Effect of pin-profiles on thermal cycle, mechanical and metallurgical properties of friction stir–welded aviation-grade aluminum alloy

Verma, Shubham; Gupta, Manju Lata; Misra, Joy Prakash

doi:10.1177/0954405419832109

Cited by 29 publications

(24 citation statements)

References 34 publications

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“…This non-symmetry is for material flow from the frontal side of the pin to the backside of the pin during welding. 19 During material flow from AS to RS, the solid material transforms into semi-solid; thus, this semi-solid material is cooled on the RS. Therefore, AS has a higher temperature than RS.…”

Section: Resultsmentioning

confidence: 99%

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Effect of process parameters on temperature and force distribution during friction stir welding of armor-marine grade aluminum alloy

Verma

Misra

2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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This research investigates the effect of process parameters on real-time temperature and forces distribution during friction stir welding of AA7039. Experiments are conducted at different rotational speed, welding speed, and tilt angle conditions. For the experimentation, a low-cost real-time force-measuring fixture is indigenously developed in-house. However, eight K-type L-shaped thermocouples are used to examine the real-time temperature distribution. The forces in the z-direction are of a higher magnitude than the x-direction. The maximum force in the z-direction of 3.25 kN is witnessed for 2° tilt angle and a minimum of 2.1 kN for 26 mm/min of welding speed. The maximum force in the x-direction of 0.97 kN is obtained at 2° tilt angle and a minimum of 0.27 kN is obtained at 1.3° tilt angle. The maximum temperature of 390 °C is observed at 1812 r/min, whereas a minimum of 283 °C is observed at 43 mm/min of welding speed. The variations in temperature and force distribution during friction stir welding are also evaluated by utilizing two phenomenological models.

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Section: Resultsmentioning

confidence: 99%

“…Investigators tried to study the mechanical and metallurgical properties of FSW welds under different welding conditions. Verma et al 6 tried to enhance the weld quality of FSW AA6082 plates using different pin geometries. It is found that the square pin provides an improved result.…”

Section: Introductionmentioning

confidence: 99%

Effect of process parameters on temperature and force distribution during friction stir welding of armor-marine grade aluminum alloy

Verma

Misra

2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…During FSW, frictional heat changes the hardness of base materials. [35][36][37] The hardness changes can be completely different between metallic materials and polymeric materials. For this reason, differential scanning calorimetry (DSC) analysis was carried out on PMMA sample to find root of hardness changes in SZ.…”

Section: Hardnessmentioning

confidence: 99%

Study on the effects of tool tile angle, offset and plunge depth on friction stir welding of poly(methyl methacrylate) T-joint

Eyvazian

Derazkola

Elyasi

2019

Proceedings of the Institution of Mechanical Engineers, Part B:

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The effects of tilt angle (TTA), plunge depth (TPD) and offset (TO) of tool in friction stir welding of poly(methyl methacrylate) T-joint were investigated. To understand better the effects of process parameter, thermomechanical simulation of joint was assessed. The results seem to show that at higher TPD and TTA, frictional heat increases. Woven tissue structure joint line forms after friction stir welding of poly(methyl methacrylate) sheets. The distance of woven layers was affected by TPD and TTA, while TO do not significantly affect heat generation of joint. The best material flow and adequate heat are generated at 0 mm TA, 2° TTA and 0.2 mm TPD, respectively. The highest flexural and tensile strength of friction stir welded joint were approximately 93% and 90% of as-received poly(methyl methacrylate), respectively. Crack forking was detected on the fractured surface of flexural samples and crack path was detected in the vicinity of shrinkage holes at fracture surface of tensile samples. These holes and degradation of poly(methyl methacrylate) during friction stir welding process decrease strength and hardness of the joint.

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“…[3][4][5][6][7] More recently, the in-line process monitoring and artificial intelligence (AI) algorithms are applied to this process but are mainly targeting specific material configuration and joint thicknesses. [8][9][10][11][12][13][14] Gibson et al gave a review about control architecture involving closed-loop position, force, torque or hybrid control modes, as well as in-process evaluation systems such as e-NDE V R and MonStir V R . The latter methods use process forces coupled to discrete Fourier transforms, neural network, or dimensional reduction techniques to correlate defect formation or mechanical properties.…”

Section: Introductionmentioning

confidence: 99%

“…3–7 More recently, the in-line process monitoring and artificial intelligence (AI) algorithms are applied to this process but are mainly targeting specific material configuration and joint thicknesses. 8–14…”

Section: Introductionmentioning

confidence: 99%

Machine learning models applied to friction stir welding defect index using multiple joint configurations and alloys

Nadeau

Thériault

Gagné

2020

Proceedings of the Institution of Mechanical Engineers, Part L:

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Friction stir welding process has been studied extensively in the last decades since its early stage. Most of the research done so far is related to the process development including tool design, material weldability, post-weld mechanical behavior, and microstructural properties. More recently, in-line process monitoring and artificial intelligence algorithms are introduced into this process, but mainly to specific material configuration and joint thicknesses. This study will focus on the evaluation of different machine learning approaches including principle component analysis, K-nearest neighbor, multilayer perceptron, single vector machine, and random forest methods on a friction stir welding cell environment. The input variables provided from this cell environment are namely divided into two groups: one group refers to the application variables and the other group is related to the friction stir welding process variables. The application variables target the aluminum alloys, joint configuration, sheet thicknesses, initial mechanical properties, and their chemical composition. The friction stir welding process variables dictate the rotational speed, travel speed, forging force, longitudinal and transverse forces, torque, and specific energy. The output response to model from these machine learning algorithms is the defect index, which has been quantified using high-resolution immersed bath ultrasounds. This nondestructive evaluation technique has been described previously, which can detect defects ≥150 µm in thin sheets. The defect index has been classified into five classes, which is distinguished by the nature of defect, cold weld, or hot weld, as well as the width of the internal volumetric defect upon ultrasound C-scan result. The dataset, which is composed of around 500 various process conditions, has been generated over the last few years and the variables were taken exclusively in constant weld regime and in the force control mode using the output average values. This paper compares the best resulting machine learning methods applied on a friction stir welding cell basis, which is the K-nearest neighbor and multilayer perceptron algorithms. The K-nearest neighbor model reaches a deviation of 0.55 on the defect index in comparison with the experimental values, which is slightly better than the multilayer perceptron model, which obtains a score of 0.69. Over the initial 59 available model parameters, 10 and 15 of them were retained in the final algorithm using these techniques. The main predictors include the material thickness, base material ultimate tensile stress, rotational speed, travel speed, weld forces, and specific energy. The K-nearest neighbor model was able to provide a map of defect indices with regard to rotational speed and travel speed but was only possible when a higher density of data was found within the prediction area. A data density score was also included within the model to inform the end-user about the prediction reliability. The machine learning models are mainly about differentiating various cases rather than representing the physical phenomena as determined using the finite element analysis. That being said, in order to improve the prediction reliability as well as the machine learning models, the data twinning concept, which consists of generating simulated friction stir welding process conditions by finite element analysis, is briefly discussed.

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Effect of pin-profiles on thermal cycle, mechanical and metallurgical properties of friction stir–welded aviation-grade aluminum alloy

Cited by 29 publications

References 34 publications

Effect of process parameters on temperature and force distribution during friction stir welding of armor-marine grade aluminum alloy

Effect of process parameters on temperature and force distribution during friction stir welding of armor-marine grade aluminum alloy

Study on the effects of tool tile angle, offset and plunge depth on friction stir welding of poly(methyl methacrylate) T-joint

Machine learning models applied to friction stir welding defect index using multiple joint configurations and alloys

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