Analyzing Process Parameters for Finishing of Small Holes Using Magnetically Assisted Abrasive Flow Machining Process

Singh, Palwinder; Singh, Lakhvir; Singh, Sehijpal

doi:10.1007/s40735-019-0315-8

Cited by 12 publications

(15 citation statements)

References 11 publications

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“…At the centre of the workpiece, it seems that abrasive medium passes through without abrasion on the internal surface. 25 The abrasive medium does not show significant change of material removal rate at high value of extrusion pressure and magnetic flux density. This resulted in a less percentage improvement in change in roughness.…”

Section: Resultsmentioning

confidence: 90%

Comparative prediction of surface roughness for MAFM finished aluminium/silicon carbide/aluminium trioxide/rare earth oxides (Al/SiC/Al₂O₃)/REOs) composites using a Levenberg–Marquardt Algorithm and a Box–Behnken Design

Sharma¹,

Janardhan

Sharma

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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The aim of the current research is to compare the surface roughness models based on the Box–Behnken Design and Levenberg–Marquardt Algorithm-based artificial neural networks. For prediction of the surface roughness of magnetic abrasive flow machining (MAFM) finished rare earth oxides (REOs) aluminium composites, Box–Behnken Design models were developed using three-level factorial design as magnetic flux density, number of cycles and extrusion pressure as process parameters. The artificial neural networks predictive models of surface roughness were developed using feed forward back propagation network procedures called the Levenberg-Marquardt Algorithm. Also, an attempt has been made to compare the Levenberg–Marquardt Algorithm-based artificial neural networks and Box–Behnken Design for the modeling of surface roughness results. The value of coefficient of determination for the Box–Behnken Design model is found to be high ( R2 = 0.9737), which is an indication of good fit for the model with high significance. The percentage error for the Box–Behnken Design model is observed to be more as compared to the Levenberg–Marquardt Algorithm-based artificial neural networks model. The comparison evidently indicates that the prediction capabilities of trained artificial neural networks models are far better than the Box–Behnken Design models. Further, specimens were examined using atomic force microscopy for three-dimensional surface profiles.

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Section: Resultsmentioning

confidence: 90%

Comparative prediction of surface roughness for MAFM finished aluminium/silicon carbide/aluminium trioxide/rare earth oxides (Al/SiC/Al₂O₃)/REOs) composites using a Levenberg–Marquardt Algorithm and a Box–Behnken Design

Sharma¹,

Janardhan

Sharma

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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“…The electromagnet was designed and fabricated. 34 "It consisted of two magnetic cores of 40 mm dia enclosed by two coils". "A comparative study of different coil design parameters was made" and "the final coil parameters were core material-low carbon mild steel, no.…”

Section: Two-way Maafm Machinementioning

confidence: 99%

“…MFD-The literature revealed that the SF starts to stabilize and the material removal goes on increasing by increasing applied MFD. 3,4,8,[34][35][36] It is anticipated that when the majority of the crests are expelled from the surface, the improved MRR because of applied MFD may not additionally enhance the SF. Media Viscosity-Media viscosity is the key parameter of the MAAFM and is based on the amount & type of gel and the polymer utilized in the preparation of the media.…”

Section: Process Parameters In Maafm Processmentioning

confidence: 99%

“…Several researchers have reported that surface finish improves during early process cycles and almost begins to stabilize after that. [34][35][36]40 It was stated that SF increased with increases no. of processing cycles.…”

Section: Process Parameters In Maafm Processmentioning

confidence: 99%

“…Extrusion pressure-The past research [34][35][36]38 indicated that material cutting is rapid at increased extrusion pressure as all other parameters remain constant. Williams et al 41 stated that MRR increases with extrusion pressure.…”

Section: Process Parameters In Maafm Processmentioning

confidence: 99%

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A review on magnetically assisted abrasive flow machining and abrasive material type

Singh

2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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The development of fine surface finish with no defects is still a significant challenge for the present industry and the finishing operation in the manufacturing of the precision components is the major problem. It is difficult rather impossible to polish/finish the difficult-to-reach surfaces, internal surfaces, and complex geometries by existing finishing methods. Abrasive flow machining (AFM) is a well-established advanced finishing process that can meet the required finishing requirements. However, the main disadvantages of this process are the surface integrity with low surface finish and high machining times. Magnetically Assisted Abrasive flow machining (MAAFM) is one of the non-conventional machining techniques that can successfully handle these problems. This process was developed in the early 2000s as a technique for deburring, polishing, and radiusing tough to reach surfaces like complicated configurations and edges/boundaries by flowing a magnetic abrasive polymeric medium across them. Magnetic flux density (MFD), extrusion pressure, flow volume, abrasive type, grit size, number of process cycles, medium, and workpiece configuration are the principal machining parameters that control the surface finish characteristics. The medium extruded forward and backward between two vertically opposed hydraulic cylinders across transit created by the workpiece and tooling. Abrasion of workpiece takes place wherever the medium proceeds across the highly confining transit. The major elements of the MAAFM process are the machine, tooling, and the magnetic abrasives containing media. MAAFM has a wide scope of uses in industries including automotive, aviation, precision dies, medicinal, electronics. This article represents a review of current advancements in the area of the MAAFM process and the type of magnetic abrasive particles (MAPs) used.

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Design of Experiments Technique for Evaluation of Material Removal and Surface Quality of Ductile Material

Singh

2022

Lecture Notes in Mechanical Engineering

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Analyzing Process Parameters for Finishing of Small Holes Using Magnetically Assisted Abrasive Flow Machining Process

Cited by 12 publications

References 11 publications

Comparative prediction of surface roughness for MAFM finished aluminium/silicon carbide/aluminium trioxide/rare earth oxides (Al/SiC/Al₂O₃)/REOs) composites using a Levenberg–Marquardt Algorithm and a Box–Behnken Design

Comparative prediction of surface roughness for MAFM finished aluminium/silicon carbide/aluminium trioxide/rare earth oxides (Al/SiC/Al₂O₃)/REOs) composites using a Levenberg–Marquardt Algorithm and a Box–Behnken Design

A review on magnetically assisted abrasive flow machining and abrasive material type

Design of Experiments Technique for Evaluation of Material Removal and Surface Quality of Ductile Material

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Analyzing Process Parameters for Finishing of Small Holes Using Magnetically Assisted Abrasive Flow Machining Process

Cited by 12 publications

References 11 publications

Comparative prediction of surface roughness for MAFM finished aluminium/silicon carbide/aluminium trioxide/rare earth oxides (Al/SiC/Al2O3)/REOs) composites using a Levenberg–Marquardt Algorithm and a Box–Behnken Design

Comparative prediction of surface roughness for MAFM finished aluminium/silicon carbide/aluminium trioxide/rare earth oxides (Al/SiC/Al2O3)/REOs) composites using a Levenberg–Marquardt Algorithm and a Box–Behnken Design

A review on magnetically assisted abrasive flow machining and abrasive material type

Design of Experiments Technique for Evaluation of Material Removal and Surface Quality of Ductile Material

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Product

Resources

About

Comparative prediction of surface roughness for MAFM finished aluminium/silicon carbide/aluminium trioxide/rare earth oxides (Al/SiC/Al₂O₃)/REOs) composites using a Levenberg–Marquardt Algorithm and a Box–Behnken Design

Comparative prediction of surface roughness for MAFM finished aluminium/silicon carbide/aluminium trioxide/rare earth oxides (Al/SiC/Al₂O₃)/REOs) composites using a Levenberg–Marquardt Algorithm and a Box–Behnken Design