Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 4031917, 9 pages
https://doi.org/10.1155/2017/4031917

Research Article

FAHP and TOPSIS Prediction of Diamond Segments Wear When Using Frame Saw to Cut Granites

Qin Sun,1,2,3,4 Jinsheng Zhang,1,2,3 and Jibo Hu1,2,3

¹School of Mechanical Engineering, Shandong University, Jinan 250061, China
²Research Centre for Stone Engineering, Shandong Province, China
³Key Laboratory of High-Efficiency and Clean Mechanical Manufacture (Shandong University), Ministry of Education, Jinan, China
⁴School of Mechanical Manufacturing and Automation Engineering, Shandong Yingcai University, Jinan 250104, China

Correspondence should be addressed to Qin Sun; sunhaiqin1998@163.com and Jinsheng Zhang; zhangjs@sdu.edu.cn

Received 17 August 2017; Accepted 19 October 2017; Published 12 November 2017

Academic Editor: Nazrul Islam

Copyright © 2017 Qin Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Fuzzy Analytic Hierarchy Process (FAHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approaches were employed to predict the sawability of a diamond frame saw to cut granites. FAHP is used to determine the weights of the criteria of decision-makers and TOPSIS is used to rank sawability. The sawability was evaluated by diamond segment wear. The prediction of segment wear is important to determine the segments service life and sawing cost and may determine cutting parameter selection for a given stone. Sawing experiments were conducted to verify the analysis result of the applied method in this study. The experimental results are in good agreement with the theoretical analysis. The ranking method can be used to evaluate segment wear. Stone properties, such as uniaxial compressive strength, shore hardness, quartz content, and bending strength, must be determined for the best segment wear ranking.

1. Introduction

The prediction of stone sawability directly affects cutting parameter selection and sawing cost estimation for stone companies. A reasonable prediction of stone sawing can make the process more efficient. Scholars have studied the prediction of sawability by using diamond tools to cut stones. Stone sawability is related to rock properties and cutting parameters. Sawability criteria are mainly based on power consumption, slab production rate, specific cutting energy, classification, segment wear, and sawing efficiency. The main evaluation methods mainly include fuzzy analytic hierarchy process (FAHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approaches, artificial neural networks, multiple regression statistical analysis, and multifactorial fuzzy approach. Brazilian tensile strength, uniaxial compressive strength, hardness, bending strength, quartz content, Young’s modulus, impact strength, shear strength, density, porosity, abrasivity, and grain size are the input parameters of the prediction model. The sawability criteria, main evaluation methods, and input parameters are presented in Tables 1, 2, and 3, respectively.

Table 1: The main sawability criteria.

Sawability criteria
References (researchers)

Power consumption
Mikaeil et al. (2011) [1]

Slab production
Mikaeil et al. (2013) [2]; Kahraman et al. (2006) [3]; Kahraman et al. (2004) [4]; Ataei et al. (2012) [5]; Tumac (2016) [6]; Mikaeil et al. (2011) [7]; Ersoy and Atıcı (2004) [8]; Fener et al. (2007) [9]; Sadegheslam et al. (2013)

Specific cutting energy
Yurdakul and Akdaş (2012) [10]

Classification
Tutmez et al. (2007) [11]; Delgado et al. (2005) [12]

Segment wear
Xu and Zhang (2004) [13]; Wei et al. (2003) [14]; Eyuboglu et al. (2003) [15]; Ersoy et al. (2005) [16]; Özçelik (2007) [17]; Buyuksagis (2007) [18]

Sawing efficiency
Buyuksagis and Goktan (2005) [19]

Table 2: The main evaluation methods.

Evaluation methods
References

FAHP and TOPSIS
[1, 2, 20]

Artificial neural networks
[3, 6, 13]

Multiple regression statistical analysis
[4, 5, 7–10, 15–19]

Multifactorial fuzzy approach
[11, 13]

Table 3: The input parameters of the prediction model.

