Material Removal Rate Research Articles

Pulsed Unipolar-Polarisation Plasma Electrolytic Polishing of Ni-Based Superalloys: A Proof of Conception

The enhanced performance of aerospace equipment drives parts development towards integration, complexity, and structural optimization. This advancement promotes metal near-net fabrication technologies like wire electrical discharge machining (WEDM) and 3D printing. However, the high initial surface roughness from WEDM or 3D printing poses significant challenges for the high-performance surface finishing required. To effectively reduce the surface roughness of the workpieces with high initial surface roughness, this paper proposes pulsed unipolar-polarisation plasma electrolytic polishing (PUP-PEP). The study examined the material removal mechanisms and surface polishing quality of PUP-PEP. This technique combines the high current density and material removal rate of the electrolytic polishing mode with the superior surface polishing quality of PEP through voltage waveform modulation. For an Inconel-718 superalloy part fabricated by WEDM, PUP-PEP reduced surface roughness from Ra 7.39 μm to Ra 0.27 μm in 6 min under optimal conditions. The roughness decreased from Ra 7.39 μm to Ra 0.78 μm in the first 3 min under pulsed unipolar-polarisation voltage, resulting in a remarkable 233% increase in efficiency compared to that with conventional PEP. Subsequently, the voltage output voltage is transformed into a constant voltage mode, and PEP is continued based on PUP-PEP to finally reduce the workpiece surface roughness value to Ra 0.27 μm. The proposed PUP-PEP technology marks the implementation of ‘polishing’ instead of conventional rough-finish machining processes, presenting a new approach to the surface post-processing of metal near-net fabrication technologies.

Analysis, modelling, and optimization of force in ultra-precision hard turning of cold work hardened steel using the CBN tool

The machinability of high-performance materials such as superalloys, composites, and hardened steel has been a big challenge due to their mechanical, physical, and chemical properties, which give them inherent complex machining characteristics. Additionally, majority of machinability tests conducted on these materials have been carried out on conventional and less precise lathes based on Taguchi, composite, and other designs of experiments that do not exploit all the possible combinations of cutting parameters. This work reports an investigation on ultra-precision hard turning (UHT) of cold work hardened AISI D2 steel of HRC 62, based on the full factorial design of experiment, carried out on an ultra-precision lathe. A theoretical analysis of the force components generated is reported. Modelling of the process, based on the resultant force, is also reported through a machine learning model. The model was developed from the experimental data and statistically evaluated with validation data. Its average MAPE values of 1.47%, 4.81%, and 10.66% for training, testing, and validation, respectively, attest to its robustness. The excellent coefficient of determination values, R2, also justify the model’s robustness. Multi-objective optimization was also conducted to optimize material removal rate (MRR), resultant force, and vibration simultaneously. For sustainable and efficient UHT, optimal cutting velocity (158.8 m/min), feed (0.125 mm/rev), and depth of cut (0.074 mm) were proposed to generate optimal resultant force (224.8 N), MRR (2603.6 mm3/min), and vibration (0.03 m/s^2) simultaneously. These results can be beneficial in planning UHT processes for high-performance materials.

Traditional and Integrated MCDM Approaches for Assessment and Ranking of Laser Cutting Conditions

Multiple benefits and advantages that offer laser cutting technology are difficult to achieve if the laser cutting parameter conditions are not adequately set. Determining laser cutting conditions is not trivial task considering large number of controllable inputs, existence of multiple process performances which are often mutually opposed as well as effects of noise factors. Different scientific methods and engineering approaches represent more sophisticated approaches which aid assessment and determination of the most favourable (optimized) cutting conditions. This study is focused assessment and ranking of laser cutting conditions in CO2 laser cutting of AISI 316L stainless steel. Based on kerf width and surface roughness experimental data and estimation of material removal rate (MRR) and total variable costs, four criteria in the multi-criteria decision making (MCDM) model were considered. For solving the developed model, six well-known MCDM methods were applied, and in addition, in order to overcome ranking inconsistency, robust decision-making rule was developed upon which final ranking was obtained.

