Thermal analysis of the friction stir welding process based on boundary conditions and operating parameters

Aissani, Mouloud; Bassir, David; Boukraa, Moustafa; Chekifi, Tawfiq; Ighil, N. Tal; Lebaal, Nadhir; Mataoui, Amina

doi:10.3176/proc.2021.4.20

Cited by 16 publications

(5 citation statements)

References 14 publications

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“…In the conventional FSW process, the heat is created by sliding friction and plastic deformation at the tool/workpiece interface [13,14]. The initial temperature materiel and the ambient temperature are supposed to be 300 K, the velocity and heat boundary conditions for the tool/workpiece contact interfaces are defined in detail as follows.…”

Section: Boundary Conditionmentioning

confidence: 99%

Transient Numerical Simulation the Friction Stir Welding Process of Aa 2017 Alloys, Contact Parameter Effects on Thermal History and Material Flow

Boukraa

Chekifi²

2022

KEM

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The present work is a 3D transient numerical simulation of material flow and heat transfer during the FSW process. The equations of energy, conservation of mass and momentum were solved in the Eulerian coordinate, in which the spatially variable thermo-physical properties and the non-Newtonian viscosity for the flow of material was calculated taking into account the deformation velocity, the temperature and the properties of the material depending on the temperature. In the purpose of calibrating the proposed model, the effects of contact conditions at the tool / work-piece interface such as the friction coefficient and the slip coefficient on thermal cycles, distribution and the evolution of the maximum value of the temperature have been studied. However, the results obtained show a significant asymmetry of the temperature and mass distribution. Convective heat transport was an important heat transfer mechanism near the tool surface. Furthermore, the numerically simulated temperature value, cooling rates and the length of the thermo-mechanically and thermally affected zone increase with increasing slip coefficient.

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Section: Boundary Conditionmentioning

confidence: 99%

Transient Numerical Simulation the Friction Stir Welding Process of Aa 2017 Alloys, Contact Parameter Effects on Thermal History and Material Flow

Boukraa

Chekifi²

2022

KEM

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“…Internet of Things coupled and industry 4.0 concepts [1], [2] have revolutionized the data acquisition techniques, sensors, information processing, and digital twin models. It has opened new research paths for many industries including welding [3]- [5]. Welding is a fabrication process that joins materials, usually metals, by melting the workpieces and adding a filler material to create a strong bond when the melted material cools and solidifies.…”

Section: Introductionmentioning

confidence: 99%

Integration of the AI and ML Approaches for Prediction Analysis in Welding: Review

Bassir

2023

Advances in Transdisciplinary Engineering

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Welding techniques are presents in almost all industries from automotive, aerospace, building, etc. Despite the fact that the technology behind welding remain simple, it is very complicated to characterize and predict the final mechanics responses without considering all the process of the welding. In particular when we refer to the fatigue as the main constraints are located at these singularities areas. Such challenges include poorly controlled welding parameters and weld geometry, which lead to weld quality problems. Nowadays, with the recent involvement of Artificial intelligence (AI) and Machine Learning (ML) into the industry 4.0, it was just matter of time before it is applied to welding problems. The potential applications of ML and AI in welding are vast, as it should bring more efficient and streamlined compared to what is used to be in the early 90’s. In this review a focus will be done on the recent advanced, and reviews previous investigation on AI applications in welding process control and welding robot control. Quality control of such welds are important when we deal with mass production of sensitive products. The main challenges remain the data acquisition to increase the efficiency of the prediction. Testing are sometime coupled with finite elements simulation for training and testing the ML Perspectives and future challenges regarding the integration and the impact of AI in the welding industry in the era of Industry 4.0.

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“…In addition, Elanchezhian et al, [23] have used the hybrid method GRA-RSM for multi-objective optimization of FSW process parameters of aluminum alloys AA8011. Other interesting works have been done using GRA coupled with other optimization methods such as RSM and algorithm (GA), to optimize the parameters of FSW processes on various manufacturing processes [24,25].…”

Section: Introductionmentioning

confidence: 99%

Hybrid multi-response optimization and analysis of UFSW process using entropy measurement: effect of tool deviation on mechanical properties of an optimum joint

Boukraa,

Chekifi,

Madani

et al. 2023

Int J Adv Manuf Technol

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This study focuses on enhancing weld joints of AA2017 aluminum alloys obtained through the UFSW (Underwater Friction Stir Welding) process. An integrated method utilizing Grey relational entropy measurement as a multi-response optimization tool was employed. The objective was to improve the performance of the weld joint by optimizing the process parameters, including advanced speed, tool rotation speed, and forging force, to enhance tensile strength, elongation, and microhardness. The L9 orthogonal design with three factors and three levels was utilized, and subsequent analysis of variance and confirmation tests were conducted. Furthermore, the influence of lateral deviation of the welding tool on the mechanical properties and microstructural characteristics of the optimal joint was investigated. Experimental results aligned well with the outcomes predicted by the proposed mathematical model, affirming the effectiveness of the optimization method. Notably, the study demonstrated an increase in weld joint efficiency from 68.55% to 93% through the implementation of the optimization approach. The welding tool deviation ranging from -1.5 mm to 1.5 mm showed that the tensile strength of the optimal joint decreases while the strain rate increases when the offset was on the advancing side (AS).

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Thermal analysis of the friction stir welding process based on boundary conditions and operating parameters

Cited by 16 publications

References 14 publications

Transient Numerical Simulation the Friction Stir Welding Process of Aa 2017 Alloys, Contact Parameter Effects on Thermal History and Material Flow

Transient Numerical Simulation the Friction Stir Welding Process of Aa 2017 Alloys, Contact Parameter Effects on Thermal History and Material Flow

Integration of the AI and ML Approaches for Prediction Analysis in Welding: Review

Hybrid multi-response optimization and analysis of UFSW process using entropy measurement: effect of tool deviation on mechanical properties of an optimum joint

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