Prediction of specific cutting energy in natural stone cutting processes using the neuro-fuzzy methodology

Yurdakul, Murat; Gopalakrishnan, Kasthurirangan; Akdaş, Hürriyet

doi:10.1016/j.ijrmms.2014.01.015

Cited by 40 publications

(10 citation statements)

References 28 publications

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“…These parameters are selected as a result of literature search. 11,12,14,18,19 In real applications, measuring some of these parameters can be difficult or expensive. However, first aim of this study is to determine the most proper features.…”

Section: Description Of Experimental Proceduresmentioning

confidence: 99%

“…They developed a ploughing detection algorithm that compares the normalized variance in peak values with preset thresholds. Yurdakul et al 14 proposed adaptive hybrid intelligence (AHI) techniques to develop SE prediction models based on 40 different natural building stones in 19 different stone processing plants. The feed rate, depth of cut and uniaxial compressive strength, bending strength and point load strength of the rock to be cut which constitute rock physical-mechanical properties were used as the input parameters in the development of SE prediction models.…”

Section: Introductionmentioning

confidence: 99%

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Classification of disc damage status by discovering knowledge from experimental data in marble cutting process

Hocaoğlu

Ucun

2016

Proceedings of the Institution of Mechanical Engineers, Part C:

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In marble industry, it is of vital importance to determine the damaged discs on time to prevent possible industrial injuries. Therefore, in this study, it is proposed to classify the status of the cutting discs that are used while cutting the natural stones. To classify the deflections of the discs, 673 different experiments are performed. Cutting discs corresponding to four different damage classes (undamaged disc, less damaged disc, much damaged disc, and broken disc) are employed in the tests. Eight different parameters (cutting forces (F x , F y , F z ), noise, peripheral speed of the disc, current, voltage, power consumption) are measured and recorded in the experiments. For each experiment, mean values of different measured data are studied. Artificial neural networks are employed as classifiers. In the first stage, all of these mean values corresponding to eight parameters are selected as the input vectors of the artificial neural networks, whereas in the second stage, the dimension of input vector is decreased by leaving out the parameters one by one. In this stage, it is aimed to determine the most important parameter that caries much more information about the cutting process.

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Section: Description Of Experimental Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of disc damage status by discovering knowledge from experimental data in marble cutting process

Hocaoğlu

Ucun

2016

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…Many studies have been found on the C f in circular saws depending on the physicomechanical properties of natural rocks, modeling of the S e and socket [19][20][21][22][23], the definition of the theoretical chip geometry [24], and connections between tangential cutting force and chip thickness [25][26][27]. Some other studies address the effect of processing parameters on tool wear [28][29][30][31], the modeling of natural stone cutting with diamond cutting tools [32] and specific grinding energy of chip samples under a scanning microscope [33] in order to determine specific cutting energy and power consumption [34,35]. Energy and the type of cutting mechanism used in the production of end products from natural rocks lead to the wear of cutting tools.…”

Section: Introductionmentioning

confidence: 99%

Force and Specific Energy in Natural Rocks Cutting by Four-Axis Machine

Sarıışık¹

2019

Earth Crust

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This study used a four-axis computer controlled machine to determine the cutting forces (C f), specific cutting energy (S c) and specific energy (S e) affecting the processability of natural rocks. A total of 17 types of natural rocks were categorized according to their geological formations. This study consisted of three parts: (1) experimental measurement of the C f resulting from processing natural rocks using a 6.0-mm diameter end mill cutting tool and a four-axis machine; (2) calculation of C f ,S c and S e values; and (3) statistical analysis of the parameters. In terms of C f ,S c and S e values, a statistically significant difference (P < 0.001) was observed among a depth of cut (d p) and feed speed (V a). It was found that the parameters that affected the processability of natural rocks were d p and V a. Accordingly, the processability of natural rocks processing type, d p and V a was found that affected. Simple linear regression was performed to examine the relationship between physicomechanical properties, and between C f ,S e and S c values. As a result, regression equations were developed in consideration of the physicomechanical properties of natural rocks and the C f ,S c and S e equation for the prediction of the efficiency of computer numerical control (CNC) machines.

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“…A considerable amount of literature indicate that the physical and mechanical properties of the rock are the main factors affecting the specific energy of rock cutting. In this context, some prediction models of specific energy have been developed by using single factor regression analysis (Copur et al 2003;Bilgin et al 2006;Tumac et al 2007, Gunes et al 2015, regression trees and artificial neural networks (Tiryaki 2009), and adaptive hybrid intelligence techniques (Yurdakul et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Tool Forces and Specific Energy Prediction Models in the Process of Sandstones Cutting by Using Conical Picks

Wang¹,

Wang²,

Su³

2018

Bilimsel Madencilik Dergisi

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In this study, unrelieved rock cutting experiments were conducted at the linear rock cutting machine and the characteristics of tool forces were discussed. The correlations among tool forces, specific energy, cutting depth, and rock strength were analyzed using single factor regression analysis method. Based on multiple non-linear regression method, the models of tool forces and specific energy were developed considering the rock strength and cutting depth. The results indicate that models of tool forces have the superior performance. When the model of specific energy is analyzed using the compressive strength of the rock, it was seen that the correlations are weak compared to the model related to tensile strength of rock. In conclusion, it is emphasized that the proposed models presented in this study are particularly recommended for performance prediction of soft and medium-hard strength sandstones in case conical picks are employed. ÖZ Bu çalışmada, doğrusal kesme setinde bağımsız kesme deneyleri yapılmış ve buna göre keski kuvvetlerinin karakteristiği tartışılmıştır. Tek faktörlü regresyon analiz yöntemi kullanılarak keski kuvvetleri, spesifik enerji, kesme derinliği ve kayacın dayanımı arasındaki ilişkiler analiz edilmiştir. Ayrıca, kayacın dayanımı ve kesme derinliği göz önüne alınarak doğrusal olmayan çoklu regresyon yöntemiyle keski kuvvetleri ve spesifik enerji modelleri geliştirilmiştir. Sonuçlar, kesme ve normal kuvvetleriyle ilgili tüm modellerin oldukça üstün olduğunu göstermiştir. Spesifik enerji ile basınç dayanımına bağlı olarak model kurulduğunda ilişki katsayılarının çekme dayanımına göre zayıf olduğu belirlenmiştir. Sonuç olarak, bu çalışmada sunulan modellerin yumuşak ve ortasert dayanımdaki kumtaşının kalem uçlu keski kullanılarak kazılması durumunda kullanımının önerilebileceği vurgulanmıştır.

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Prediction of specific cutting energy in natural stone cutting processes using the neuro-fuzzy methodology

Cited by 40 publications

References 28 publications

Classification of disc damage status by discovering knowledge from experimental data in marble cutting process

Classification of disc damage status by discovering knowledge from experimental data in marble cutting process

Force and Specific Energy in Natural Rocks Cutting by Four-Axis Machine

Tool Forces and Specific Energy Prediction Models in the Process of Sandstones Cutting by Using Conical Picks

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