Magnetic Abrasive Machining Research Articles

Eccentric magnetic abrasive finishing of 316 steel

This paper presents a new method of eccentric magnetic-abrasive finishing as applied to 316 stainless steel. The proposed solution is based on solid ferromagnetic abrasive grains (Fe-Si) held by an external magnetic field. A workpiece fixed in a holder with a certain eccentricity is inserted between the grains and the holder is rotated. The use of an additional eccentricity has a significant impact on the nature of the process, in particular, it increases the speed of the sample relative to the abrasive grains, makes it possible to use more abrasive grains, induces mixing abrasive grains, which can prolong their active life time. In this study, a three-factor, five-level design of experiment was conducted. The variable parameters were: machining time, eccentric value, and rotational speed. Samples in the form of rollers with a diameter of 3 mm were mounted in a special holder that allowed to obtain the assumed eccentricity. Based on the results obtained, regression equations were developed for selected roughness parameters Ra, Rz, Rq, Rt. The value of Ra roughness decreased from the initial value of 0.575 μm to a value below 0.1 μm (an 80% decrease) in 9.1 min. The results obtained provided the basis for further research in conducting eccentricity in magnetic-abrasive machining as an intensifying element of the process.

Box-Behnken Design for MAFM Precision Surface Finishing of Al-6063/SiC/B<sub>4</sub>C Composites: A Comparative Study with Nature-Inspired Algorithms

Precision polishing is difficult for advanced materials like silicon carbide and boron carbide. Magnetic abrasive flow machining (MAFM) has become an effective method for cleaning, deburring, and polishing metal and high-tech engineering parts. By finishing hybrid Al/SiC/B4C-metal matrix composites (MMCs), this research uses MAFM for experimental readings. The present work is innovative due to the aluminum workpiece fixture, hybrid composites, and response surface methodology (RSM) modeling. The neural simulation of the MAFM process and nature-inspired error reduction make it unique. Using six input and two output parameters, a generic framework is created. Box-Behnken design (BBD) of response surface methodology plans and executes 54 runs of experimentation. The hybrid artificial neural network (ANN) technique is used to compare the MAFM process systematically. ANN is used to model parameter input-output relations. To anticipate the created surface accurately, regression models must be precise. These hybrid particle swarm optimization (PSO)-genetic algorithm (GA)-simulated annealing (SA) algorithms optimize the MAFM process. Additionally, trained ANN models outperform the BBD model in prediction. For optimal error reduction, the neural network uses Bayesian regularization with 112 iterations. The ANN model regression graph shows a correlation between inputs and outputs. A scanning electron microscope (SEM) with 300-magnification examines the workpiece surface. According to SEM, MAFM provides fine surface textures, thus reducing abnormalities.

Multi-response optimization of magnetic abrasive finishing process parameters on AISI H13 hot die steel using grey relational analysis

This paper presents a new approach for the optimization of machining parameters on magnetic abrasive finishing (MAF) of AISI H13 hot die steel with multiple responses based on the orthogonal array with grey relational analysis. The process has been explored widely for the finishing of plane surfaces on pillar drilling machine. Taguchi orthogonal arrays L27 Designs of experiment method was used for planning of experiments. Total of 27 Experiments were conducted on AISI H13 hot die steel to check the best factor level combination. Five process parameters such as abrasive size, cutting speed, finishing time, machining gap, and magnetic flux density are optimized using multiple responses such as surface roughness (Ra) and metal removal rate (MRR). The Grey Relational Analysis (GRA) was utilized to analyze the results to find the optimum condition and identify the significant parameters affecting the surface roughness and the metal removal rate on H13 hot die steel. Magnetic abrasive finishing machining parameters combination of experiment no. 14 has the highest grey relation grade (GRG) of 0.922. The ideal parameter combination to achieve optimum output are cutting speed of 118.24 m/min, abrasive mesh size of 600, finishing time of 10 min, Machining gap of 1 mm, and Magnetic flux density of 485 gauss. The surface roughness (Ra) value was improved from 0.753 µm to 0.452 µm.

