Recast Layer Thickness and Residual Stress Analysis for EDD AA8011/h-BN/B4C Composites Using Cryogenically Treated SiC and CFRP Powder-Added Kerosene

Vivek, J.; Maridurai, Thirupathy; Lewise, K. Anton Savio; Pandiyarajan, R.; Chandrasekaran, K.

doi:10.1007/s13369-022-06636-5

Cited by 6 publications

(1 citation statement)

References 59 publications

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“…8,9 B 4 C's low density (2.52 g/cm 3 ), being the hardest mineral after diamond, high wear resistance and high stability against chemicals distinguish it from other ceramic particles. 10 In addition, depending on the boron carbide content, it is also preferred as neutron absorption in reactor vessels. 11 Al6061 was one of the widely used composition in the 6xxx series of aluminum family.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis on mechanical and machinability attributes of Al6061-B₄C-T6 composites processed via hot extrusion

Tinga,

Karakoç,

Yağmur

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part B:

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In this study, composite materials with Al6061 alloy matrix and B4C particles reinforcement were produced by hot extrusion process. Fabricated composites were examined for the mechanical and machinability attributes. Al6061 alloy powders (<100 µm) and B4C particles (<10 µm) were mixed with the different weight fraction of B4C (3%, 6%, and 9% by weight) of and then cold pressed under 200 MPa pressure. The cold pressed powder metal block was extruded hot after preheating at 550°C for 1 h. Further, the composites were subjected to T6 aging heat treatment after hot extrusion. Microstructure, density, hardness, tensile strength, and wear properties were examined. The effects of cutting speed and cutting force on the machinability were also investigated. In addition, surface roughness and chips formation were examined. High density (99.6%) values were achieved with the extrusion process. With increasing B4C particles ratio, the hardness and tensile values were increased which was substantiated by the fracture morphology of the tensile specimens. It has been observed that tool smearing was high in the machining of the low-reinforced (3% B4C) composite. The highest cutting force was measured as 236 N at a feed rate of 0.27 mm/rev and a cutting speed of 250 m/min. It has also been observed that the average surface roughness decreased with increasing cutting speed and it increased with increasing feed rate.

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Section: Introductionmentioning

confidence: 99%

Performance analysis on mechanical and machinability attributes of Al6061-B₄C-T6 composites processed via hot extrusion

Tinga,

Karakoç,

Yağmur

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part B:

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Surface Integrity Characteristics and Multi-response Optimization in Wire-EDM of Al–Al3Fe Composites

Anand,

Sardar,

Guha

et al. 2024

Arab J Sci Eng

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Artificial neural networks-based modelling of effects of cryogenic electrode treatment, nano-powder, and surfactant-mixed dielectrics on wear performance and dimensional errors on superalloy machining

Sana,

Khan,

Farooq

et al. 2024

J Braz. Soc. Mech. Sci. Eng.

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In the present era dominated by Industry 4.0, the digital transformation and intelligent management of industrial systems is significantly important to enhance efficiency, quality, and the effective use of resources. This underscores the need for a framework that goes beyond merely boosting productivity and work quality, aiming for a net-zero impact from industrial activities. This research introduces a comprehensive and adaptable analytical framework intended to bridge existing gaps in research and technology within the manufacturing sector. It encompasses the essential stages of using artificial intelligence (AI) for modelling and optimizing manufacturing systems. The effectiveness of the proposed AI framework is evaluated through a case study on electric discharge machining (EDM), concentrating on optimizing the electrode wear rate (EWR) and overcut (OC) for aerospace alloy Inconel 617. Utilizing a comprehensive design of experiments, the process modelling through an artificial neural network (ANN) is carried out, accompanied by careful fine-tuning of hyperparameters throughout the training process. The trained models are further assessed using an external validation (Valext) dataset. The results of the sensitivity analysis indicated that the surfactant concentration (Sc) has the highest level of influence, accounting for 52.41% of the observed influence on the EWR, followed by the powder concentration (Cp) with a contribution of 33.14%, and the treatment variable with a contribution of 14.43%. Regarding OC, Sc holds the highest percentage significance at 72.67%, followed by Cp at 21.25%, and treatment at 6.06%. Additionally, parametric optimization (PO) shows that EWR and OC overcome experimental data by 47.05% and 85.00%, respectively, showcasing successful performance optimization with potential applications across diverse manufacturing systems.

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Recast Layer Thickness and Residual Stress Analysis for EDD AA8011/h-BN/B4C Composites Using Cryogenically Treated SiC and CFRP Powder-Added Kerosene

Cited by 6 publications

References 59 publications

Performance analysis on mechanical and machinability attributes of Al6061-B₄C-T6 composites processed via hot extrusion

Performance analysis on mechanical and machinability attributes of Al6061-B₄C-T6 composites processed via hot extrusion

Surface Integrity Characteristics and Multi-response Optimization in Wire-EDM of Al–Al3Fe Composites

Artificial neural networks-based modelling of effects of cryogenic electrode treatment, nano-powder, and surfactant-mixed dielectrics on wear performance and dimensional errors on superalloy machining

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Recast Layer Thickness and Residual Stress Analysis for EDD AA8011/h-BN/B4C Composites Using Cryogenically Treated SiC and CFRP Powder-Added Kerosene

Cited by 6 publications

References 59 publications

Performance analysis on mechanical and machinability attributes of Al6061-B4C-T6 composites processed via hot extrusion

Performance analysis on mechanical and machinability attributes of Al6061-B4C-T6 composites processed via hot extrusion

Surface Integrity Characteristics and Multi-response Optimization in Wire-EDM of Al–Al3Fe Composites

Artificial neural networks-based modelling of effects of cryogenic electrode treatment, nano-powder, and surfactant-mixed dielectrics on wear performance and dimensional errors on superalloy machining

Contact Info

Product

Resources

About

Performance analysis on mechanical and machinability attributes of Al6061-B₄C-T6 composites processed via hot extrusion

Performance analysis on mechanical and machinability attributes of Al6061-B₄C-T6 composites processed via hot extrusion