Clustering of Ornamental Stones Based Upon the Sawability by Using the Fuzzy Clustering Technique

Document Type : Technical Note

Authors

1 Dept. of Mining Engineering, Faculty of Environment, Urmia University of Technology, Urmia, Iran

2 Dept. of Civil Engineering, University of Calabria, Via Bucci, 87036 Rende (CS), Italy

3 Dept. of Mining, Petroleum & Geophysics, Shahrood University of Technology, Shahrood, Iran

10.29252/anm.2019.1629

Abstract

Summary
The complete knowledge of the cutting process and the performance of the sawing machine can help increase the efficiency and quality of the manufactured product.  During the field studies, 12 rock samples from two kinds of ornamental stones were collected and some major mechanical properties including uniaxial compressive strength (UCS), Mohs hardness (Mh), Schimazek’s F-abrasiveness factors (SF-a), and young modulus (YM) were measured. Also, Fuzzy C-means has been used as a soft computing technique in this study. All different studied rock samples are classified into 3, 4, and 5 classes. Then, the results of classification were compared with consumed energy values. Finally, it can be concluded that FCM could be a simple but efficient tool in the evaluation of the sawability of ornamental stone as one of the most important factors to estimation and prediction of production cost and productivity of processing plants.
 
Introduction
The rock sawability and prediction of consumed energy is one of the most important factors in the estimation and prediction of production cost and productivity of processing plants. In this study, for laboratory tests, some rock blocks were collected from the studied quarries. Then, by using the fuzzy C-means clustering approached and considering laboratory results of rock samples, all of the rocks have been classified in 3, 4, and 5 separate clusters. The results clearly showed that the FCM algorithm was used as a reliable and efficient tool for classifying the ornamental stone.
 
Methodology and Approaches
Nowadays, with the increasing growth of uncertain problems, the use of soft computing techniques with high ability in solving these kinds of problems has increased significantly. Assessment of the sawability of ornamental stone is the most important factor in the identification and prediction of production cost and productivity of processing plants. Given the unreliability of all experimental laboratory, one of the most important tasks in the sawability of ornamental stone’ classification is using a method with the highest possible accuracy. According to the importance of the issue, in this study, the Fuzzy C-means algorithm is used for the classification. Four important physical and mechanical characteristics of 12 rock samples are considered for the classification of sawability of ornamental stone from two kinds of ornamental stone, including hard and soft rock groups using Fuzzy C-means Optimization, including uniaxial compressive strength, Mohs hardness, Schmiazek F-abrasivity factor, and Young's modulus. Finally, in this study, the results of the classification are compared with consumed energy.
 
Results and Conclusions
In this study employing Fuzzy C-means as soft computing technique and some major mechanical properties such as uniaxial compressive strength, Mohs hardness, Schmiazek F-abrasivity factor, and Young's modulus to evaluate the sawability of ornamental stone, 3 to 5 classes are considered. Generally, according to this research, the following remarks can be concluded:
1- The 12 rock samples from two kinds of ornamental stones, including hard and soft rock groups, are evaluated and tested.
2- A comparison was made between 3 different models (3 classes from 3 to 5 classes) with consumed energy of the sawing machine. The results show that all 12 rock samples are classified as very suitable.
3- In comparison to all classes, it can be concluded that FCM is a reliable technique for clustering the sawability of ornamental stone with highly acceptable degrees of robustness.

Keywords

Full Text

تا کنون مطالعات نسبتاً خوبی در مقیاس‏‏های مختلف صنعتی و آزمایشگاهی در زمینه قابلیت برش سنگ‏ها انجام شده است. در این میان برخی از محققین به بررسی خصوصیات سنگ و برخی هم به بررسی مشخصات طرح برش و برخی دیگر نیز به بررسی پارامترهای عملیاتی برش پرداخته‏اند. از میان این عوامل، مشخصات سنگ از جمله مهم‌ترین پارامترهایی بوده است که در بیش از نیمی از مطالعات مورد بررسی قرار گرفته است. مطالعات انجام شده در زمینه قابلیت برش سنگ‏ها را می‏توان در دو بخش ارائه سیستم‏های طبقه‏بندی سنگ‏ها و روابط تجربی برای ارزیابی عملکرد برش، تقسیم‏بندی کرد. جدول 1 مطالعات صورت گرفته توسط محققین در بخش سیستم‏های طبقه‌بندی سنگ‏های ساختمانی را نشان می‏دهد.

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Journal of Analytical and Numerical Methods in Mining Engineering
Volume 10, Issue 24
November 2020
Pages 137-148

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History

  • Receive Date: 17 October 2020
  • Accept Date: 17 October 2020

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