Input parameters
References

Uniaxial compressive strength
[1, 2, 4, 6–11, 14–19]

Brazilian tensile strength
[4, 7–11, 15–19]

Hardness
[1, 2, 4, 6–12, 14–19]

Bending strength
[10, 16]

Quartz content
[3, 8, 12, 14, 16–19]

Young’ modulus
[1, 2, 8, 15, 16]

Impact strength
[4, 8, 9, 11, 16]

Shear strength
[3, 8, 16]

Density
[6, 8, 10, 16, 18]

Porosity
[6]

Abrasivity
[1, 2, 4, 6–9, 11, 14, 16, 18, 19]

Grain size
[8, 16]

The effects of carbonate rock properties, such as uniaxial compressive strength, Schmiazek -abrasivity factor, Mohs hardness, and Young’s modulus on power consumption and production rate have been reported [1, 2]. The artificial neural network method is better than the statistical regression method to product the slab production of carbonated stones by shear strength [3]. The influence of P-wave velocity, impact strength, point load strength, and Schmidt hammer value on slab production were evaluated [4]. A slab production prediction model was established using some rock properties, such as uniaxial compressive strength, Schmidt hammer valve, Los Angeles abrasion (LA abrasion), and Brazilian tensile strength [5]. The relationship between slab production and rock properties including uniaxial compressive strength, Brazilian tensile strength, Cerchar abrasivity index, porosity, and density was proposed [6]. A classification method of stone sawability was proposed, and the sawing rate was classified into five categories [7]. The influence of cutting parameters and rock properties on specific sawing energy was reported [8]. Production can be predicted from LA abrasion loss, tensile strength, and compressive strength [9]. The specific cutting energy based on rock properties and cutting parameters was evaluated [10]. Sawing performance was classified into three categories, and the stone processing companies can select a suitable saw to cut stones only by the model developed [11]. Rock hardness has greatly influenced stone sawing rates compared with other stone properties [12]. The influences of cutting speed and depth of cut on segment wear were studied [13]. Segment wear and cutting force can be evaluated based on rock properties [14]. Shore scleroscope hardness, water absorption, and cone indenter hardness had a greater effect on segment wear than other rock properties [15]. The wear mechanism of diamond segments and a matrix was studied, and the cutting specific energy during the sawing process associated with segment wear was proposed [16]. The effects of mineralogical properties on segment wear and sawing speed were tested. Experimental results indicated that as the texture coefficient values increased, wear and sawing speed increased; however, as the grain size increased, segment wear decreased [17]. A predictive model of specific wear showed that plagioclase and bending strength were the most dominant rock parameters [18]. The optimum sawing performance for a particular stone based on depth of cut and travel speed was investigated [19].

Stone sawability when using a diamond frame saw to cut granite was predicted in this study. A prediction model was established by using segment wear as the evaluation criterion, and stone properties, including SiO₂ content, quartz content, orthoclase content, plagioclase content, shore hardness, density, uniaxial compressive strength, and bending strength as input parameters, and FAHP and TOPSIS techniques were employed to evaluate the sawability.

2. Theoretical Concepts

2.1. FAHP—Fuzzy Analytical Hierarchy Process

2.1.1. Triangular Fuzzy Number

The theory of fuzzy sets was first proposed by Zadeh in 1965 [21]. The method of fuzzy comparison judgment based on triangular fuzzy number was described by van Laarhoven and Pedrycz in 1983 [22].

Definition 1. is defined as a fuzzy number on (), if its membership function , is illustrated in

Set and as the upper and lower bounds of the fuzzy number , respectively, is the maximum value, and the triangular fuzzy number () is shown in Figure 1.
Operation rules of two fuzzy numbers [23]: if and , the algorithms of and are as follows:

Figure 1: Triangular fuzzy number membership function.

2.1.2. Fuzzy Analytic Hierarchy Process

The method of fuzzy analytic hierarchy process has been used widely [24, 25], and it is divided into several steps: constructing the fuzzy judgment matrix, determining the initial weight, and gaining the final weight, as described next.

(1) Constructing the Fuzzy Judgment Matrix. Figure 2 shows the relative weight of the evaluation index. Based on a nine-point fundamental scale [26], the triangular fuzzy judgment set is provided in Table 4 [27]. According to this nine-point fundamental scale, experts compare each group (such as and ) to get a fuzzy number:

Then the fuzzy numbers are integrated:

Table 4: Relative weight of evaluation index.

Relative weight
Illustration

Criterion is equally important compared to criterion

Criterion is moderately more important than criterion

Criterion is more important than criterion

Criterion is strongly more important than criterion

Criterion is extremely more important than

, , ,
The scale value corresponding to the intermediate state

Reciprocal
If the ratio of and is , the importance of and is ,

Figure 2: Fuzzy numbers of linguistic variable set.

