Application of Artificial Neural Network to Friction Stir Welding Process of AA7050 Aluminum Alloy

Dutt, Aniket K.; Sindhuja, K.; Reddy, Siriyapureddy Vijaya Narasimha; Kumar, Pawan

doi:10.1007/978-981-15-4739-3_34

Cited by 3 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, [36] used a Mamdani-type fuzzy logic model in FSW to predict tensile strength and weld seam hardness, and [37] applied the support vector machine (SVM) method and ANN modeling. Furthermore, [38] simulated FSW parameters and responses using an ANN approach with regard to the hardness in the heat-affected zone (HAZ) and the peak temperature in the stir zone and the HAZ. The result was a solution identical to that in the literature.…”

Section: Introductionmentioning

confidence: 99%

A Multiple Response Prediction Model for Dissimilar AA-5083 and AA-6061 Friction Stir Welding Using a Combination of AMIS and Machine Learning

et al. 2023

View full text Add to dashboard Cite

This study presents a methodology that combines artificial multiple intelligence systems (AMISs) and machine learning to forecast the ultimate tensile strength (UTS), maximum hardness (MH), and heat input (HI) of AA-5083 and AA-6061 friction stir welding. The machine learning model integrates two machine learning methods, Gaussian process regression (GPR) and a support vector machine (SVM), into a single model, and then uses the AMIS as the decision fusion strategy to merge SVM and GPR. The generated model was utilized to anticipate three objectives based on seven controlled/input parameters. These parameters were: tool tilt angle, rotating speed, travel speed, shoulder diameter, pin geometry, type of reinforcing particles, and tool pin movement mechanism. The effectiveness of the model was evaluated using a two-experiment framework. In the first experiment, we used two newly produced datasets, (1) the 7PI-V1 dataset and (2) the 7PI-V2 dataset, and compared the results with state-of-the-art approaches. The second experiment used existing datasets from the literature with varying base materials and parameters. The computational results revealed that the proposed method produced more accurate prediction results than the previous methods. For all datasets, the proposed strategy outperformed existing methods and state-of-the-art processes by an average of 1.35% to 6.78%.

show abstract

Section: Introductionmentioning

confidence: 99%

A Multiple Response Prediction Model for Dissimilar AA-5083 and AA-6061 Friction Stir Welding Using a Combination of AMIS and Machine Learning

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Likely these applications machine learning is also being used in Friction Stir Welding process. Dutt et al [8 ] developed artificial neural network model for studying about the correlation between the Friction Stir Welding input parameters such as a rotation rate and traverse rate with the mechanical property of friction stir welded precipitation strengthened AA7050 aluminum alloys. Hartl et al [9] used Bayesian optimization and reinforcement learning method to improve the surface quality of friction stir welded joints.…”

Section: Introductionmentioning

confidence: 99%

Discrete Wavelet Transformation Approach for Surface Defects Detection in Friction Stir Welded Joints

Mishra

2020

Fatigue of Aircraft Structures

View full text Add to dashboard Cite

Friction Stir Welding joint quality depends on input parameters such as tool rotational speed, tool traverse speed, tool tilt angle and an axial force. Surface defects formation occurs when these input parameters are not selected properly. The main objective of the recent paper is to develop Discrete Wavelet Transform algorithm by using Python programming and further subject it to the Friction Stir Welded samples for the identification of various external surface defects present.

show abstract

Investigation of AA6063-based metal–matrix composites reinforced with TiO2 dispersoids through digitally assisted techniques for mechanical, tribological, and microstructural characterizations

Pattar,

Ramesh,

Malghan

et al. 2024

Front. Mech. Eng.

View full text Add to dashboard Cite

Aluminum metal–matrix composites (AMMCs) were prepared by dispersing TiO2 dispersoids of different volume fractions into an AA6063 matrix via stir casting and subjected to process–structure correlation studies. Four different samples based on weight ratio were considered herein: 99Al-1TiO2, 97Al-3TiO2, 95Al-5TiO2, and the as-received AA6063. Their mechanical properties namely, microhardness, tensile strength, and tribological behavior, were determined. In addition, the microstructure of the samples was also analysed. It was observed that the addition of 5% TiO2 particles enabled the AA6063 matrix to accommodate a higher strain energy while providing the required driving force to generate dislocations and substructures. Therefore, considering the plastic deformation, the ultimate tensile strength σut increased gradually with the addition of TiO2 (in weight%). The flow curves of the 95Al-5TiO2 sample showed the highest value of σut, whereas the as-received AA6063 matrix exhibited the lowest value. For linear elastic deformation, AA6063 showed the lowest yield strength (σys) as compared to the AMMC samples for all TiO2 weight% values; however, the variation in σys among the AMMC samples was minimal. The microhardness of the samples increased gradually with the addition of TiO2, and the percentage reduction in area at the fracture was largest for 95Al-5TiO2. The Taguchi’s L9 array and variance analysis of the process parameters indicated that the material wear was largely affected by the normal load, followed by weight% of TiO2 and sliding speed. Wear surface characteristics, such as microvoids, delamination, microcracks, and wear debris, were qualitatively observed in all the AMMC samples. The overall strength improvement was attributable to the effects of addition of the dispersoids. During melt solidification, the TiO2 particles surpassed/pinned and hindered the grain growth, resulting in grain-size refinement.

show abstract

Application of Artificial Neural Network to Friction Stir Welding Process of AA7050 Aluminum Alloy

Cited by 3 publications

References 11 publications

A Multiple Response Prediction Model for Dissimilar AA-5083 and AA-6061 Friction Stir Welding Using a Combination of AMIS and Machine Learning

A Multiple Response Prediction Model for Dissimilar AA-5083 and AA-6061 Friction Stir Welding Using a Combination of AMIS and Machine Learning

Discrete Wavelet Transformation Approach for Surface Defects Detection in Friction Stir Welded Joints

Investigation of AA6063-based metal–matrix composites reinforced with TiO2 dispersoids through digitally assisted techniques for mechanical, tribological, and microstructural characterizations

Contact Info

Product

Resources

About