Optimization and characterization of boric acid powder mixed electrical discharge machining of Ti-6Al-4V ELI

The powder mixed electrical discharge machining (PMEDM) process is used for enhancing machining efficiency and improving the surface characteristics of the workpiece compared to normal EDM. The present study evaluates the effect of boric acid powder mixed in deionized water in PMEDM of Ti-6Al-4V ELI (extra low interstitial). The PMEDM process is assessed by evaluating material removal rate (MRR) and surface roughness (SR) at varying powder concentration (PC), current and pulse duration ( Ton). Boric acid PMEDM exhibits advantageous for both MRR and SR, where MRR increases and SR decreases with an increase in PC. To simultaneously achieve higher MRR and lower SR, the grey-fuzzy logic method is employed. Initially, the raw input data is normalized into grey numbers and a single index is obtained through grey relational analysis. Subsequently, triangular membership functions are assigned to the inputs and output. Fuzzy logic rules are then formulated, and grey fuzzy relational grade (GFRG) is calculated for each condition. The optimum condition (GFRG = 0.787) is attained at 15 g/l PC, 9 A current and 106 µs Ton, resulting in a 63% improvement compared to the initial condition. At the optimum condition, MRR increased by 145% and SR decreased by 29% compared to the initial condition. The effect of boric acid in PMEDM can be visualized through the formation of smooth surfaces, exhibiting reduced SR, smaller and shallower craters, and fewer debris and globules. Additionally, the boric acid PMEDM experiments resulted in the formation of a hard TiB phase on Ti-6Al-4V, thus improving its wear resistance.

Rapid manufacturing of copper-graphene composite EDM electrode for improved machining performance

ABSTRACT Recently, graphene has emerged as reinforcing material for copper-based electric discharge machine (EDM) tools because of its superior electrical, thermal and high melting properties. 3D printing methods to fabricate electrodes are convenient and faster compared to conventional methods. Therefore, this study investigated the fabrication of copper-graphene composite electrodes for EDM using rapid tooling and pressureless microwave sintering. The influence of graphene content on the machining performance was examined, revealing enhanced electrical conductivity and machining efficiency. Electrical conductivity of the composite electrodes having 0.25 and 0.5 wt% of graphene was higher than pure copper. Additionally, upto 89% improvement in material removal rate and upto 14% reduction in electrode wear rate was obtained. Optical study showed an improvement of upto 28% in surface finish of the workpiece machined using composite electrode having 0.25 wt% graphene. A complex shaped composite tool was successfully fabricated and machined cavity was obtained.

Effects of organic compounds rich in phosphate functional groups as multifunctional inhibitors on copper film chemical-mechanical polishing properties: Combined experiment and theoretical calculation

For copper (Cu) interconnection chemical mechanical polishing (CMP) process, the Cu film slurry follows the principles of high efficiency, environmentally friendly and low cost. In this study, hydroxyethylene diphosphonic acid (HEDP), amino-trimethylene phosphonic acid (ATMP) and ethylenediamine etramethylenephosphonic acid (EDTMP) were used as green inhibitors to study the effects of organic compounds rich in two, three and four phosphate functional groups on the corrosion inhibition properties of Cu films. The results indicated that the Cu film material removal rate (MRR) of HEDP reaches 5879 A/min and the surface roughness (Sq) is 0.85 nm, superior to the other two inhibitors. Through mechanical action analysis, electrochemical experiment, UV-Visible, X-ray photoelectron spectroscopy (XPS) test analysis and theoretical calculation, it was observed that three inhibitors could not only form stable complexes with Cu, which have the effect of corrosion inhibition, but also play a lubricating role of surfactant, and simplify the composition of slurry, and the inhibition efficiency order of them is HEDP > ATMP > EDTMP.

Optimizing High-Performance Predictive Modeling of the Medium-Speed WEDM Processing of Inconel 718

The purpose of this research was to create a predictive model for a medium-speed wire electrical discharge machine (WEDM) utilizing an artificial neural network (ANN). Medium-speed WEDM experiments were developed based on the I-optimal mixture design for machining, the Inconel 718 superalloy. During the experiment, the input parameters were the spark ontime, spark offtime, wire feed, and current, with the material removal rate (MRR) and surface roughness (Ra) selected as performance indicators. The ANN model was trained on experimental data and built using a feed-forward backpropagation neural network with a (4-8-2) structure and the Bayesian regularization (BR) learning approach. The model correctly predicted the relationship between the medium-speed WEDM’s primary process parameters and machining performance. An integrated ANN model and the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) were used to determine the ideal parameters for the MRR and Ra, resulting in a set of Pareto-optimal solutions. The confirmation experiment revealed that the mean prediction error between the experimental and ideal solutions had a maximum error percentage of 1% for the MRR and 2% for the Ra, which are within acceptable ranges. This showed that the best process–parameter combinations were better for the MRR and Ra.

