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Bit Diameter

The Experts below are selected from a list of 288 Experts worldwide ranked by ideXlab platform

Seung Hyo Jeong – 1st expert on this subject based on the ideXlab platform

  • The effect of multiple drilling on a sclerotic proximal tibia during total knee arthroplasty
    International Orthopaedics, 2015
    Co-Authors: Seung Hyo Jeong

    Abstract:

    Purpose To assess the depth of cement penetration and the occurrence of radiolucent line (RLL) according to drill Bit Diameter used in multiple drilling for the sclerotic bone of the medial proximal tibia during total knee arthroplasty (TKA). Methods The multiple drilling procedure was performed with 2.0 mm Diameter in group 1 ( n  = 290) and with 4.5 mm Diameter in group 2 ( n  = 109) to enhance the cement penetration. The postoperative RLL in the cement-bone interface and the depth of cement penetration were measured under the tibial implant at three, six, 12 and 24 months after TKA. The progression of RLL was also evaluated at the latest follow-up. Results Cumulative occurrence rates of RLLs were significantly lower in group 2 than in group 1 at 12 and 24 months postoperatively ( P  = 0.005 and 0.004). The depth or width was increased in nine cases only in group 1 at the latest follow up. There was no tibial implant loosening in both groups at the latest follow-up. Mean maximal depths of cement penetration were 1.1 mm in group 1 and 4.8 mm in group 2 at three months ( P  

  • The effect of multiple drilling on a sclerotic proximal tibia during total knee arthroplasty
    International Orthopaedics, 2014
    Co-Authors: Seung Hyo Jeong

    Abstract:

    Purpose
    To assess the depth of cement penetration and the occurrence of radiolucent line (RLL) according to drill Bit Diameter used in multiple drilling for the sclerotic bone of the medial proximal tibia during total knee arthroplasty (TKA).

Mahalingam Govindaraj – 2nd expert on this subject based on the ideXlab platform

  • Artificial neural network model for prediction of rock properties from sound level produced during drilling
    Geomechanics and Geoengineering, 2013
    Co-Authors: B. Rajesh Kumar, Harsha Vardhan, Mahalingam Govindaraj, Sowmya P. Saraswathi

    Abstract:

    In many rock engineering applications such as foundations, slopes and tunnels, the intact rock properties are not actually determined by laboratory tests, due to the requirements of high quality core samples and sophisticated test equipments. Thus, predicting the rock properties by using empirical equations has been an attractive research topic relating to rock engineering practice for many years. Soft computing techniques are now being used as alternative statistical tools. In this study, artificial neural network models were developed to predict the rock properties of the intact rock, by using sound level produced during rock drilling. A database of 832 datasets, including drill Bit Diameter, drill Bit speed, penetration rate of the drill Bit and equivalent sound level (Leq) produced during drilling for input parameters, and uniaxial compressive strength (UCS), Schmidt rebound number (SRN), dry density (ρ), P-wave velocity (Vp), tensile strength (TS), modulus of elasticity (E) and percentage porosity (n…

  • Regression analysis and ANN models to predict rock properties from sound levels produced during drilling
    International Journal of Rock Mechanics and Mining Sciences, 2013
    Co-Authors: B. Rajesh Kumar, Harsha Vardhan, Mahalingam Govindaraj, G S Vijay

    Abstract:

    Abstract This study aims to predict rock properties using soft computing techniques such as multiple regression, artificial neural network (MLP and RBF) models, taking drill Bit speed, penetration rate, drill Bit Diameter and equivalent sound level produced during drilling as the input parameters. A database of 448 cases were tested for determination of uniaxial compressive strength (UCS), Schmidt rebound number (SRN), dry density (ρ), P-wave velocity (Vp), tensile strength (TS), modulus of elasticity (E) and percentage porosity (n) and the prediction capabilities of the models were then analyzed. Results from the analysis demonstrate that neural network approach is efficient when compared to statistical analysis in predicting rock properties from the sound level produced during drilling.

  • Sound level produced during rock drilling vis-à-vis rock properties
    Engineering Geology, 2011
    Co-Authors: B. Rajesh Kumar, Harsha Vardhan, Mahalingam Govindaraj

    Abstract:

    Abstract The process of drilling, in general, always produces sound. Though sound is used as a diagnostic tool in mechanical industry, its application in predicting rock property is not much explored. In this study, an attempt has been made to estimate rock properties such as uniaxial compressive strength, Schmidt rebound number and Young’s modulus using sound level produced during rotary drilling. For this purpose, a computer numerical controlled vertical milling centre was used for drilling holes with drill Bit Diameters ranging from 6 to 20 mm with a shank length of 40 mm. Fourteen different rock types were tested. The study was carried out to develop the empirical relations using multiple regression analysis between sound level produced during drilling and rock properties considering the effects of drill Bit Diameter, drill Bit speed and drill Bit penetration rate. The F-test was used to check the validity of the developed models. The measured rock property values and the values calculated from the developed regression model are fairly close, indicating that the developed models could be efficiently used with acceptable accuracy in prediction of rock properties.

