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European Journal of Applied Sciences – Vol. 12, No. 6

Publication Date: December 25, 2024

DOI:10.14738/aivp.126.17885.

Hung, T. Q., Tam, D. T., & Tung, L. A. (2024). Determining Best Dressing Factors in Surface Grinding Hardox 500 Using MABAC

Technique. European Journal of Applied Sciences, Vol - 12(6). 742-749.

Services for Science and Education – United Kingdom

Determining Best Dressing Factors in Surface Grinding Hardox

500 Using MABAC Technique

Tran Quoc Hung

Ha Noi University of Industry, Hanoi, Vietnam

Do Thi Tam

Thai Nguyen University of Technology, Thai Nguyen, Vietnam

Luu Anh Tung

Thai Nguyen University of Technology, Thai Nguyen, Vietnam

ABSTRACT

This paper presents the results on the application of the multi-criteria decision- making (MCDM) method for selecting the optimal dressing mode for surface

grinding of Hardox 500. The study addressed the MCDM problem through the

application of the Multi-Attributive Border Approximation Area Comparison

(MABAC) method, with criterion weights established via Method Based on the

Removal Effects of Criteria (MEREC) method. Additionally, surface roughness (RS)

and material removal rate (MRR) were selected as the two criteria for the current

study. Also, five dressing variables were analyzed: non-feeding dressing nn, fine

dressing depth df, fine dressing times nf, rough dressing depth dr, and rough

dressing times nr. Moreover, 16 experimental runs (design’s type L16 (44x21)) were

designed and executed. The problem concerning MCDM has been addressed. The

investigation's findings suggest that option No. 5, characterized by the input

parameters dr = 0.02 mm, nr = 1, nf = 1, df = 0.01 mm, and nn = 2, represents the

optimal dressing mode.

Keywords: Surface grinding, Hardox 500, MABAC method, MEREC method, Surface

Roughness, Material removal rate.

INTRODUCTION

Until now, several studies have been undertaken to optimize the grinding process. M. S.

Rodrigues et al. [1] conducted a study to improve alternative ways by examining the grinding

of hardened AISI 4340 steel with four lubricooling fluids: Base fluid, Volatile Corrosion Inhibitor

(VCI), VCI Low Cost (VCI LC), and VCI Extreme Pressure (VCI EP). The parameters obtained in

the study comprise surface roughness, roundness error, wheel wear, grinding power, pollution,

and cost. Corrosion inhibitors can enhance workpiece quality but yield varying results across

different parameters. Hoang X.T. et al. [2] presented a study on the computation of the optimal

exchanged grinding wheel diameter in the external grinding of 9CrSi tool steel. This study

examined the impact of grinding process factors, including starting grinding wheel diameter,

total dressing depth, radial grinding wheel wear per dress, and wheel life on the exchanged

grinding wheel diameter. Additionally, the influence of cost components such as the machine

tool hourly rate and the grinding wheel expense was examined. A model was presented to

determine the optimal swapped grinding wheel diameter based on the data. Luu A. T. et al. [3]

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743

Hung, T. Q., Tam, D. T., & Tung, L. A. (2024). Determining Best Dressing Factors in Surface Grinding Hardox 500 Using MABAC Technique. European

Journal of Applied Sciences, Vol - 12(6). 742-749.

URL: http://dx.doi.org/10.14738/aivp.126.17885

performed a study to improve the dressing parameters of grinding wheels for 9CrSi tool steel,

aiming to decrease average roughness and flatness tolerance by the Taguchi technique and Grey

Relational Analysis (GRA). The results indicate that the optimal dressing parameters for

achieving minimal average roughness and flatness tolerance are a coarse dressing depth of

0.025 mm, three coarse dressing cycles, a fine dressing depth of 0.005 mm, two fine dressing

cycles, three non-feeding dressings, and a dressing feed rate of 1.6 m/min. Experiments with

the optimized dressing parameters have been conducted to validate the predictive model. Tran

T.H. et al. [4] performed a study to determine the ideal dressing conditions for grinding SKD11

tool steel utilizing a HaiDuong grinding wheel. This study examined the impacts of six input

parameters: feed rate, depth of rough dressing cut, rough dressing duration, depth of finish

dressing cut, finish dressing duration, and non-feeding dressing.

H. Liu et al. [5] investigated the impact of speed on the creation of machined surfaces during

ultrahigh-speed grinding of IN718 superalloy at velocities reaching 240 m/s. This study

meticulously analyzes the grinding forces and surface integrity across several speed ranges.