Magnetic Abrasive Machining of Hard Workpieces by New Diffusion-Alloyed Materials

Magnetic Abrasive Machining of Hard Workpieces by New Diffusion-Alloyed Materials

Influence of process parameters on magnetic abrasive machining efficiency

The development of modern mechanical engineering forces us to constantly improve the technology of processing materials and look for ways not only to improve the quality of products but also to increase the efficiency of the processing process itself. This article discusses an alternative method to finish the product, which can replace the traditional method of grinding, magnetic abrasive treatment. This is a modern solution that allows you to obtain high surface quality of the processed product with minimal defects in their surface layer, and reduce the number of technological operations, which has a beneficial effect on the efficiency of the processing process. The efficiency of the magnetic abrasive machining process, which was determined by the amount of specific removal of the material, is influenced by many parameters, some of which were studied in this article: electromagnetic induction and processing time. The success of the application of magnetic abrasive treatment can also be judged by the topography of the surface of the product made using an electron microscope. The data obtained in the course of the study demonstrated the importance of individual input parameters of the magnetic abrasive treatment process in the formation of the surface layer of the product. According to the results of experimental studies, the dependences of the varied parameters were determined and recommendations were formed to improve the efficiency of the use of the magnetic abrasive process.

Features of mechanical processing of aluminum alloys

The issue of ensuring the technological quality of aluminum alloy products is considered. The problems that arise during the processing of aluminum alloys are described in detail. The results of various studies aimed at solving the described problem are presented. The author proposes to consider the method of magnetic abrasive machining as one of the promising methods for processing complex products.

Experimental investigations into ultrasonic assisted magnetic abrasive flow machining process

ABSTRACT In the present study, an advanced variant of abrasive flow machining (AFM) is developed. Ultrasonic assistance and magnetic field assistance with rotating effect have been designed, fabricated, and simultaneously used to develop ultrasonic-assisted magnetic abrasive flow machining (UAMAFM) process. The ultrasonic horn and electromagnets were designed based on the simu modified by adding iron particles to make abrasive media respond effectively under the applied magnetic field. The effects of the external assistances, machine, and media-based parameters were explored on the amount of material removed (MR) and percentage improvement in the surface roughness (% ΔRa) for pure titanium (Grade II). The influence of input parameters was investigated using analysis of variance (ANOVA) by analyzing the process responses. Quadratic regression models showing the effect of input parameters and their interaction were developed for each response. The influence of individual parameters on process outcomes and response surface plots for significant interactions have also been discussed. The maximum MR of 26.62 mg and ΔRa of 54.42% were achieved at the optimized setting of process parameters. The predicted values of MR and % ΔRa from developed statistical models agreed with the experimental results.

Experimental Examination on Finishing Characteristics of Aluminum Pipes in Magnetic Abrasive Machining Using SiC Contained Glued Magnetic Abrasives

With the rapid development in the industry, applications of finished parts are increasing day by day. However, the surface finish of the parts fabricated by conventional processes could not readily meet the requirements of various applications. Therefore, post-processing is needed to further improve the surface quality. Magnetic abrasive machining uses a flexible magnetic abrasive brush to remove material from the workpiece surface at a controllable rate. This cutting tool sticks to the workpiece during finishing operation and exerts a small force on the surface. In magnetic abrasive machining, the cutting tool neither requires compensation nor dressing. In this paper, the internal finishing of aluminum pipes has been investigated in magnetic abrasive machining tests using silicon carbide-based glued magnetic abrasives. For evaluating the performance of these magnetic abrasives, experimental work according to the central composite design technique was carried out to finish the aluminum pipes. The results so obtained were analyzed to study the influence of process parameters like magnetic field strength, speed of workpiece, abrasive mesh size and quantity of magnetic abrasives on percentage improvement in surface finish and material removal rate. The analysis showed that magnetic field strength was the most effective parameter while finishing the aluminum pipe followed by the quantity of magnetic abrasives. The finishing at optimal condition resulted in a surface finish of 0.07 μm. Further, scanning electron microscopy of the surface before and after magnetic abrasive machining was taken to study the improvement in surface finish.
 HIGHLIGHTS
 
