Application of machine learning approaches to predict joint strength of friction stir welded aluminium alloy 7475 and PPS polymer hybrid joint

Abstract

Vehicle weight has been a critical concern in the aerospace and automobile industries for decades. Integrating dissimilar aluminium and polymer hybrid structures is beneficial for weight reduction without affecting structural performance. In the present work, aluminium alloy 7475 and polyphenylene sulfide (PPS) sheets were joined using the friction stir welding (FSW) technology in lap joint configuration. A series of FSW experiments have been performed by the design matrix developed using response surface methodology. Tensile lap shear strength (TLS) is calculated for each experimental run. In this study, an attempt has been made to assess the potential of machine learning algorithms to predict the TLS of the joint. It was found that the support vector machine (SVM) model with RBF kernel was the most effective for predicting the TLS. Furthermore, FSW process parameters are optimized by means of the desirability approach. The optimal set to attain maximum TLS is identified as the tilt angle of 2°, welding speed of 5.12 mm/min and tool rotational speed of 1185.92 r/min. Finally, a confirmation test was performed to validate the optimal set and the adequacy of the developed SVM model. From the confirmation test, the error percentage between experimental and prediction values is less than 5%. Metallographic analysis revealed that the joining mechanism is the macro and micromechanical interlocking assisted by chemical bonding.

Enabling electrical response through piezoelectric particle integration in AA2017-T451 aluminium parts using FSP technology

“…Scanning electron microscope (SEM) and EDX analyses were conducted using a Zeiss Merlin VP compact SEM with a Bruker XFlash EDX detector. The accelerating voltage was 15 kV, and the working distances were approximately between [12,22] mm.…”

“…This paper delves into the intricate interplay between selfsensing capabilities and the piezoelectric properties of PZT and BT, with a particular focus on their potential application in aluminium components. Aluminium, widely used for its lightweight and corrosion-resistant properties in aerospace, automotive, and structural applications, holds significant promise for implementing advanced SHM technologies [19][20][21][22].…”

Friction stir welding of polypropylene based graphene nanocomposites fabricated with 3-D printing: An investigation on the microstructure and mechanical properties

“…Prasad et al 20 implemented the FSW of PP/Al 2 O 3 nanocomposites and stated that the increase of rotational speed improved the tensile and flexural strengths of weld joint, while the addition of Al 2 O 3 content up to 10 wt% increased the hardness and tensile strength of the weld joint. The feasibility of FSW process of the polymer nanocomposites was also investigated by Azarsa et al, 21 Alyali et al 22 and Junior et al 23,24 The FSW of dissimilar materials such as aluminium alloy 7475 and polyphenylene sulfide thermoplastic polymer was performed by Sandeep et al 25,26 They reported that the maximum tensile strength attained at tilt angle of 2°, welding speed of 5.12 mm/min and tool rotational speed of 1185.92 rpm. In accordance with the metallographic investigation, they also observed that different pin profiles of welding tool have distinct bonding mechanisms.…”

Optimization of the Mechanical Property of Friction Stir Welded Heat Treatable Aluminum Alloy by using Bio-Inspired Artificial Intelligence Algorithms

“…Investigated was how well machine learning methods might predict the joint's TLS. The most effective method for predicting the TLS was found to be the support vector machine (SVM) framework with RBF kernel [23]. Guan et al [24] provided a method for creating machine learning models driven by force data that accurately anticipate faults and their categories in friction stir welding (FSW).…”