# Determining the Best Dressing Parameters for External Cylindrical Grinding Using MABAC Method
## 1 Introduction
The effective use of the environment-friendly unconventional fluids when machining Ti-6 Al-4V has also been reported when using an alumina wheel [19; 20] and a SiC wheel [21]. [PERSON] et al. [22] investigated the wear-adaptive optimization of in-process conditioning factors during the face plunge grinding of PcBN. It was noted that the optimal input factors allow the process of in-process conditioning to dramatically reduce grinding tool wear without increasing the process time or non-productive time. [PERSON] et al. [23] presented the results of a multi-target optimization study to find the optimum input factors for getting the minimum surface roughness and maximum material removal rate when external grinding SCM440 steel. When grinding GH4169 alloy with a ceramic bonded CBN grinding wheel, a multi-information fusion recognition model and experimental study of grinding wheel wear status were reported in [24]. [PERSON] and [PERSON] [25] conducted online-optimizing, the internal grinding process, and the dressing parameters to reduce the production time while maintaining part quality requirements. The optimum process parameters when grinding Ti-6 Al-4 V using a CBN grinding wheel was introduced in [26]. The Taguchi method and gray relational analysis (GRA) were applied to find the optimum dressing parameters when processing the SKD11 tool steel in surface grinding to increase the material removal rate and to reduce the roundness tolerance of the ground parts [11]. Table 1 presents a brief overview of some previous studies on the dressing process of different types of grinding. In this table, the specifications of the grinding type, wheels, dresser, workpiece material, the objective of study, and the output optimum factors presented in these studies are indicated. This table helps to determine the ultimate optimal parametric combination for all methods considered. From the above analyses and Table 2, it can be noted that so far, there have been quite a few studies on the dressing mode and on the determination of the optimum or reasonable wheel dressing mode. Besides, different dressing modes such as coarse dressing, fine dressing, and non-feeding dressing influence the efficiency of the dressing process. However, to date, there has been no research on determining the best dressing mode among many different dressing setups for external grinding SKD11 tool steel using an MCDM method. Therefore, finding the best dressing mode in external cylindrical grinding SKD11 tool steel using an MCDM method has been taken into considered. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Ginding Type** & **Wheel Type** & **Dressing Tool Material** & **Method** & **Objective** & **Optimum Parameters** & **Reference** \\ \hline Surface grinding & Alumina & Single-point diamond dresser & Ti-6 Al-4V & - Experimental & - Grinding ratio & - Dressing infeed & [27; 28; 29] \\ \hline Surface grinding & Silicon carbide & Multi-point diamond dresser & 90 CrSi & - Experimental & - Targetuchi and GRA & Surface roughness & - Coarse dressing times & - Coarse dressing times & [30] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Several previous research results on optimization of dressing process. Multi-criteria decision making (MCDM) is a common problem when it is necessary to analyze multiple options to choose the best one. Numerous studies on MCDM have been conducted for various mechanical machining processes such as milling, turning, grinding, electrical discharge machining (EDM), etc. [33]. This is because the machining process is frequently required to meet many criteria, such as the maximal material removal rate (MMR), the minimal surface roughness (SR), or the minimal tool wear rate. So far, several studies have been conducted to determine the best dressing mode using MCDM methods. [PERSON] and [PERSON] [34] presented a scoping review on the applications of MCDM methods for the parametric optimization of machining processes.