 Magnetic abrasive machining (MAM) of aluminum work specimens have been performed by SiC-based magnetic abrasives
 The central composite design has been used for planning and execution of experiments
 The surface finish and material removal rate of the machined work specimens have been analysed as a performance measure of MAM process
 The high value of improvement in surface finish and material removal rate at optimum machining conditions have been observed
 Scanning electron microscopy (SEM) has been employed to study the surface topography of machined surfaces
 
 GRAPHICAL ABSTRACT

Investigation of a novel ultrasonically aided electrochemical magnetic abrasive machining process for SS 316L

Many present day applications require machining and finishing of complex-shaped and tough to machine materials with precision and accuracy by conventional or non-conventional machining and polishing processes. Ultrasonically aided electrochemical magnetic abrasive machining (UAEMAM) process is a novel non-conventional machining technique which can machine and finish the product for better surface quality. In present research work, the development and performance evaluation of UAEMAM process for machining of SS 316L is reported. The UAEMAM process is optimised using response surface methodology (RSM) based on central composite design (CCD) and grey relational analysis. The main machining variables of UAEMAM such as work piece rotational speed, working gap, concentration of NaNO3, percentage of abrasives by weight and pulse on time were considered to determine percentage improvement in surface finish (PISF) and material removal rate (MRR). The significant machining factors and the best combination levels of machining parameters associated with PISF and MRR were determined. The present work shows complete elimination of oxide layer from work piece surface, subsequently increasing the PISF value to 84.73 which is 2.73% higher than value achieved by previous studies employed. The benefits of ultrasonic aided electrochemical magnetic abrasive machining were also confirmed from the micrograph observations of the surface. Experimental results show that UAEMAM process is effective for machining and polishing components for better surface finish.

Mathematical Modeling of Material Removal and Surface Roughness in Ultrasonic-Assisted Magnetic Abrasive Flow Machining Process

Abstract Ultrasonic-assisted magnetic abrasive flow machining (UAMAFM) process shows enhanced finishing performance compared to conventional abrasive flow machining (AFM). In this present research paper, mathematical models for M˙R and Ra have been developed for the UAMAFM process by considering both steady-state and transient phenomena. The external ultrasonic and magnetic field assistance enhanced the velocity and length of contact of active abrasives, calculated from the kinematic analysis. The resultant finishing forces have also been evaluated by considering these external aids. The steady-state material removal per finishing cycle remains constant and depends on the velocity of motion, length of contact, resulting forces, number of active abrasives, and work material hardness. The transient material removal per finishing cycle was calculated in terms of the volume of irregularities present over the work surface, i.e., initial surface roughness. The mathematical model for surface roughness was developed in terms amount of material removed (MR), and initial (Ra0) and critical surface roughness (Racr). The predicted values of material removed and surface roughness from developed mathematical models agreed with experimental results with a deviation of 7.80% and 2.44%, respectively.

Influence of properties of the working technological environment on the surface roughness and productivity during magnetic abrasive machining

The results of experimental studies of the surface roughness parameters of ball bearing treadmills made of steel SHX-15 and the performance of the magnetic abrasive treatment process depending on the properties of the components of the working process medium are presented. The research used methods of mathematical modeling of the technological process of magnetic abrasive processing, subsequent analysis of the obtained multivariate regression equations to identify the most significant technological factors according to the criteria of their interaction and relative influence on surface roughness and processing performance. The relative total contribution to the change in the roughness of the treated surface (Ra, microns) and processing performance (ΔG, mg/min) was established: single control technological factors affect 29,1 % and 48,2 %, respectively; interacting control technological factors 46.8 % and 45.9 %, respectively. The controlling technological factors in descending order of the degree of influence by generalized significance are arranged in the sequence: hydrogen pH, gradient of magnetic induction B (T/mm), microhardness of abrasive HV (GPa), coolant viscosity γ (cSt), processing time t (s) and magnetic permeability µ (mH/m). The interpretation of the physical mechanisms of interaction of controlling technological factors is given. The obtained results of a quantitative assessment of the relative total contribution of single control technological factors can be used in assigning modes of magnetic abrasive treatment of bearing rings, and their interaction – in studies of the synergism of the parameters of the working technological environment, which allows obtaining a much greater effect than using each parameter separately.

