Artificial neural network modeling studies to predict the friction welding process parameters of Incoloy 800H joints

Anand, K.; Barik, Birendra Kumar; Tamilmannan, K.; Sathiya, P.

doi:10.1016/j.jestch.2015.02.001

Cited by 36 publications

(16 citation statements)

References 16 publications

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“…Anand et al [16] developed a model to establish the input/output relationship of friction welding using ANN. Then, this model was used for optimization of friction parameters by using a force ANN technique [91]. Zakaulla et al [92] predicted coefficient of friction and wear rate of polycarbonate-based composite by using ANN and the main feature parameters were testing conditions and composition of the materials [92].…”

Section: Machine Learningmentioning

confidence: 99%

“…Thus, by careful analysis of the lifespan of each tribo-elements, innovative and creative tribo-techniques at designing can be adopted to extend the lifespan of a given tribo-system. The LCA of any tribo-system is directly and indirectly influenced by a number of participating factors such as the wear-resistant behavior of the system, lubricating behavior, extending life span by lubricating and re-furbishing the components [16,91]. These factors are mostly in-built into any tribo-system.…”

Section: Life Cycle Assessments (Lcas)mentioning

confidence: 99%

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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: Machine Learningmentioning

confidence: 99%

Section: Life Cycle Assessments (Lcas)mentioning

confidence: 99%

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

Kurdi

Alhazmi

et al. 2020

Materials

View full text Add to dashboard Cite

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“…This is the case, for instance, with the work carried out in [ 9 ], where an artificial neural network was designed in order to calculate penetration-to-fuse-widths and penetration-to-haz-widths for different laser powers, welding speeds, and focal positions. In [ 10 ], an ANN-optimization hybrid model was proposed for prediction and optimization of penetration for different welding parameters in CO 2 LASER-MIG welding. Four controllable welding parameters were taken as input, namely power, focal distance from the work piece surface, torch angle, and distance between the laser and welding torches.…”

Section: Introductionmentioning

confidence: 99%

“…The mapping of the process in the reverse direction, i.e., feeding the desired responses as inputs to the model and obtaining, as outputs, the process parameters required to achieve the desired set of responses. This is a valuable support for decision making about the selection of the suitable process parameters, and represents a relevant issue for process automation [ 10 ]. According to the reverse mapping approach, ANN were employed in the present research work, with the aim of estimating the laser welding process parameters (laser power, welding speed, and defocusing distance) required to obtain a weld bead with defined geometrical features (bead crown width, root width, heat affected zone width on the top surface, heat affected zone width on the bottom surface, and area of the fused zone).…”

Section: Introductionmentioning

confidence: 99%

Investigation of Laser Welding of Ti Alloys for Cognitive Process Parameters Selection

Caggiano

2018

Materials

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Laser welding of titanium alloys is attracting increasing interest as an alternative to traditional joining techniques for industrial applications, with particular reference to the aerospace sector, where welded assemblies allow for the reduction of the buy-to-fly ratio, compared to other traditional mechanical joining techniques. In this research work, an investigation on laser welding of Ti–6Al–4V alloy plates is carried out through an experimental testing campaign, under different process conditions, in order to perform a characterization of the produced weld bead geometry, with the final aim of developing a cognitive methodology able to support decision-making about the selection of the suitable laser welding process parameters. The methodology is based on the employment of artificial neural networks able to identify correlations between the laser welding process parameters, with particular reference to the laser power, welding speed and defocusing distance, and the weld bead geometric features, on the basis of the collected experimental data.

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“…Yu Cao et al [10] studied the effects of annealing time and prestrain during the post deformation recrystallization at 1000°C on incoloy 800H grain boundary character and concluded that ∑ 3 regeneration was the main mechanism for the increase in ∑ 3 n boundaries. P.Sathiya et al [11] optimized the friction welding process parameters of incoloy 800H joints using artificial neural network and concluded that the low heating pressure, upsetting time and high upsetting pressure, heating time are required to obtain sound quality joint. Xizang Chen.…”

Section: Introductionmentioning

confidence: 99%

Effects of Heat Input on the Mechanical and Metallurgical Characteristics of Tig Welded Incoloy 800Ht Joints

Kumar¹,

Sathiya²

2017

Archives of Metallurgy and Materials

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This study focuses on the effect of heat input on the quality characteristics of tungsten inert arc gas welded incoloy 800HT joints using inconel-82 filler wire. Butt welding was done on specimens with four different heat inputs by varying the process parameters like welding current and speed. The result indicated that higher heat input levels has led to the formation of coarser grain structure, reduced mechanical properties and sensitization issues on the weldments. The formation of titanium nitrides provided resistance to fracture and increased the tensile strength of the joints at high temperatures. Further aging was done on the welded sample at a temperature of 750°C for 500 hours and the metallographic result showed formation of carbides along the grain boundaries in a chain of discrete and globular form which increased the hardness of the material. The formation of spinel NiCr 2 O 4 provided oxidation resistance to the material during elevated temperature service.

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Artificial neural network modeling studies to predict the friction welding process parameters of Incoloy 800H joints

Cited by 36 publications

References 16 publications

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

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

Investigation of Laser Welding of Ti Alloys for Cognitive Process Parameters Selection

Effects of Heat Input on the Mechanical and Metallurgical Characteristics of Tig Welded Incoloy 800Ht Joints

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