A sampling study on rock properties affecting drilling rate index (DRI)

Yenice, Hayati; Ozdogan, Mehmet Volkan; Özfırat, M. Kemal

doi:10.1016/j.jafrearsci.2018.01.015

Cited by 20 publications

(5 citation statements)

References 9 publications

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“…For the experiment, the perforation/drilling process 29 of the Calacatta-Carrara white marble is executed using 5-axes CNC working center (OMAG-Mill5). The 2 3 32 holes were processed using four (300 L 3 300 W 3 30 H ) plates.…”

Section: Experimental Design and Data Processingmentioning

confidence: 99%

Assessment of cylindricity and roughness tolerances of holes drilled in marble using multiple regression and artificial intelligence

Abbassi

Trabelsi

Akrichi

et al. 2021

Advances in Mechanical Engineering

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The Calacatta-Carrara marble is widely used due to its excellent physico-chemical characteristics and attractive aspect. However, the sensitivity of this materiel, when performing delicate manufacturing operations, presents for the engineers a hard challenge to overcome. This issue is mainly encountered with complex shapes of parts, for which it is difficult to preserve surface integrity and avoid geometric defects. The paper aims at finding out optimal drilling parameters of cutting in the Calacatta-Carrara white marble material, in order to minimize the holes cylindricity (HC) and surface roughness (HR) using six controlled operating factors, namely, the rotation speed ( N), the feed speed ( F), the drill bit diameter (BD), the drill bit height (BH), the number of pecking cycles ( P), and the drilling depth (DD). The experimental design uses a [Formula: see text] fractional factorial plan that is replicated once for cost consideration. The optimization process, that is, minimum cylindricity and roughness tolerances, is carried out using the Gray Relational Analysis (GRA) technique. Numerical modeling of machining parameters is performed using Multi-Layer Perceptron Artificial Neural Network (MLP ANN) and Multiple Regression Model (MR) to predict surface quality. For the sake of completeness these two models were compared in terms of fitness and predictability. The models were assessed statistically using the correlation coefficient. Results showed that either solution predicts a roughness tolerance which is in good agreement with the test data (both R-sq.(adj.) and R-sq.(pred.) >94%). However, the holes cylindricity tolerance response was shown to be superior with MLP-ANN model (R-sq.(adj.) 50.64% and R-sq.(pred.) 48.67%). The GRA analysis shows that minimum cylindricity and roughness are met when N and F are set high, BD and BH low, P high and DD low.

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Section: Experimental Design and Data Processingmentioning

confidence: 99%

Assessment of cylindricity and roughness tolerances of holes drilled in marble using multiple regression and artificial intelligence

Abbassi

Trabelsi

Akrichi

et al. 2021

Advances in Mechanical Engineering

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“…The perforability can be defined as the drilling of a rock in a certain time. 22 The operation of diamond wire cutting depends essentially on the physical, chemical, mechanical, and mineralogicalpetrographic properties of the rock. 23,24 The physicomechanical properties of the rock used in the experiments are shown in Table 2.…”

Section: Materials Choice: Machine Workpiece and Cutting Toolmentioning

confidence: 99%

Experimental study of drilling white Calacatta–Carrara marble using artificial neural approach

Abbassi

Akrichi

Yahia

2019

Advances in Mechanical Engineering

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Highly accurate marble processing is increasingly needed to comply with tight parametric/geometric tolerances and surface integrity specifications encountered while structuring, sculpture, and decorating. In this study, a new approach based on the artificial neural network technique is evaluated for the prediction of process parameters in the machining of white Calacatta-Carrara marble. The rotation speed, feed speed, drill bit diameter, drill bit height, number of pecking cycles, and drilling depth were considered as input factors. Corresponding surface roughness, hole circularity, hole cylindricity, and hole-location error were sought in output. A series of experiments was carried out using a 5-axes computer numerical control vertical machining center (OMAG) to obtain the data used for the training and testing of the artificial neural network with reasonable accuracy, under varying machining conditions. A MATLAB TM interface was developed to predict surface roughness and geometric defects (circularity, cylindricity, and localization). A 6 3 4 size multilayered neural network was developed. The number of iterations was 1000 and no smoothing factor was used. The drill quality (holelocation error, hole circularity, and hole cylindricity) and the surface roughness were modeled and evaluated individually. One hidden layer used for all models, with the number of neurons for all the responses being executed separately, was 12 while the number of neurons in the hidden layer, with all the responses executed together, was 14. In conclusion, from the obtained verified experimentally optimization results, the errors are all within acceptable ranges, which, again, confirm that the artificial neural network technique is an efficient and accurate method in predicting responses in drilling.

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“…Uncontrollable factors are geological variables, geomechanical properties of rock and rock mass properties. Most researchers have studied on drilling rate index for selection of the most proper excavation or drilling machine and investigated the relationship between rock mechanical properties and drilling rate index (Thuro 1997, Tumac et al 2007, Altindag 2002, Kahraman 2002, Dahl 2003, Hoseinie et al 2008, Moein et al 2014, Ceryan 2014, Ataei et al 2015, Taheri et al 2016, Ozfirat et al 2016, Yaralı 2017, Yetkin et al 2016, Rostami & Chang 2017, Yenice et al 2018.…”

Section: Introductionmentioning

confidence: 99%

Examining of rock drilling properties in underground metal mine excavation

Yetkin

2021

An. Acad. Bras. Ciênc.

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In underground metal mines, drilling and blasting is widely used production method. Drilling holes is especially a time consuming operation. The aim of this study is to examine and analyze drilling operation and its duration in underground ore and dead rock. Drillability experiments are carried out on samples taken from the field. In core sampling two different types of core samples are taken whether it is parallel or perpendicular to ore bedding and dead rock. Results of drillability experiments are compared between the two types of core samples. DRI values are found to be 51 and 47 for the parallel and perpendicular core samples to ore bedding, respectively. DRI values for the parallel and perpendicular core samples to dead rock bedding are found to be 57 and 55 for the, respectively. In addition, graphs of drilling speed and drilling depth belonging to ore and dead rock are analyzed. It is examined that drilling speed increases steadily to maximum point in the first 5sec. and stays stable after that point. Therefore, it is concluded that production galleries and holes which are formed parallel to bedding would increase production efficiency.

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A sampling study on rock properties affecting drilling rate index (DRI)

Cited by 20 publications

References 9 publications

Assessment of cylindricity and roughness tolerances of holes drilled in marble using multiple regression and artificial intelligence

Assessment of cylindricity and roughness tolerances of holes drilled in marble using multiple regression and artificial intelligence

Experimental study of drilling white Calacatta–Carrara marble using artificial neural approach

Examining of rock drilling properties in underground metal mine excavation

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