Magnetic-abrasive machining in manufacturing of medical implants

Despite the rapid development of new biocompatible medical materials, the question of an optimal mechanical processing of implants surfaces remains a major challenge. Surface integrity of sliding pair surfaces in implant joints is the key to prostheses long-term usage. In this article, a free form finishing process – magnetic-abrasive machining (MAM) – is presented as an alternative for machining implant joints. MAM shows a positive impact on surface integrity of a spherical workpiece made of cobalt-chromium alloy. Comparing to the initial turned surface, the roughness is reduced by more than 50% and compressive residual stresses are increased by more than 10%.

Modelling Of Wind Turbine Magnet for Magnetic Abrasive Finishing and Magnetic Field Assisted Abrasive Flow Machining Process

Magnetic Abrasive Finishing is a significant process for finishing up to the micro-level. However, with the advancement of technology and hybrids like Viscoelastic magnetic abrasive Finishing and Magnetic abrasive Flow machining, it has become a nano finishing process. To improve the finishing process, the researchers have made a Model and tested the feasibility of the wind turbine magnet in Finishing. The Maxwell simulations were done for the cylindrical Specimen of Brass, Steel Aluminum. The simulations results were in accordance with the fact that the proposed wind turbine magnet may be used for the simulations.

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

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.

Neural Simulation of Surface Generated During Magnetic Abrasive Flow Machining of Hybrid Al/SiC/B4C-MMCs

Neural Simulation of Surface Generated During Magnetic Abrasive Flow Machining of Hybrid Al/SiC/B4C-MMCs

A box behnken design approach for parametric optimization in processing of aluminum 6061 tubes

ABSTRACT In this paper, bonded magnetic abrasives-based media were applied in magnetic field-assisted abrasive flow machining (MFAAFM) for the processing of aluminum 6061 material tubes to improve the surface quality. The effects of different parameters including magnetic field strength (300, 450, 600 mT), number of working cycles (15, 20, 25), and hydraulic pressure (30, 45, 60 bars) on surface roughness improvement rate (SRIR) were studied, and optimization was done utilizing the response surface methodology technique. A Box–Behnken design (BBD) was employed to maximize the SRIR, which was taken as a response variable. A maximum SRIR (72.2%) was attained when the aluminum 6061 tubes was processed at 450 mT of MFS, 25 working cycles, and 60 bars of hydraulic pressure. The model is well fitted and represented utilizing 2nd order polynomial equations. Furthermore, the model validation revealed a 99.84% fit among the actual and experimental values. The surface roughness (Ra) = 0.20 μm of the aluminum 6061 tube samples can be obtained, and the surface morphology after finishing has been greatly improved as revealed by SEM micrographs and Ra peak-valley diagram.

Experimental Investigation of Process Parameters of Al-SiC-B4C MMCs Finished by a Novel Magnetic Abrasive Flow Machining Setup

Abrasive flow machining (AFM) is one of the non-conventional finishing processes used to attain good surface quality and high material removal. However, limited attempts have been made to improve the performance of these processes. This paper presents a novel magnetic abrasive flow machining (MAFM) setup fabricated by adding a magnetization effect in which a nylon fixture and permanent magnets are replaced by a newly fabricated aluminium fixture and coil-type magnets, respectively. Inner cylindrical surfaces of hybrid Al/SiC/B4C metal matrix composites (MMCs) are finished by the MAFM process. One variable at a time (OVAT) approach is used for studying the effect of 6 input parameters, extrusion pressure (Ep), the number of cycles (N), abrasives concentration (C), workpiece material (Wp), abrasive mesh size (M), and magnetic flux density (Mf) upon response parameters, material removal rate (MRR) and change in surface roughness (ΔRa). The experimental results obtained for MRR and ΔRa show a significant improvement from 3.92 to 7.68 μg/s and 0.49 to 0.74 μm, respectively due to the increase of the extrusion pressure from 1 to 9 Mpa. The MRR and ΔRa was reduced from 6.89 to 6.78 μg/s and 0.46 to 0.22 μm, respectively with an increase in mesh number of abrasives from 80 to 400. The variation in concentration of abrasives from 40 to 60 % shows an improvement in MRR from 4.51 to 6.42 μg/s; whereas, there is a negligible effect on ΔRa which comes out from 3.82 to 3.86 μm. The MMCs, which are used for the experimentation shows a decline in MRR and ΔRa from 5.12 to 3.85 μg/s and 0.77 to 0.42 μm, respectively. This happened because there was a percentage change of reinforcement of SiC from 9 to 7 % and B4C from 1 to 3 % in Al-6063. An increase in the number of cycles from 50 to 250 shows a significant improvement in both MRR and ΔRa from 1.79 to 3.75 μg/s and 0.97 to 1.86 μm, respectively. Variation in magnetic effect also significantly improves MRR and ΔRa from 1.35 to 3.17 μg/s and 0.38 to 1.06 μm, respectively, when it is varied from 0.15 - 0.45 Tesla. The work carried out shows an overall significant improvement in MRR and ΔRa by using the MAFM process. The MAFM process finds a wide range of applications in finishing like surgical instruments, mechanical components, aerospace industry, electronics industry, etc. HIGHLIGHTS The hybrid MMCs (Al/SiC/B4C) are finished by novel MAFM setup An aluminium fixture and coil-type magnets play a significant role for finishing the workpiece surfaces An abrasive laden media acts as a cutting tool in the finishing process The OVAT approach is used for investigating the parametric effect The extrusion pressure, number of cycles and magnetic flux density are the significant parameters affecting the MRR and ΔRa GRAPHICAL ABSTRACT

Effect of process parameters on material removal rate in magnetic abrasive flow machining of Al/SiC/B4C metal matrix composites

In the recent times, the necessity for magnetic abrasive flow machining (MAFM) process in the industry arises as it is superior to conventional machining processes. MAFM is used to obtain high surface finishing and materials removal rate for the cylindrical, complex and intricate profiles. In this paper, the effect of process parameters has been studied on the material removal rate in MAFM process of metal matrix composites of Al/SiC/B4C prepared by stir casting process. The process parameters are magnetic field, extrusion pressure, no. of cycles, mesh size of abrasives, workpiece material and concentration of abrasives.Taguchi design of experiments has been used for design of experiments and the analysis of variance technique has been used to check the significance level of each input parameter. The results show that the intensity of magnetic field has a dominant effect on the material removal rate.

Theoretical-Probability Model for Calculating Roofness in Magnetic Abrasive Machining

The work is devoted to the problem of calculating the surface roughness during magnetic abrasive processing. Cutting grains have random dimensional characteristics, are randomly located on the surface of the tool, the workpiece has an irregular profile. The cutting parts of the grains partially remove the chips and partially elastoplastically deform the metal. Some of the vertices fall into the marks on the surface of the workpiece formed by the previous processing, and some - on the marks from the passage of the previous vertices. This process is determined by the probability of contact of the top of the grain with the metal. The developed probabilistic-theoretical model makes it possible to predict the removal of metal from the treated surface depending on the time and parameters of the operation, which creates the prerequisites for their use in the design of polishing operations.

Mathematical modeling and process parameters optimization of ultrasonic assisted electrochemical magnetic abrasive machining

A new machining technique called ultrasonic assisted electrochemical magnetic abrasive machining integrates ultrasonic vibrations, electrochemical machining and magnetic abrasive finishing is used to machine and finish biomaterials more efficiently as compared to single process. This investigation highlights a novel technique for machining of hard materials in order to improve machining efficiency with hybrid process. Experimental design of Taguchi L27 and TOPSIS methods are implemented to find the optimum parameters of process. The objective is to maximize MRR and PISF and minimize PCMH simultaneously. This research investigates the interaction between input parameters (rotational speed of work piece, machining time, working gap, ultrasonic frequency time and voltage) and their effect on output characteristics of process like MRR, PCMH and PISF. The recommended settings of parameters for UEMAM process is found to be rotational speed = 600 rpm, machining time = 5 minutes, working gap = 3 mm, frequency time = 4 sec and voltage = 3 V from TOPSIS and rotational speed = 600 rpm, machining time = 15 minutes, working gap = 3 mm, frequency time = 6 sec and voltage = 4.5 V from Taguchi optimization plot. A comparison of experimental results and theoretical results have been done to validate the proposed mathematical model. The calculation results of the model showed that the maximum value of error is not more than 7.351 while comparing with experimental results, which suggests that the mathematical model is reasonable.