Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Vijay Kumar, S.Subject: search.filters.subject.Temperature

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    Effect of thermal response on physical properties during drilling operations-A case study
    (Elsevier Ltd, 2018) Vijay Kumar, S.; Murthy, Ch.S.N.; Kunar, B.M.
    The temperature induced or generated during deep hole drilling operations is due to the heat generated between the interface of work piece surface and the tool surface. Several research study have been conducted to predict the temperature involved while drilling process as a predominant functions of many parameters like feed rate, torque, depth of cut etc. Similarly many experimental procedures have been conducted by several researchers to measure temperature directly by using thermocouples, infrared measurement, pyrometer, and thermisters etc. There is no precise experimental method is available to measure analytical value of energy, power, heat flux etc, while drilling process. The temperature rigma depends on material compositions and physical properties. This paper presents the influence of temperature on physical properties of some study samples during drilling operations. © 2017 Elsevier Ltd.
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    Temperature Measurement During Rotary Drilling of Rocks - A Statistical Approach
    (Springer Nature, 2020) Vijay Kumar, S.; Kunar, B.M.; Murthy, C.S.N.
    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. © 2020, Springer Nature Switzerland AG.