Various methodologies are utilized to characterize and investigate the subsurface

microstructure. The findings indicate that brittle-mode removal of IN718 superalloy transpires

at a grinding speed surpassing 190 m/s, significantly reducing work hardening and heat

production caused by increased plastic deformation. Moreover, the machining speed affects the

formation mechanism of the recrystallization layer, progressively shifting from dominance of

discontinuous dynamic recrystallization (dDRX) to that of continuous dynamic recrystallization

(cDRX) as grinding speed increases. Le X.H. et al. [6] conducted a study to analyze the impact of

coolant parameters on surface roughness during the internal cylindrical grinding of annealed

9CrSi steel. Thirteen tests utilizing central composite design and response surface methodology

examine the concentration and flow rate of the coolant. The impact of each parameter and their

interaction on surface roughness is examined by their regression model. Optimal parameters

are derived from that model to achieve the least surface roughness. Tran T.H. et al. [7]

performed a study to identify the optimal dressing parameters for achieving the minimal

flatness tolerance in the grinding of SKD11 steel with a HaiDuong grinding wheel. This research

examines the impact of six input parameters—feed rate (S), depth of rough dressing cut (ar),

rough dressing times (nr), depth of finish dressing cut (af), finish dressing times (nf), and non- feeding dressing (nnon) — on flatness tolerance. Tran T.H. et al. [8] conducted a study to examine

the influence of process factors on the surface roughness in surface grinding of 90CrSi tool steel.

This study considered process characteristics such as coolant concentration, coolant flow, cross

feed, table speed, and depth of cut. The impact of the process parameters on surface roughness

was assessed. A predictive methodology for calculating surface roughness was proposed.

Le X.H. et al. [9] performed a study on the adjustment of dressing parameters in internal

cylindrical grinding to achieve maximum material removal rate. This study examined the effects

of dressing parameters, including dressing feed rate, coarse dressing depth, coarse dressing

frequency, fine dressing depth, fine dressing frequency, and dressing count without depth of

cut, on the material removal rate. Yueming Liu et al. [10] utilized kinematic simulations to

predict the extent of surface roughness resulting from grinding. The research investigated three

unique configurations of abrasive grains (sphere, truncated cone, and cone) with a model of

single-point diamond dressing. The proposed surface roughness model was empirically

validated, revealing a deviation of 7-11 percent. Tran T.H. et al. [11] conducted a study to

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European Journal of Applied Sciences (EJAS) Vol. 12, Issue 6, December-2024

determine the optimal exchanged grinding wheel diameter to minimize grinding costs in the

surface grinding process for stainless steel. The relationship between grinding costs and the

optimal swapped grinding wheel diameter has been investigated and expressed in

mathematical formulas. The optimal exchanged grinding wheel diameter, which minimizes

grinding costs, has been established as a function of various parameters, including initial

grinding wheel diameter, total dressing depth, radial grinding wheel wear per dress, wheel life,

machine tool hours, and grinding wheel cost. Le X.H. et al. [12] performed a study on the optimal

computation of the exchanged diameter of grinding wheels in the internal grinding of stainless

steel. This study examined the impact of grinding process parameters, including initial

diameter, total depth of dressing cut, wheel life, radial grinding wheel wear per dress, and the

ratio of length to diameter of workpieces on the exchanged grinding wheel diameter. The effects

of cost parameters, including the machine tool hourly rate and the grinding wheel expense,

were examined. A proposed model for determining the optimal swapped grinding wheel

diameter was presented based on the study's findings.

L.M. Kozuro et al. [13] proposed a dressing strategy for external grinding able to attaining a

surface roughness of Ra = 0.32-1.25 (μm). This technique involves a longitudinal feed rate of

0.4 m/min, four dressing passes with a dressing depth of 0.03 mm, and four non-feeding

dressing operations. Tran T.H. et al. [14] performed an optimization analysis to ascertain the

ideal swapped grinding wheel diameter for external grinding. This study explored seven input

grinding parameters: initially grinding wheel diameter, grinding wheel width, wheel life, radial

grinding wheel wear per dressing, total depth of dressing cut, machine tool hourly rate, and

grinding wheel cost. The impact of grinding parameters on the optimal exchanged grinding

wheel diameter for the external cylindrical grinding process was analyzed in connection with

the screening trials. The impact of the interactions among the input grinding parameters was

also assessed. The regression equation for calculating the optimal exchanged grinding wheel

diameter was provided. Tran T.H. et al. [15] performed a study on the multi-criteria

optimization of dressing parameters in surface grinding of 90CrSi tool steel. The aim of the

study is to reduce surface roughness and normal shear force while enhancing the useful lifetime

of the grinding wheel through the application of the Taguchi method and Grey Relational

Analysis (GRA). The study's results enabled the suggestion of the ideal dressing parameters.

The predicted model's validity has been corroborated by experimentation.

This paper provides the outcomes of an MCDM assessment aimed at selecting the best dressing

mode for surface grinding of Hardox 500. The study employed the MCDM approach utilizing the

MABAC method, with the weights of the criteria established through the MEREC method. After

addressing the MCDM problem with two criteria (SR and MRR), the optimal dressing factors

have been proposed.

METHODOLOGY

Method to Solve MCDM

This study addressed the MCDM problem via the MABAC methodology. The subsequent actions

must be adhered to in order to utilize this strategy [16]:

Step 1: Create initial decision-making matrix: