Machinability Investigations of Aluminum Metal Matrix Composites (LM26 + Graphite + Flyash) by Using Wire Electrical Discharge Machining Process

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

doi:10.1007/978-981-19-0244-4_91

Cited by 6 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Surface roughness and MRR are highly influenced by pulse on time, and AA5083 has better MRR and surface roughness than AA6061. Similar machinability studies on aluminum metal matrix composites were reported and the output factors such as material removal rate, surface finish and overcut were investigated [14]. The overcut is one of the important factors for many applications, since it decides the geometrical accuracy of parts with intricate shapes and sizes.…”

Section: Introductionmentioning

confidence: 77%

Al-Mg-MoS2 Reinforced Metal Matrix Composites: Machinability Characteristics

et al. 2022

View full text Add to dashboard Cite

Several components are made from Al-Mg-based composites. MoS2 is used to increase the composite’s machinability. Different weight percent (3, 4, and 5) of MoS2 are added as reinforcement to explore the machinability properties of Al-Mg-reinforced composites. The wire cut electrical discharge machining (WEDM) process is used to study the machinability characteristics of the fabricated Al-Mg-MoS2 composite. The machined surface’s roughness and overcut under different process conditions are discussed. The evaluation-based distance from average solution (EDAS) method is used to identify the optimal setting to get the desired surface roughness and overcut. The following WEDM process parameters are taken to determine the impact of peak current, pulse on time, and gap voltage on surface roughness, and overcut. The WEDM tests were carried out on three different reinforced samples to determine the impact of reinforcement on surface roughness and overcut. The surface roughness and overcut increase as the reinforcement level increases, but the optimal parameters for all three composites are the same. According to EDAS analysis, I3, Ton2, and V1 are the best conditions. Furthermore, peak current and pulse on-time significantly influence surface roughness and overcut.

show abstract

Section: Introductionmentioning

confidence: 77%

Al-Mg-MoS2 Reinforced Metal Matrix Composites: Machinability Characteristics

et al. 2022

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract