کلاس‌بندی سنگ‏های ساختمانی از دیدگاه قابلیت برش با استفاده از روش خوشه‌بندی فازی

پیش‌بینی قابلیت برش سنگ به عنوان یکی از فاکتورهای موثر در تخمین هزینه‏ها و پیش‌بینی میزان تولید یک کارخانه فرآوری سنگ از اهمیت بالایی برخوردار است. بنابراین شناخت کامل سنگ‏های ساختمانی و ارزیابی توان اجرایی دستگاه‏های برش در کارخانه‏های فرآوری، طراحان و برنامه‏ریزان تولید را به سمت بهبود سرعت فرآوری و افزایش تولید سوق می‏دهد. از این رو، به کارگیری روش‏های نو و کاربردی برای دست‏یابی به این اهداف لازم و ضروری است. در این تحقیق سعی شده است پس از تعیین مهم‌ترین پارامترهای فیزیکی و مکانیکی موثر در فرایند برش، قابلیت برش‌پذیری نمونه سنگ‏های ساختمانی با استفاده از روش خوشه‌بندی فازی (fuzzy cmeans) مورد ارزیابی و کلاس‌بندی قرار ‏گیرد. بدین ترتیب 12 نمونه سنگ ساختمانی مشتمل بر دو گروه از سنگ‏های ساختمانی سخت و نرم مورد آزمایش و ارزیابی قرار گرفت. نمونه‌ها در مدل‏های 3، 4 و 5 کلاسه مورد ارزیابی و کلاس‌بندی قرار گرفت، سپس نتایج با شدت جریان مصرفی دستگاه برش مورد اعتبارسنجی قرار گرفت. نتایج حاصل از بررسی‏ها پس از اعتبارسنجی با آزمایش‏های دقیق، نشان داد که روش خوشه‌بندی فازی می‏تواند به عنوان یکی از روش‏های نو و کاربردی برای طبقه‌بندی و بررسی قابلیت برش نمونه سنگ‏های ساختمانی با توجه به معیارهای تاثیرگذار نظیر مقاومت فشاری تک محوری، سختی موهس، سایندگی شیمازک و مدول الاستیسیته مورد استفاده قرار گیرد.

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

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 Fabrasiveness factors (SFa), and young modulus (YM) were measured. Also, Fuzzy Cmeans 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 Cmeans 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 Cmeans 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 Cmeans Optimization, including uniaxial compressive strength, Mohs hardness, Schmiazek Fabrasivity 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 Cmeans as soft computing technique and some major mechanical properties such as uniaxial compressive strength, Mohs hardness, Schmiazek Fabrasivity 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.