when the spindle torque was evaluated. Using all nine different process variables to predict the topography analysis, the accuracy could be improved slightly to 88.0%. The sampling rate of the spindle torque was varied between 40 Hz and 9600 Hz and no significant influence was determined. The findings show that Convolutional Neural Networks are well suited for the interpretation of friction stir welding process data and can be used to predict the resulting surface quality. In future work, the results are to be used to develop a parameter optimization method for friction stir welding.

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Section: Pre-processing Of the Process Datamentioning

confidence: 99%

Section: En Aw-6082 T6mentioning

confidence: 99%

Prediction of the surface quality of friction stir welds by the analysis of process data using Artificial Neural Networks

Hartl

Praehofer

Zaeh

2020

Proceedings of the Institution of Mechanical Engineers, Part L:

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“…Friction stir welding process [226]: Weld seam in FSW may also be monitored using a visual sensor like in arc and laser welding. At Vanderbilt University, a method similar to through-the-arc seam tracking in arc welding has been developed [227] by weaving the FSW tool back-and-forth perpendicular to the direction of travel during welding.…”

Section: Sensing and Controlmentioning

confidence: 99%

Advanced Welding Manufacturing: A Brief Analysis and Review of Challenges and Solutions

Zhang

Yang

Wei

et al. 2020

Journal of Manufacturing Science and Engineering

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Welding is a major manufacturing process that joins two or more pieces of materials together through heating/mixing them followed by cooling/solidification. The goal of welding manufacturing is to join materials together to meet service requirements at lowest costs. Advanced welding manufacturing is to use scientific methods to realize this goal. This paper views advanced welding manufacturing as a three step approach: (1) pre-design that selects process and joint design based on available processes (properties, capabilities, and costs); (2) design that uses models to predict the result from a given set of welding parameters and minimizes a cost function for optimizing the welding parameters; (3) real-time sensing and control that overcome the deviations of welding conditions from their nominal ones used in optimizing the welding parameters by adjusting the welding parameters based on such real-time sensing and feedback control. The paper analyzes how these three steps depend on process properties/capabilities, process innovations, predictive models, numerical models for fluid dynamics, numerical models for structures, real-time sensing, and dynamic control. The paper also identifies the challenges in obtaining ideal solutions and reviews/analyzes the existing efforts toward better solutions. Special attention and analysis have been given to (1) gas tungsten arc welding (GTAW) and gas metal arc welding (GMAW) as benchmark processes for penetration and materials filling; (2) keyhole plasma arc welding (PAW), keyhole-tungsten inert gas (K-TIG), and keyhole laser welding as improved/capable penetrative processes; (3) friction stir welding (FSW) ......

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“…Sensors are being used to control the force, torque, temperature, current power vibration etc. during FSW process [6].…”

Section: Introductionmentioning

confidence: 99%

Effect of SiC/Si3N4 micro-reinforcement on mechanical and wear properties of friction stir welded AA6061-T6 aluminum alloy

Kumar¹,

Patel

2020

SN Appl. Sci.

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Friction stir welding (FSW) is extensively used to join aluminum alloys components for applications in structural, aviation and automotive sectors. In this investigation, the mechanical properties (tensile strength, percentage elongation, and microhardness), abrasive wear properties and microstructure of friction stir welded joint of AA6061-T6 were studied by inter-plate reinforcing of silicon carbide microparticles (µSiC) and silicon nitride microparticles (µSi 3 N 4). The friction stir welding was performed at different tool rotational speed (TRS) (750, 1000 and 1400 rpm) and welding speed of 56, 80 and 112 mm/min. The friction stir welded joint without reinforcement and with µSiC exhibited maximum tensile strength at two combinations of welding speed and tool rotational speed; one at welding speed of 112 mm/min, TRS of 1000 RPM and other at welding speed of 56 mm/min and TRS of 1400 rpm. It was realized that the reinforcement of µSiC and µSi 3 N 4 decreased the tensile properties of the welded joint but improved the abrasive wear and microhardness properties considerably in the stir zone. The reinforcement of µSiC during FSW demonstrated better tensile, microhardness and abrasive wear resistant properties than the FSW joints with µSi 3 N 4 reinforcement. The improvement in microhardness of the FSW joint reinforced with SiC and Si 3 N 4 microparticles is attributed to the pinning effect produced by the hard particles inclusions.

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A review on sensor based monitoring and control of friction stir welding process and a roadmap to Industry 4.0

Cited by 128 publications

References 150 publications

Prediction of the surface quality of friction stir welds by the analysis of process data using Artificial Neural Networks

Prediction of the surface quality of friction stir welds by the analysis of process data using Artificial Neural Networks

Advanced Welding Manufacturing: A Brief Analysis and Review of Challenges and Solutions

Effect of SiC/Si3N4 micro-reinforcement on mechanical and wear properties of friction stir welded AA6061-T6 aluminum alloy

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