Performance evaluation of friction stir welding using machine learning approaches

Verma, Shubham; Gupta, Monika; Misra, Joy Prakash

doi:10.1016/j.mex.2018.09.002

Cited by 60 publications

(31 citation statements)

References 23 publications

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“…Gaussian process regression (GPR) is based on assumption that there is a directional likelihood between input and output for any model of the process. When the input is (x) and output is (y), the conditional distribution of the probabilities become p (y/x) [116]. The regression process by using Gaussian distribution is built on a collection of random values and that every point provided in the model delivers information of the adjacent value afterwards [86,117].…”

Section: Gaussian Process Regressionmentioning

confidence: 99%

Practice of Simulation and Life Cycle Assessment in Tribology—A Review

Kurdi

Alhazmi

et al. 2020

Materials

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To simulate today’s complex tribo-contact scenarios, a methodological breakdown of a complex design problem into simpler sub-problems is essential to achieve acceptable simulation outcomes. This also helps to manage iterative, hierarchical systems within given computational power. In this paper, the authors reviewed recent trends of simulation practices in tribology to model tribo-contact scenario and life cycle assessment (LCA) with the help of simulation. With the advancement of modern computers and computing power, increasing effort has been given towards simulation, which not only saves time and resources but also provides meaningful results. Having said that, like every other technique, simulation has some inherent limitations which need to be considered during practice. Keeping this in mind, the pros and cons of both physical experiments and simulation approaches are reviewed together with their interdependency and how one approach can benefit the other. Various simulation techniques are outlined with a focus on machine learning which will dominate simulation approaches in the future. In addition, simulation of tribo-contacts across different length scales and lubrication conditions is discussed in detail. An extension of the simulation approach, together with experimental data, can lead towards LCA of components which will provide us with a better understanding of the efficient usage of limited resources and conservation of both energy and resources.

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Section: Gaussian Process Regressionmentioning

confidence: 99%

Practice of Simulation and Life Cycle Assessment in Tribology—A Review

Kurdi

Alhazmi

et al. 2020

Materials

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“…Verma et al [10] used various machine learning approaches like Gaussian Process Regression (GPR), Support Vectors Machine (SVM), and Multi Linear Regression (MLR) for performance evaluation in Friction Stir Welding process. It was observed that in comparison to SVM and MLR techniques, GPR approach works better for prediction of the Ultimate Tensile Strength (UTS) of the welded joints.…”

Section: Application Of Machine Learning In Friction Stir Welding Promentioning

confidence: 99%

Detection of Surface Defects in Friction Stir Welded Joints by Using a Novel Machine Learning Approach

Mishra¹,

Dutta²

2020

Applied Engineering Letters

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The Friction stir welding process is a new entrant in welding technology. The FSW joints have high strength and helps in weight saving considerably than the other joining process as no filler material is added during welding. The weld quality is affected because of various kinds of defects occurring during the FSW process. Defects like cavity, surface grooves and flash could occur due to inappropriate set of process parameters which results in excessive or insufficient heat input. Defects analysis can be done by several non-destructive methods like immersion ultrasonic techniques, X-ray radiography, thermography, eddy current testing, synchrotron technique etc. In the present work the image processing techniques are applied over the test samples to detect the surface defects like pin holes, surface grooves etc.

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“…Verma et al [9] used various sophisticated machine learning approaches like . Support Vector Machining (SVM) , Gaussian Process (GP) regression, and multi-linear regression (MLR) for evaluating the friction stir welding process.…”

Section: Application Of Machine Learning In Friction Stir Welding Promentioning

confidence: 99%

Understanding Machine Learning for Friction Stir Welding Technology

Mishra¹

2019

IJMLNCE

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achine learning process drastically decreases the time it takes to develop stronger, lighter materials. This is important to the aerospace, automotive and manufacturing sectors. Machine Learning techniques like Artificial Neural Networks and Image processing are used in a solid state joining process such as Friction Stir Welding process (FSW) for optimization of mechanical properties like Ultimate Tensile Strength (UTS), Fracture Strength , Elongation percentage and microstructure properties like grain size and understanding defects formation. In the recent paper, application of Machine Learning technique in Friction Stir Welding technology will be discussed.

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Performance evaluation of friction stir welding using machine learning approaches

Abstract: Graphical abstract

Cited by 60 publications

References 23 publications

Practice of Simulation and Life Cycle Assessment in Tribology—A Review

Practice of Simulation and Life Cycle Assessment in Tribology—A Review

Detection of Surface Defects in Friction Stir Welded Joints by Using a Novel Machine Learning Approach

Understanding Machine Learning for Friction Stir Welding Technology

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