OBRABOTKAMETALLOV Vol. 26 No. 4 2024 TECHNOLOGY Ta b l e 1 Values of parameters selected for the experiment Parameters Minimum value Maximum value Normal load (N) 400 800 Speed (rpm) 450 750 Sliding distance 4 km of 90°, and a layer thickness of 0.2 mm. According to the literature, these parameters are optimal. The test specimens were cylindrical PLA pins with a diameter of 8 mm and a length of 40 mm. These pins were printed at the printing orientation of 0°, 45° and 90°. PLA material is one of the popular fi lament materials used in FDM printing. PLA is easy to print and the printer can be easily adjusted to it. The experiments were methodically designed to study the infl uence of input parameters on specifi c wear rate. Sliding velocities were obtained by selecting the track diameter on the disk and the corresponding rotation speed of the disk. SS 316 stainless steel was chosen as the material for the disk.About 13 experiments were carried out for each printing orientation with a friction path of 4 kilometers. These were prepared based on the central compositional design (CCD) which is the eff ective design for experiments (DOE) for the RSM method. Table 1 shows the parameter values selected for the experiment. In this study, the grey relational analysis was used to optimize the parameters that ensure minimal sliding wear. The grey system theory presents the degree of grey correlation to describe the degree of correlation in the developing trends of diff erent things or diff erent factors. The greater the degree of grey correlation, the more similar the things are, and vice versa. This theory transforms a multiple response optimization problem into a single response optimization situation with the objective function of overall grey relational grade [27]. Methodology of grey analysis The procedure for obtaining the solution of GRA optimization is given as follows: Step 1. To identify input parameters that infl uence the multiple output variables. Step 2. To select of Taguchi design matrix and conduct the experiments. Step 3. To select quality characteristics for each output variable. Step 4. To normalize all response variables (grey relational generation): the smaller-the-better normalization formula was used to transfer the original sequence to a comparable sequence and is given below. (o) (o) * (o) (o) ( ) ( ) ( ) ( ) ( max . max i ) m n i i i i i x k x k x k x k x k − = − Step 5. To determine the deviation Sequences, Δ0i(k) The deviation sequence, Δ0i(k) is the absolute diff erence between the reference sequence x0∙(k) and the comparability sequence xi∙(k) after normalization. The value of x0∙(k) was considered equal to 1. 0 ( ) Þ 0 ( ) Þ ( ) . i k x k xi k Δ = ⋅ − ⋅ Step 6. To calculate the grey relational coeffi cient (GRC) for each output: grey relational coeffi cient. γ(x_0 (k),x_i (k)) ( ) max max 0 0 m ( ) ( ) ( ) in , . i i x k x k k Δ +ζΔ γ = Δ +ζΔ
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