Magnetic Abrasive Finishing Process

Singh, Sachin; Gupta, Vishal; Sankar, Mamilla Ravi

doi:10.1007/978-3-030-43312-3_8

Cited by 4 publications

(6 citation statements)

References 60 publications

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“…Firstly, the effect of five parameters (current, machining gap, speed, abrasives concentration and time) on microhardness in MAF process was studied by Ahmad et al [6]. Singh et al [35] improved the microhardness surfaces of specimens using four input parameters (mesh size, speed, time and abrasive weight). Microhardness was studied by Ahmed et al [36], MAF process was utilized after Single Point Incremental Forming (SPIF) as a finishing process with four input parameters (speed, feed rate, gap and current).…”

Section: Introductionmentioning

confidence: 99%

Optimizing the five magnetic abrasive finishing factors on surface quality using Taguchi-based grey relational analysis

Mohammed S Ahmed,

Shather

2024

Eng. Res. Express

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The effectiveness of the magnetic abrasive finishing (MAF) process relies on several factors, including the brush's flexibility that varies across tools. This study aimed to optimize the results of five key parameters (voltage, finishing time, gap distance, rotating speed, and particle size) on surface roughness (SR) and microhardness (HV) using the grey relational analysis (GRA) method. Experimental work employed the Taguchi design with L27 trials in Minitab 17, involving five variables with three levels for each. The impact of these parameters on microhardness and surface roughness for stainless steel SUS420 bubble cups was assessed using Taguchi and regression analyses. The best roughness improvement and the most substantial enhancement in microhardness were individually obtained with the GRA method. This method assigned the best results for both surface roughness and microhardness. According to Taguchi analysis, the voltage parameter has the main or maximum parameter effect on grade, followed by gap distance, time, spindle speed, and particle size. It was found that the optimal parameters were the same as the input parameters.

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Section: Introductionmentioning

confidence: 99%

Optimizing the five magnetic abrasive finishing factors on surface quality using Taguchi-based grey relational analysis

Mohammed S Ahmed,

Shather

2024

Eng. Res. Express

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“…Bounded, semi-bounded, and unbounded MAP models exist. Finishing with magnetic abrasives may be broken down into cylindrical (interior and exterior) and flat [11,12].As shown in Figure 1 Several previous studies machined stainless steel metals of different classifications with various numbers of input parameters. Firstly, the effect of five parameters (current, machining gap, speed, abrasives concentration, and time) on microhardness in the MAF process was studied by Ahmad et al [6].…”

Section: Introductionmentioning

confidence: 99%

“…Firstly, the effect of five parameters (current, machining gap, speed, abrasives concentration, and time) on microhardness in the MAF process was studied by Ahmad et al [6]. Singh et al [12]improved the microhardness surfaces of specimens by four input parameters (mesh size, speed, time, and abrasive weight). Microhardness was studied by Ahmed et al [13].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Magnetic Abrasive Finishing (MAF) Process Parameters on the Microhardness of Stainless Steel SUS420 Bubble Cups

Ahmed,

Shather

2024

ETJ

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 Magnetic abrasive finishing enhanced SUS420 stainless steel bubble cup microhardness  A 21.20% microhardness increase occurred with the smallest particle size, high voltage, moderate gap and lower rotation speed  Optimizing parameters enabled substantial microhardness improvement through magnetic abrasive finishingThe optimal performance of bubble cups relies on achieving the appropriate surface quality, a common requirement in various industrial applications. The effectiveness of the Magnetic Abrasive Finishing (MAF) process depends on several factors, including the brush's flexibility, that vary across tools. This study investigates the influence of five key parameters, voltage, finishing time, gap distance, rotating speed, and particle size, on microhardness (MH). Experimental work was based on Taguchi design with L27 trials in Minitab 17, involving five variables with three levels for each. The impact of these parameters on microhardness for stainless steel SUS420 bubble cups is assessed using Taguchi and ANOVA analyses. According to the Taguchi analysis, the main parameters that improve microhardness (MH) most are, in order, gap distance, voltage, time, particle size, and spindle speed. The percentage change in microhardness (%∆MH) increases with higher voltage and time values and decreases with higher particle size and spindle speed values. This study observes an exception to this trend for the gap distance value of 1.2 mm. The use of smaller particle sizes in the range of (20-63) µm showed the most significant enhancement in microhardness (MH) at 21.20%, whereas larger particle sizes (125-250 µm) exhibited lower enhancement in microhardness (MH) at 4.12%.

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“…It should be noted that AFM is found under a variety of process-assisted alternatives such as ultrasonic-assisted AFM [5], magnetic abrasivesassisted AFM [6], and electrochemical-aided abrasive flow finishing (ECA2FM) [7] to name a few. Other important advances in abrasive machining techniques forming a research perspective are presented in references [8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Abrasive Flow Nano-Finishing Processes by Adopting Artificial Viral Intelligence

Fountas

Vaxevanidis

2021

JMMP

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This work deals with the optimization of crucial process parameters related to the abrasive flow machining applications at micro/nano-levels. The optimal combination of abrasive flow machining parameters for nano-finishing has been determined by applying a modified virus-evolutionary genetic algorithm. This algorithm implements two populations: One comprising the hosts and one comprising the viruses. Viruses act as information carriers and thus they contribute to the algorithm by boosting efficient schemata in binary coding to facilitate both the arrival at global optimal solutions and rapid convergence speed. Three cases related to abrasive flow machining have been selected from the literature to implement the algorithm, and the results corresponding to them have been compared to those available by the selected contributions. It has been verified that the results obtained by the virus-evolutionary genetic algorithm are not only practically viable, but far more promising compared to others as well. The three cases selected are the traditional “abrasive flow finishing,” the “rotating workpiece” abrasive flow finishing, and the “rotational-magnetorheological” abrasive flow finishing.

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Magnetic Abrasive Finishing Process

Cited by 4 publications

References 60 publications

Optimizing the five magnetic abrasive finishing factors on surface quality using Taguchi-based grey relational analysis

Optimizing the five magnetic abrasive finishing factors on surface quality using Taguchi-based grey relational analysis

The Impact of Magnetic Abrasive Finishing (MAF) Process Parameters on the Microhardness of Stainless Steel SUS420 Bubble Cups

Optimization of Abrasive Flow Nano-Finishing Processes by Adopting Artificial Viral Intelligence

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