Evaluation of entropy-coupled multi-criteria decision-making methods for enhancing machinability

Optimizing machining parameters is crucial, enhancing machinability while maintaining high product quality standards. This study bridges a critical research gap by evaluating and comparing five Taguchi-based Multi-Criteria Decision Making (MCDM) techniques—Combined Compromised Solution (CoCoSo), Grey Relational Analysis (GRA), Multi-Objective Optimization Ratio Analysis (MOORA), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Complex Proportional Assessment (COPRAS)—coupled with the Entropy method to optimize machining parameters for enhancing machinability in turning medium carbon steel. The focus is on feed rate and cutting speed under dry and Minimum Quantity Lubrication (MQL) environments considering six critical machining responses: material removal rate, surface roughness, main cutting force, cutting temperature, cutting ratio, and tool life.The results reveal that MQL consistently improves machining performance, with COPRAS, TOPSIS, MOORA, and GRA converging on an optimal setting favoring an MQL environment, a 0.14 mm/rev feed rate, and a cutting speed of 137 m/min, whereas CoCoSo suggests a different optimal parameter setting. CoCoSo and GRA demonstrate the highest reliability, evidenced by minimal discrepancies between predicted and experimental results, with absolute percentage errors of 0.647 % and 0.659 %, respectively. The COPRAS method also shows strong predictive accuracy with a 5.573 % error, outperforming MOORA and TOPSIS. Spearman's rank correlation analysis reveals a high agreement between COPRAS, MOORA, and TOPSIS, with COPRAS emerging as a potential replacement for the latter in similar decision-making scenarios. SEM and EDX analyses confirm that MQL conditions reduce tool wear, enhance surface quality, and extend tool life compared to dry machining. This research provides insights into effective parameter optimization strategies for improving machinability and underscores the benefits of adopting MQL for sustainable manufacturing processes.

Experimental Study on Dry Milling of Stir-Casted and Heat-Treated Mg-Gd-Y-Er Alloy Using TOPSIS

This study examines the dry milling process of a rare-earth-based magnesium alloy, emphasizing the optimization of the milling parameters and their impact on the surface quality, cutting forces, and the rate of material removal. The objective is to improve our comprehension of the milling behavior of the Mg-Gd-Y-Er alloy. The Taguchi technique is adopted to formulate the experimental design. This study methodically investigates the influence of heat treatment (T4 and T6) on milling performance, and the effects of speed, feed rate, and depth of cut. The output variables considered for this investigation are the surface roughness (Ra, Rz, Sa, and Sz), material removal rate (MRR), and cutting force. To optimize the milling parameters and achieve superior outcomes, the multi-objective optimization technique TOPSIS is used. At a feed rate of 150 mm/min, a spindle speed of 1500 rpm, and a depth of cut of 1 mm, the T4-treated sample exhibits a minimum surface roughness value of 0.0305 µm. The highest resultant force values of 96.4416 N and 176.1070 N for 200 °C and 225 °C T6-treated alloys are obtained by combining process parameters such as a spindle speed of 1500 rpm, a feed rate of 50 mm/min, and a depth of cut of 1.5 mm. Furthermore, the maximum closeness coefficient value is achieved by combining a spindle speed of 1000 to 1500 rpm, a feed rate of 150 mm/min, and a depth of cut of 0.5 mm to 1 mm. The closeness coefficient value is significantly influenced by the most significant process parameters, as indicated by the ANOVA results.

An experimental investigation into the potential of employing mixed eco-friendly abrasives during AWJ milling of nickel-based superalloy

Non-conventional machining processes are capable of achieving higher performance compared to conventional ones due to their inherent characteristics and higher amount of parameters which can be favorably regulated. Although the correlation between the most important process parameters and process outcome has been already established for a wide range of conditions and workpiece materials, the introduction of new considerations related to the three pillars of sustainability require further investigation on new means for the enhancement of AWJ milling process. As one of the most important parameters in AWJ milling is the abrasive material, the introduction of new materials may offer considerable advantages from different perspectives. Thus, in the present work, a comprehensive investigation on the efficiency of using eco-friendly, mixed abrasives is carried out under various conditions such as different traverse feed rate, abrasive mass flow rate, water jet pressure, jet impingement angle, and mixing ratio. The feasibility of using mixed abrasives is evaluated in terms of achievable depth of penetration, kerf width, kerf taper angle as well as material removal rate (MRR), and cutting efficiency. The findings indicate that among other factors, the mixing ratio plays a noticeable role especially regarding MRR and cutting efficiency and can offer an additional effective means to achieve the desired kerf characteristics in conjunction with other significant parameters such as water jet pressure.

In-situ investigation into the atomic-scale removal behavior of gallium nitride

The process of elimination at the atomic level stands as the paramount step in crafting ultra-smooth gallium nitride (GaN) substrates. However, this delicate removal mechanism presents a notable challenge. In this study, we employed a scraping technique utilizing atomic force microscope (AFM) probe manipulation to conduct an in-situ examination of atomic-scale removal behaviors. Through in-situ regular imaging, we uncovered the principles underlying atomic-scale removal. We systematically explored how scraping and imaging techniques impacted surface topography and the behavior of atomic step-terraces removal. Our findings indicated that abrasives knocking facilitates the emergence of atomic step-terraces, while sliding removal techniques hinder the structure. During the scraping process, both the upper and lower sections of the atomic step-terraces were simultaneously eliminated, with material removal rate (MRR) being slower at the bottom. To validate our in-situ observations, we conducted polishing experiments. Furthermore, the coefficient of friction (COF) recorded during chemical mechanical polishing offered insights into the frictional disparities of abrasives on the GaN surface, thereby shedding light on the atomic-scale removal mechanism.

Experimental investigation of edge preparation for cemented carbide profile cutting tools using flexible abrasive jet polishing

This paper investigates the impact of a novel Flexible Abrasive Jet Polishing (FAJP) method on the edge preparation of profile cutting tools based on Hertz contact theory. FAJP employs flexible rubber particles encapsulating diamond micro-powders as abrasives for air jet polishing. The results indicate a significant improvement in material removal rate with FAJP compared to traditional drag finishing methods, along with superior surface quality near the tool edge. The duration of abrasive usage has the greatest impact on FAJP, depending on the specific requirements of the tool edge, with options ranging from 400 to 200 h. There exists a correlation between jet pressure and jet time, with a recommended jet pressure of 225 kPa, and different coating effects achievable by adjusting the jet time. A tool speed of 280 rpm is recommended.

Optimizing the Die-Sink EDM Machinability of AISI 316L Using Ti-6Al-4V-SiCp Electrodes: A Computational Approach

Die-sink electric discharge machining (EDM) is essential for shaping complex geometries in hard-to-machine materials. This study aimed to optimize key input parameters, such as the discharge current, gap voltage, pulse-on time, and pulse-off time, to enhance the EDM performance by maximizing the material removal rate while minimizing the surface roughness, residual stress, microhardness, and recast layer thickness. AISI 316L stainless steel was chosen due to its industrial relevance and machining challenges, while a Ti-6Al-4V-SiCp composite electrode was selected for its thermal resistance and low wear. Using Taguchi’s L27 orthogonal array, this study minimized the trial numbers, with analysis of the variance-quantifying parameter contributions. The results showed a maximum material removal rate of 0.405 g/min and minimal values for the surface roughness (1.95 µm), residual stress (1063.74 MPa), microhardness (244.8 Hv), and recast layer thickness (0.47 µm). A second-order model, developed through a response surface methodology, and a feed-forward artificial neural network enhanced the prediction accuracy. Multi-response optimization using desirability function analysis yielded an optimal set of conditions: discharge current of 5.78 amperes, gap voltage of 90 volts, pulse-on time of 100 microseconds, and pulse-off time of 15 microseconds. This setup achieved a material removal rate of 0.13 g/min, with reduced surface roughness (2.46 µm), residual stress (1518.46 MPa), microhardness (259.01 Hv), and recast layer thickness (0.87 µm). Scanning electron microscopy further analyzed the surface morphology and recast layer characteristics, providing insights into the material behavior under EDM. These findings enhance the understanding and optimization of the EDM processes for challenging materials, offering valuable guidance for future research and industrial use.

Effect of synergistic CeO2/MoS2 abrasives on surface roughness and material removal rate of quartz glass

Cerium oxide (CeO2) is a primary abrasive frequently used in quartz glass polishing slurry for facilitating glass surface planarisation. However, due to its exorbitant synthesis costs and extremely corrosive as well as toxic reagents in the system, the chemical modification of CeO2 abrasives have limited practical applications. In this work, a novel chemical mechanical polishing slurry for quartz glass incorporating potassium oleate (KOL), deionised water (DIW), cerium dioxide and molybdenum disulphide (MoS2) was developed in this paper to enhance the chemical mechanical polishing (CMP) performance of CeO2-based polishing slurry. KOL creates an alkaline environment for the system, further develops silicate insoluble substances on the quartz glass surface and boosts the material removal rate (MRR) in CMP. MoS2 exhibits a two-dimensional layered nanosheet structure between the abrasive and quartz glass, and then acts as a solid lubricant to prevent excessive mechanical damage, thus increasing the abrasive's wettability. This characteristic helps avoid scratches and other defects on the quartz glass surface. Notably, when the content of KOL and MoS2 in the system is 0.2 wt% and 0.3 wt%, respectively, the surface roughness of the quartz glass surface is 0.48 nm under the scanning area of 15 × 15 μm2 and the MRR is 24.03 μm/h following CMP. The synergistic interaction between CeO2 and MoS2 significantly enhances the abrasive performance on the glass substrate, offering a novel approach for developing polishing fluids that leverage the collaborative effects between abrasives and two-dimensional materials.

INVESTIGATIONS ON THE MACHINABILITY CHARACTERISTICS OF Al–Zn(5.4)–Mg(2.4)–Cu(1.4) ALLOY COMPOSITE USING TOPSIS APPROACH

In this research, stir casting was used to create the aluminium alloy (AA7075) composite filled with 10[Formula: see text]wt.% zirconia (ZrO2) particles as reinforcement. Die sinking electric discharge machining (EDM) was used to examine the machining characteristics of the proposed composite. The machining experiments were executed in accordance with the [Formula: see text] orthogonal layout. Here, the material removal rate (MRR) and surface roughness (SR) were taken into account as the output responses, and the discharge current ([Formula: see text]), pulse on-time ([Formula: see text]), and pulse off-time ([Formula: see text]) were taken as the machining parameters. To determine the optimal parameter conditions for the output responses, the technique for order preference by similarity to ideal solution (TOPSIS) approach was applied. According to the experimental findings, the optimum settings of the parameters are determined at 15[Formula: see text]amps of ‘[Formula: see text]’, 750[Formula: see text][Formula: see text]s of ‘[Formula: see text]’ and 50[Formula: see text][Formula: see text]s of ‘[Formula: see text]’. ANOVA results stated that ‘[Formula: see text]’ has the most notable factor with a contribution of 90.85%. The interaction effect of parameters on the responses was shown by the contour plots. The confirmatory experiment was finally conducted at the optimum machining parameter settings, and it was found that the error only occurred in 8.4%.

Influences of ultrasonic vibration directions, amplitudes, and frequencies on sapphire polishing studied by molecular dynamics

The sapphire surface morphology, atom removal rate, temperature, polishing force, subsurface damage, dislocation, and stress were explored under different ultrasonic directions, frequencies and amplitudes through molecular dynamics (MD). For both vertical and horizontal vibration, the rising ultrasonic frequency and amplitude will reduce the tangential and normal force, and increase the subsurface temperature and the material removal rate (MRR). Higher frequencies promote the basal dislocation, thus reducing the subsurface damage. Higher amplitudes cause thinner subsurface damage layer under horizontal vibration. However, it is opposite at vertical vibration. The horizontal vibration can obtain a flatter polished surface and a thinner subsurface damage layer due to the longer trajectory and less impact on sapphire surface. This study can provide reference for sapphire high-quality polishing.

Magnetic field assisted blasting erosion arc machining (M-BEAM): A novel efficient and quality improved machining method for Inconel 718

Electrical Arc Machining (EAM) presents an efficient technique for processing difficult-to-cut materials such as Inconel 718. However, despite its extremely high machining efficiency, the high energy density in machining makes it difficult to achieve a desirable surface quality that can be compatible with the subsequent cutting process. To solve this issue, this study explores the application of a magnetic field to enhance the performance of Blasting Erosion Arc Machining (BEAM), namely Magnetic Field assisted BEAM (M-BEAM). It leads to an increased Material Removal Rate (MRR) and reductions in both the Tool Wear Ratio (TWR) and the Surface Roughness (represented by Sa) with the increasing magnetic field strength, attributed to the improvements of the arc deflection and molten metal expelling. Moreover, the electromagnetic force is positively correlated with the discharge energy, which can further improve the machining performance of BEAM, especially when the hydrodynamic force is hard to act at large discharge energy. The MRR and Sa are enhanced and reduced by 14.52 % and 41.86 % respectively due to the stronger control effect at large energy. After multi-objective optimization, the optimized MRR in M-BEAM achieves a 9.22 % enhancement and Sa also decreases by 25.96 % and 60.61 % in the roughing and finishing process. This advancement also elevates machining precision as well as mitigates defects such as debris adhesion, overburning, and cracks. Moreover, the recast layer is significantly reduced by 88.59 % and 89.96 %. Consequently, M-BEAM promises extensive applications in aerospace industries for machining Inconel 718 and even for other difficult-to-cut materials with high efficiency and quality.

