Specific Energy Prediction for Circular Diamond Saw in Cutting Different Types of Rocks Using Multivariable Linear Regression Analysis

Ersoy, A.; Atici, U.

doi:10.1007/s10913-005-0089-x

Cited by 24 publications

(11 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A number of statistical factors are of importance in linear regression analysis, including the coefficient of determination, the coefficient of correlation, the adjusted coefficient of determination, standard error of the regression coefficient, confidence level, standard error, model error, significance level, t-distribution, F-distribution and the residual. Detailed explanations of these parameters can be found in previous studies (Ersoy and Atici, 2005;Myers, 1990;Tukey, 1997).…”

Section: Combined Methodsmentioning

confidence: 94%

Prediction of the strength of mineral-addition concrete using regression analysis

Atici

2010

Magazine of Concrete Research

Self Cite

View full text Add to dashboard Cite

The aim of this study was to develop a method for estimating concrete strength through non-destructive testing (NDT) methods. 27 different concrete mixtures were prepared with additions of high-volume fly ash and blastfurnace slag in place of Portland cement. Tests were then conducted to determine compressive strength (CS), splitting tensile strength (STS), flexural tensile strength (FTS), rebound number (RN) and ultrasonic pulse velocity (UPV) at 3, 7, 28, 90 and 180 days after preparation. The well-known traditional RN and UPV tests were used in this study, and correlations between NDT data and the strength properties of concrete were statistically evaluated using simple regression analysis. The analysis revealed exponential, geometric and power relationships between RN and CS, STS and FTS, respectively, with moderate correlation coefficients of 0 . 73, 0 . 72 and 0 . 72. Exponential and hyperbolic relationships were found between UPV and CS, STS and FTS, yielding correlation coefficients of 0 . 85, 0 . 85 and 0 . 79, respectively. Taking advantage of the fact that UPV and RN are related to different concrete properties, a combined method was applied to estimate concrete strength. The correlation coefficients of the models for CS, STS and FTS were 0 . 90, 0 . 84 and 0 . 88, respectively. The accuracy of the model in predicting CS was verified by statistical tests, and it was found to be reliable in practical situations.

show abstract

Section: Combined Methodsmentioning

confidence: 94%

Prediction of the strength of mineral-addition concrete using regression analysis

Atici

2010

Magazine of Concrete Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…Some researchers found strong correlations between Schmidt hardness value and the cutting rate of roadheaders, tunnel boring machines and impact hammers (Bilgin et al, 1996(Bilgin et al, , 2002Howarth et al, 1986;Poole and Farmer, 1978;Goktan and Gunes, 2005). Additionally, Schmidt hammer value is used in rock cutting applications and sawability for prediction of performance of the cutting process (Kahraman et al 2004;Ersoy and Atici, 2005;Yurdakul and Akdas, 2012).…”

Section: Prediction Of Ucs and Se From R L Valuesmentioning

confidence: 99%

Performance Prediction of Chain Saw Machines Using Schmidt Hammer Hardness

Dursun¹

2018

Bilimsel Madencilik Dergisi

View full text Add to dashboard Cite

Schmidt hammer hardness (R L ) provides a quick and inexpensive measure of surface hardness that is widely used for estimating the mechanical properties of rock material such as strength, sawability, cuttability and drillability. In this study, R L as predictors, which is thought to be a useful, simple and inexpensive test particularly for performance prediction of chain saw machine (CSM), is suggested. This study aims to estimate CSM performance from R L values of rocks. For this purpose, rock cutting and rock mechanics tests were performed on twenty four different natural stone samples having different strength values. In this study, Chain Saw Penetration Index (CSPI) has been predicted based on R L which is one of the two models previously used for performance prediction of CSMs. The R L values were correlated with UCS, CSPI and SE using simple regression analysis with SPSS 15.0. As a result of this evaluation, R L has a strong relation with UCS and SE. It is statistically proved that the model based on R L for predicting CSPI is valid and reliable for performance prediction of CSM. Results of this study indicated that the CSPI of CSMs could be reliably predicted by empirical model using R L . ÖZSchmidt çekici sertliği (R L ) kayaların dayanım, kesilebilirlik (doğrusal ve dairesel) ve delinebilirlik gibi mekanik özelliklerini belirlemek için yaygın olarak kullanılan ucuz ve kolaylık sağlayan bir yüzey sertliği ölçüsüdür. Bu çalışmada, özellikle zincirli kesme makinesinin performans tahmininde, kullanışlı, basit ve ucuz bir test olan Schmidt çekici sertliği değişken olarak önerilmiştir. Bu çalışmada amaç, kayaların Schmidt sertliklerinden zincirli kesme makinelerinin performansını tahmin etmektir. Bunun için, farklı dayanım özelliklerine sahip 24 farklı doğal taş numunesi üzerinde kesme ve kaya mekaniği testleri yapılmıştır. Bu çalışmada, zincirli kesme makinelerinin performans tahmini için daha önce kullanılan iki modelden biri olan Zincirli Kesme Penetrasyon İndeksi (CSPI) R L baz alınarak öngörülmüştür. R L değerleri ile tek eksenli basınç dayanımı, zincirli kesme indeksi ve spesifik enerji değerlerinin korelasyonu SPSS 15.0 istatistik programı kullanılarak yapılmıştır. Bu değerlendirme sonucunda; R L değerleri ile tek eksenli basınç dayanımı ve spesifik enerji değerleri arasında güçlü korelasyon olduğu belirlenmiştir. Buna göre; zincirli kesme indeksini tahmin etmek için R L 'ye dayanan modelin zincirli kesme makinesinin performans tahmini için geçerli ve güvenilir olduğu istatistiksel olarak kanıtlanmıştır. Bu çalışmanın sonuçları, zincirli kesme makinelerinin zincirli kesme indeksini, R L değerleri kullanılarak oluşturulan görgül modeller ile güvenilir bir şekilde tahmin edilebileceğini göstermiştir.

show abstract

“…Finding a way to predict the specific energy of the cutting process based on available information such as rock properties, machine parameters, and operating conditions can greatly contribute to an accurate estimation of costs, energy consumption, and the ef- . In a study by Ersoy et al (2005), they investigated the performance of a circular diamond saw by conducting experiments on 3 groups of stones that were cut with feed rates and depths of cut at a constant speed, and used the results to formulate an equation for the relationship between specific energy, sawing parameters, and rock properties. In this study, the model for the prediction of main rock characteristics was obtained by the multivariate linear regression method.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Geological Properties of Dimension Stones on the Prediction of Specific Energy (Se) During Diamond Wire Cutting Operations

Khoshouei

Jalalian

Bagherpour

2020

MGPB

View full text Add to dashboard Cite

Given the increasing demand for dimension stones, mining operations in quarries have always been an important branch of mining engineering. Among different techniques, diamond wire cutting is one of the most common methods of dimension stone mining. A reliable assessment and accurate prediction of diamond wire cutting performance are essential for feasibility analysis and operational planning in this area. This performance depends on factors such as physical, mechanical, and textural properties of the rock and the characteristics of cutting operations which can be evaluated by criteria such as specific energy, production rate, efficiency, and diamond bead wear rate. This study aims to develop a method for predicting the specific energy of diamond wire cutting in dimension stones based on rock properties. For this purpose, the specific energy of diamond wire cutting in 11 different igneous rock samples was measured. Given the high strength and abrasivity of igneous rocks, cutting operations in these rocks generally requires a great amount of energy. In a series of tests performed on the samples, rock properties such as uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), Young's modulus, density, textural properties, abrasivity and operating factors such as pullback amperage were measured. The measured parameters were divided into four groups of physical, mechanical, textural, and operating parameters. After determining the specific cutting energy of each sample, the relationship of the energy with each individual property was investigated. This investigation showed that density, abrasivity, and p-wave velocity respectively had the highest correlation with specific energy. Using the correlation results, four input parameters (one from each of the four considered parameter groups) were selected for inclusion in the prediction model. These parameters were density, abrasivity, wave velocity, and amperage. Multivariate linear regression was then used to analyse the effect of rock properties and operating parameters on specific energy. The developed regression model showed that once the rock properties are known, the specific energy can be predicted with an accuracy of 85.8%. The proposed model can be used to estimate the specific energy of diamond wire cutting operations in dimension stone quarries in advance, and predict the amount of energy consumption, the required energy source, and the optimal cutting machine accordingly.

show abstract

Specific Energy Prediction for Circular Diamond Saw in Cutting Different Types of Rocks Using Multivariable Linear Regression Analysis

Cited by 24 publications

References 20 publications

Prediction of the strength of mineral-addition concrete using regression analysis

Prediction of the strength of mineral-addition concrete using regression analysis

Performance Prediction of Chain Saw Machines Using Schmidt Hammer Hardness

The Effect of Geological Properties of Dimension Stones on the Prediction of Specific Energy (Se) During Diamond Wire Cutting Operations

Contact Info

Product

Resources

About