Modelling for evaluation of surface roughness in magnetic abrasive finishing of flat surfaces

Shanbhag, Vignesh. V.; Naveen, K.; Balashanmugam, N.; Vinod, Prakash

doi:10.1504/ijptech.2016.078190

Cited by 11 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Magnetic abrasive finishing was used to finish flat surface, internal and external surfaces of tubes and some of freeform surfaces. Analytical model is developed Shanbhag et al [106] and the responses were found in good agreement with the experimental results.…”

Section: Magnetic Abrasive Finishing (Maf)mentioning

confidence: 65%

Nanofinishing of freeform/sculptured surfaces: state-of-the-art

2018

View full text Add to dashboard Cite

Freeform surfaces are being used in a multiplicity of applications in different kinds of industries related to Bio-medical (Bio-implants), micro channels in micro fluidics, automotives, turbine blades, impellers of artificial heart pumps, automobiles etc. Different parts in these industries need nano-level surface finish as their functional inevitability. It is very difficult and challenging to achieve high level of surface finish, especially on the components having freeform (or sculptured) surfaces, complex shapes, and 3-D features. Surface finish is a significant factor, which affects life and functionality of a product. Many traditional and advanced finishing processes have been developed for finishing of freeform/sculptured surfaces but still it has not been possible to achieve uniform nano level surface finish specially in case of freeform surfaces. To overcome the limitations of the existing nanofinishing processes, researchers are developing new processes for uniform nanofinishing of freeform surfaces. In this article, an attempt has been made to review different nanofinishing processes employed for freeform surfaces useful in different types of applications. In addition, experimental work, theoretical analysis and existing challenges of the finishing processes have been identified to fill the research gap.

show abstract

Section: Magnetic Abrasive Finishing (Maf)mentioning

confidence: 65%

Nanofinishing of freeform/sculptured surfaces: state-of-the-art

2018

View full text Add to dashboard Cite

show abstract

“…Micro-cutting/chipping [24] and micro-ploughing [25,26] are reported to be the primary modes of material removal during the MAF. Several studies were carried out to formulate an appropriate model to study the material removal phenomenon and the effect of parametric variation in the MAF process [27][28][29][30][31][32][33]. One of the earliest works simulating surface accuracy in MAF was conducted by Kim et al [32].…”

Section: Introductionmentioning

confidence: 99%

Novel Process Modeling of Magnetic-Field Assisted Finishing (MAF) with Rheological Properties

et al. 2023

View full text Add to dashboard Cite

The performance of a magnetic-field-assisted finishing (MAF) process, an advanced surface finishing process, is severely affected by the rheological properties of an MAF brush. The yield stress and viscosity of the MAF brush, comprising iron particles and abrasives mixed in a liquid carrier medium, change depending on the brush’s constituents and the applied magnetic field, which in turn affect the material removal mechanism and the corresponding final surface roughness after the MAF. A series of experiments was conducted to delineate the effect of MAF processing conditions on the yield stress of the MAF brush. The experimental data were fitted into commonly used rheology models. The Herschel–Bulkley (HB) model was found to be the most suitable fit (lowest sum of square errors (SSE)) for the shear stress–shear rate data obtained from the rheology tests and used to calculate the yield stress of the MAF brush. Processing parameters, such as magnetic flux density, weight ratio of iron and abrasives, and abrasive (black ceramic in this study) size, with p-values of 0.031, 0.001 and 0.037, respectively, (each of them lower than the significance level of 0.05), were all found to be statistically significant parameters that affected the yield stress of the MAF brush. Yield stress increased with magnetic flux density and the weight ratio of iron to abrasives in MAF brush and decreased with abrasive size. A new process model, a rheology-integrated model (RM), was formulated using the yield stress data from HB model to determine the indentation depth of individual abrasives in the workpiece during the MAF process. The calculated indentation depth enabled us to predict the material removal rate (MRR) and the instantaneous surface roughness. The predicted MRR and surface roughness from the RM model were found to be a better fit with the experimental data than the pre-existing contact mechanics model (CMM) and wear model (WM) with a R2 of 0.91 for RM as compared to 0.76 and 0.78 for CMM and WM. Finally, the RM, under parametric variations, showed that MRR increases and roughness decreases as magnetic flux density, rotational speed, weight ratio of iron to abrasive particles in MAF brush, and initial roughness increase, and abrasive size decreases.

show abstract

“…After ANOVA analysis, they found that working gap and rotational speed are the most influencing parameters [24]. Shanbhag et al developed analytical model for examining the effect of active abrasives, circumferential speed, feed, magnetic flux density and diameter of magnetic abrasive particles on surface roughness in MAF process [25]. Gopal et al used multicriteria optimization module based on Taguchi-GRA-TOP-SIS to minimize the effect of cutting force, temperature and surface roughness in end milling of Mg hybrid MMC.…”

Section: Introductionmentioning

confidence: 99%

A novel hybridization of artificial neural network and moth-flame optimization (ANN–MFO) for multi-objective optimization in magnetic abrasive finishing of aluminium 6060

Singh

Gangwar

Singh

et al. 2019

J Braz. Soc. Mech. Sci. Eng.

View full text Add to dashboard Cite

In industries, the impact of magnetic abrasive finishing (MAF) is well recognized in achieving accurate surfaces, minimizing imperfections and providing high-quality surface finish especially in micro-and nano-range. In the same context, this paper presents a hybrid optimization method for multi-objective optimization that combines back-propagation artificial neural network (ANN) with a newly developed nature-inspired optimization algorithm, i.e. moth-flame optimization (MFO) algorithm, which is used to predict optimal process parameters of magnetic abrasive finishing process. The back-propagation ANN is gradient-based local search algorithm, while MFO is a rapid and global search algorithm for controlling the generation of candidate solutions. Initially, the MFO algorithm is integrated for training the neural network with unknown set of weights and biases and in the process overcomes the shortcomings of traditional training algorithms having slow convergence and local optima stagnation. Further, the MFO algorithm is also employed to predict the number of neurons in hidden layers. Finally, the proposed hybrid ANN-MFO methodology establishes a multi-objective optimization model in terms of important process parameters for optimizing the MAF process conditions. The optimization of different MAF process parameters is performed using MFO algorithm by incorporating the adaptive search procedure, which further enhances the exploration behaviour. Surface roughness, temperature of workpiece during the finishing operation and hardness of finished surface are investigated as the output response in the finishing of aluminium 6060. The design variables considered are working gap, abrasive weight, voltage and rotational speed. The results predicted by the proposed hybrid ANN-MFO algorithm provide effective and accurate process parameters to enhance surface finish and part quality that will be beneficial for real manufacturing environment.

show abstract

Modelling for evaluation of surface roughness in magnetic abrasive finishing of flat surfaces

Cited by 11 publications

References 0 publications

Nanofinishing of freeform/sculptured surfaces: state-of-the-art

Nanofinishing of freeform/sculptured surfaces: state-of-the-art

Novel Process Modeling of Magnetic-Field Assisted Finishing (MAF) with Rheological Properties

A novel hybridization of artificial neural network and moth-flame optimization (ANN–MFO) for multi-objective optimization in magnetic abrasive finishing of aluminium 6060

Contact Info

Product

Resources

About