Use of RSM desirability approach to optimize WEDM of mild steel

Wire Electrical Discharge Machining (WEDM) is a non-traditional material removal process commonly used for precision machining of hard materials such as super alloys, ceramics, carbide, and composite materials. Optimization of process parameters is critical for improving machining efficiency and achieving the desired surface quality. Response Surface Methodology (RSM) systematically optimizes process parameters and investigates their impact on machining performance. WEDM control parameters such as pulse ON Time (TON) (50–60 μs), pulse OFF Time (TOFF) (25–34 μs), gap voltage (VG) (25–250 V), peak current (IP) (1–6 A), and dielectric flow rate (Df) (1–3 LPM) are optimized to reduce surface roughness (SR) and taper angle (TA) while increasing material removal rate (MRR) during the machining of Mild Steel. The optimal parameters are TON as 53 μs, TOFF as 28 μs, IP as 2.65 A, VG as 185 V, and Df as 1.5 LPM. The experimental findings are presented to demonstrate the usefulness of the proposed strategy in optimizing WEDM control parameters. The validation test was conducted under optimal conditions and the results were reported. The manufacturing industries can use RSM optimization in the manufacturing domain.

Novel statistical investigation on performance measures of WEDM: Optimization, microstructure and mechanical properties

A comparative performance analysis has been investigated on wire electrical discharge machining (WEDM) responses while machining a hybrid titanium matrix composite (TMC) varying the key input parameters like power (P), peak current (IP) and time-off (Toff). Two novel multi-objective optimization algorithms are developed namely desirable multi-objective genetic algorithm (DMOGA) and desirable multi-objective giant pacific octopus optimizer (DMOGPOO) for tackling various issues in many industries like automobile valve pins in crank and cam shafts, aerospace propeller and biomedical implants. The principal advantage of DMOGA to other algorithm is accuracy and robustness. The novelty fits in the iterative progression of growth of efficient grandee set, uttered as population congregating to a fitness function. Many techniques frequently encounter substandard solutions when evaluating MOO problems, as opposed to solving properly approximated functions of Pareto optimal solutions in targets. DMOGPOO is an enthralling statistical method which mimics the octopus’s predatory behavior, performs better than other multi-objective optimization (MOO) algorithms, where the desirable objective functions is fetched in python using MOGPOO. In a multi-objective foraging environment, the archive was utilized to imitate octopus predatory behavior and establish social hierarchies. DMOGPOO approach is designed with multi-objective formulations to preserve and guarantee enhanced coverage of optimum solutions across all goals. Experimental investigation is accepted on material removal rate (MRR), surface roughness (SR), kerf width (KW) and over cut (OC). Combined desirability in case of DMOGA is 0.716 which improved to 0.813 when DMOGPOO is proposed. MOO is improved with DMOGPOO of 13.547% when contrasted with DMOGA, with MRR of 3.81 mm3/min, SR of 0.79 µm, KW of 0.349 mm, OC of 0.099 mm, and combined desirability of 0.813. Improved optimality set is obtained when DMOGPOO is used. %improvement of MRR is 5.54%, SR is 75.95%, KW is 0.29%, and OC is 4.21%.

Theoretical and Experimental Investigation on a Novel Cavitation-Assisted Abrasive Flow Polishing Method.

A novel polishing method is proposed to increase material removal rates through the acceleration of abrasive movements using micro-jets formed by spontaneous collapses of bubbles due to the cavitation in a special-shaped Venturi tube. The Venturi structure is optimized by numerical simulations. Process-related parameters for the optimal cavitation ratio are investigated for achieving maximum adaptation to polishing flat workpieces. Furthermore, this novel approach enhances processing efficiency by approximately 60% compared to traditional abrasive flow polishing. The processing method that employs cavitation bubbles within a special-shaped Venturi tube to augment the flow of abrasive particles holds significant potential for material polishing applications.