(2) Determining the Initial Weight. Let be an object set and be the target set, as described [28]. The degree analysis values for each object are recorded as , , where   () are triangular fuzzy numbers.

The fuzzy synthetic value (initial weight) of the first object is expressed as follows:

where

(3) Gaining the Final Weight

Definition 2. and are two triangular fuzzy numbers. If and , the possibility degree of is defined as triangular fuzzy function:

where sup is the smallest upper bound

Definition 3. The possibility of a fuzzy number greater than the other fuzzy numbers is defined as

if , , ; the final weight vector is described as

Standardized weight vector is obtained through standardized processing as follows:

2.2. TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution)

The TOPSIS evaluation method was first proposed by Hwang and Yoon [29] based on the degree of closeness between a limited evaluation object and an ideal goal. TOPSIS evaluation methods are as follows:

(1)Normalizing the evaluation object:

(2)Constructing a normalized weighting matrix:

(3)Determining positive and negative ideal solutions:

(4)Calculating the distance between the evaluated object and the positive and negative ideal:

 Distance to ideal solution:

 Distance to negative solution:

(5)Calculating the closeness of each evaluation index to the ideal solution:

(6)Determining the ranking of evaluation indexes according to values.

3. Materials and Methods

Ten granite samples were selected from a stone sawing company and used as the workpiece for the experiments. Table 5 lists the main physical and mechanical properties of the stone workpiece. The rock properties were tested by a construction engineering quality test center in Shandong, China. A uniaxial compressive strength test was carried out using five 50-mm cubic specimens at a 0.6~0.4 Mpa/s constant loading rate according to GB/T 9966.1 Chinese Standard. Next, the bending strength test was performed using five specimens with dimensions of 20 mm × 40 mm × 160 mm at a 0.2 Mpa/s loading rate according to GB/T 9966.2 Chinese Standard. The shore hardness test was conducted in accordance with GB/T 9966.5 Chinese Standard using five specimens with dimensions of 20 mm × 100 mm × 100 mm. The distance of the test point from the workpiece edge was greater than 10 mm. A density test was done in accordance with GB/T 9966.3 Chinese Standard using five 50-mm cubic specimens.

Table 5: Main physical and mechanical properties of stone workpiece.

Rock properties
G1
G2
G3
G4
G5
G6
G7
G8
G9
G10

SiO₂ (%)
72.36
75.25
72.78
73.28
73.82
70.19
67.25
64.2
72.53
67.34

Quartz (%)
29.2
40.3
32.15
28.7
36
26.7
45.11
25.2
31.1
19.3

Orthoclase (%)
20.35
41.35
56
49
27.98
56.2
17.65
57.9
27.35
30.25

Plagioclase (%)
42.5
13.35
10.45
13.3
20.53
13.2
25.07
8
35.26
25

Shore hardness (HSD)
85
102
98.46
111
85.14
115
104
90.5
101
75

Density (g/cm³)
2.56
2.68
2.7
2.6
2.61
2.65
2.58
2.64
2.62
2.65

Bending strength (MPa)
8.93
15.1
20.64
14.8
20.16
17.06
17.1
12.8
12.9
7.63

Compression strength (MPa)
92.3
165.9
226.3
162.5
199.48
168.29
209.9
153.8
219.3
85.69

A diamond frame saw is typically used to cut large stone blocks (~2 m × 2 m × 2 m.). Figure 3 shows the sawing machines that can mount 60~120 blades. The stroke of a diamond frame saw is 600 mm. The flywheel speed is ~85 r/min, and the feed rate of the block table is 60 mm/h. The blade size is 4500 mm × 180 mm × 3.5 mm and the size of the diamond segments is 20 mm × 10 mm × 4.5 mm. The mesh of the diamond particles is 40/50, and the diamond concentration is 20.

Figure 3: Sawing machine.

4. FAHP–TOPSIS Evaluation of Sawability Using a Diamond Frame Saw to Cut Granite

4.1. Determining the Standardized Weight

  (), quartz (), orthoclase (), plagioclase (), shore hardness (), density (), bending strength (), and compression strength () were chosen as input parameters, and the diamond segment wear was predicted using a diamond frame saw to cut granite.