Ch. S. N. Murthy – 3rd expert on this subject based on the ideXlab platform

  • Temperature Measurement During Rotary Drilling of Rocks – A Statistical Approach
    International Conference on Emerging Trends in Engineering (ICETE), 2020
    Co-Authors: Vijay Kumar Shankar, B. M. Kunar, Ch. S. N. Murthy

    Abstract:

    This paper discusses a statistical analysis to measure the temperature during rotary drilling of fine-grained sandstone (pink) using embedded thermocouple method. The regression models consist of three input variables such as Diameter of the Bit, rpm and rate of penetration for different depth of thermocouples. Experimental test were conducted in computer numerical control (CNC) vertical machining centre. The measured temperature has been applied to study the influencing parameter using statistical technique. Analysis of variance (ANOVA) shows that the percentage contribution ratio of each operational parameters on temperature (output response). The most influencing parameter for temperature is rate of penetration with a percentage contribution of 71.32%, followed by drill Bit Diameter and spindle speed which contribute 19.27% and 2.99% respectively. The ANOVA and regression models for temperature give p-values of less than 0.05. Hence the predicted regression models are statistically significant and good predictive capabilities with acceptable accuracy.

  • ANN model for prediction of Bit–rock interface temperature during rotary drilling of limestone using embedded thermocouple technique
    Journal of Thermal Analysis and Calorimetry, 2019
    Co-Authors: Vijay Kumar Shankar, B. M. Kunar, Ch. S. N. Murthy

    Abstract:

    In the present work, an artificial neural network (ANN) model has been developed to predict the Bit–rock interface temperature using a newly fabricated grounded K-type thermocouple (range 0–1250 °C) during rotary drilling in a CNC vertical machining center. The data have been taken from experimental observation using an embedded thermocouple technique in the laboratory at room temperature (28 °C) using a masonry drill Bit. The observations were made using four different operational conditions, namely drill Bit Diameter (6, 8, 10, 12 and 16 mm), spindle speed (250, 300, 350, 400 and 450 rpm), rate of penetration (2, 4, 6, 8 and 10 mm min^−1) and depth (6, 14, 22 and 30 mm). The ANN has been developed based on the multi layer perceptron neural network (MLPNN) with four different input parameters. A Levenberg–Marquardt (LM) algorithm with feed-forward and backward propagation has been used in this model. The predicted value of the Bit–rock interface temperature with the highest R ^2 value provides a satisfactory result with the experimental data. The training value of RMSE is 1.2127, MAPE is 0.0196 and R ^2 is 0.9960, while the testing value of RMSE is 1.2770, MAPE is 0.0170 and R ^2 is 0.9978. The ANN model shows that the proposed MLPNN model successfully predicts the Bit–rock interface temperature during the rotary drilling of limestone.

  • Experimental Investigations on Penetration Rate of Percussive Drill
    Procedia Earth and Planetary Science, 2015
    Co-Authors: S. B. Kivade, Ch. S. N. Murthy, Harsha Vardhan

    Abstract:

    Abstract In this paper, detailed studies were carried out to determine the influence of rock properties on the penetration rate during pneumatic drilling. Further investigation was also carried out on the effect of thrust, air pressure, and compressive strength on penetration rate. Rock properties, like compressive strength and abrasivity of various samples collected from the field were determined in the laboratory. Drilling experiments were carried out on ten different rock samples for varying thrust, air pressure values and Bit Diameter. It was observed that very low thrust results in low penetration rate. Even very high thrust does not produce high penetration rate at higher operating air pressures. With increase in thrust beyond the optimum level the penetration rate starts decreasing and causes the drill Bit to ‘stall’. Results of the study show that penetration rate increases with increase in the thrust level. After reaching the maximum, they start decreasing despite the increase of thrust. The main purpose of the study is to develop a general prediction model and to investigate the relationships between penetration rate during drilling and physical properties such as uniaxial compressive strength and abrasivity of sedimentary rocks. The results were evaluated using the multiple regression analysis taking into account the interaction effects of predictor variables.