Investigations and regression modeling on mechanical characterization of cast aluminum alloy based (LM 26 + graphite + fly ash) hybrid metal matrix composites

Prakash, Chander; Senthil, P.; Manikandan, N.; Palanisamy, D.

doi:10.1007/s12008-022-00881-6

Cited by 7 publications

(1 citation statement)

References 31 publications

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“…This allows researchers to achieve the desired performance in various engineering applications. [33][34][35][36][37][38][39] When it comes to the manufacturing of a component, the orientation of the components and the form tolerances are two of the most imperative aspects that need to be taken into attention. This is because both of these aspects have a direct influence on the quality and the mechanical characteristics of a surface.…”

Section: Introductionmentioning

confidence: 99%

Optimization of wire spark erosion machining of Grade 9 titanium alloy (Grade 9) using a hybrid learning algorithm

Natarajan,

Pasupuleti,

Giri

et al. 2024

AIP Advances

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Manufacturing has grown challenging because of the increased usage of harder materials, such as titanium alloys, in many industries, such as aerospace, automobiles, and marine. Conventional material removal procedures are not suitable for these tough materials due to their increased hardness and slow machinability. Wire Electrical discharge machining (WEDM) is a modern approach for material removal, particularly for harder materials, such as titanium alloys, nickel alloys, hard particle reinforced metal matrix composites, etc. The research design was performed by deeming the independent factors, such as duration of pulse and applied current. The removal rate of material, surface roughness of the machined region, dimensional deviation, and tolerance errors in form/orientation are considered performance metrics. Taguchi’s approach was engaged to assess the process variables, and the importance of the process factors was established using analysis of variance approach. The purpose of this research is to create an AI based decision making tool, which can be utilized to anticipate the various parameters that impact the WEDM material removal process. The discoveries of the present exploration allowing the manufacturers to make better-informed decisions with a developed model’s capability by demonstrating that the model’s predicted values were in close confirmation to the actual values.

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