Twenty decision-makers evaluated the importance of these factors by completing a questionnaire. Seven of the respondents are stone industry professors, graduate students, or other scientific researchers. Seven respondents are granite plate production enterprise managers and workshop workers, and the rest are engineers or people who work in companies that manufacture stone processing equipment. To increase the reliability of the weight factors, each decision-maker scored the parameters according to Table 4. The pair-wise comparisons of the decision-makers’ values are transformed in the fuzzy judgment matrix, as shown in Table 6.

Table 6: Fuzzy judgment matrix.

The fuzzy comprehensive value was calculated according to (5), and the initial weights are shown in Table 7. According to (8) and (9), the final weight was determined and is presented in Table 8. The final weight was standardized, to obtain the normalized weight of the stone parameters.

Table 7: Initial weight.

SiO₂
0.0490
0.0878
0.1535

Quartz
0.1377
0.2219
0.3561

Orthoclase
0.0314
0.0580
0.1097

Plagioclase
0.0198
0.0358
0.0656

Shore hardness
0.0726
0.1252
0.2120

Density
0.0144
0.0212
0.0366

Bending strength
0.1022
0.1700
0.2798

Compression strength
0.1787
0.2802
0.4375

Table 8: Final weight and normalized weight.

Final weight

Normalized weight

4.2. Ranking the Sawability

Because the values of , , , and are zero, stone properties with nonzero values, including quartz (), shore hardness (), bending strength (), and compression strength (), were chosen to rank the sawability, as listed in Table 9.

Table 9: Stone parameter data.

Granite
Quartz (%)
Shore hardness (HSD)
Bending strength (MPa)
Compression strength (MPa)

()
()
()
()

G1

G2

G3

G4

G5

G6

G7

G8

G9

G10

First, normalization processing of the data was performed by application of (12), as listed in Table 10. Then, a normalized weighting matrix was constructed via (13), as listed in Table 11.

Table 10: Normalized matrix.

Quartz (%)
Shore hardness (HSD)
Bending strength (MPa)
Compression strength (MPa)

()
()
()
()

Table 11: Weighting matrix.

Quartz (%)
Shore hardness (HSD)
Bending strength (MPa)
Compression strength (MPa)

()
()
()
()

According to (14), the positive and negative ideal solutions are as follows:

The evaluation results were determined by application of (15) and (16) and are shown in Table 12.

Table 12: Evaluation results.

Ranking

4.3. Experimental Results

Table 13 lists the experimental results of segment wear as measured by micrometer. A graph of segment wear against was constructed, as shown in Figure 4. As segment wear increases, increases. The determination coefficient ( = 0.9265) illustrated that there was a high statistical correlation between segment wear and the value. Thus, the prediction model of sawability is acceptable and reasonable using the FAHP and TOPSIS method. The regression equation of segment wear and value is as follows:

Table 13: Experimental results of segment wear (μm).

Stones
G1
G2
G3
G4
G5
G6
G7
G8
G9
G10

Wear (μm)
110
167
184
137
155
147
176
123
151
95

Figure 4: Graph of segment wear against .

5. Conclusions

A diamond frame saw is capable of mounting more than 60 blades, each 4500 mm in length. Twenty-six diamond segments are welded on each blade. Segment wear is directly related to the cutting performance and sawing cost, so an accurate prediction of the segment wear of a diamond frame saw would be beneficial to a stone sawing company.

The predicted segment wear of granite was studied using FAHP and TOPSIS mathematical methods. FAHP is utilized to determine the weight of factors, and TOPSIS is used to rank the sawability. For validation of the employed ranking method, a sawing experiment was conducted and diamond segment wear was measured and was used as the evaluation criterion. A prediction mathematical model of and segment wear was established, and the analysis showed that these two values showed a high statistical correlation. Overall, this ranking method is reasonable and acceptable for evaluating segment wear at a stone sawing company using a diamond frame saw to cut a large granite block (2 m × 2 m × 2 m). Segment wear of a diamond frame saw can be determined based on rock properties, such as quartz, shore hardness, bending strength, and compression strength.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This research received financial support from the Independent Innovation and Achievement Transformation Special Project of Shandong Province, China (2014CGZH0802). The authors are most grateful to Rizhao Hein Saw Co., Ltd. and Shandong Renown Diamond Tools Co., Ltd. for supporting this work by providing the frame saw, diamond segment, and dimensioned rock samples.

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