Orissa Engineering College, Bhubaneswar is a First private engineering college established in Odisha. It was set up in 1986. Orissa Engineering College is the Odisha Electronics Control Library result of the vision of Shri Kamini Kanta Patnaik. It was inaugurated by the then chief minister of Orissa Biju Patnaik. The college has alumni all across the world. The institute provides courses in B.Tech, M.Tech and MBA. The college is affiliated to Biju Patnaik University of Technology and has been approved by AICTE. The college has received NBA for three departments; Electronics and Telecommunication Engineering, Mechanical Engineering and Electrical Engineering. Dushyant Mohanty, Sanjam Patnaik, Dilip Tripathy and Sidhartha Dash, Subhakanta Mishra, Binaya Parhy are some of the notable alumni who have made significant contribution to the corporate world..
Business organizations are highly dependent on culture and communication that define the essence of their existence. Researches indicate that while some organizations have successfully placed themselves in the global market places, there are still others who have deteriorated because of weak communication or insufficient assimilation with other organizations of different cultures. Recent studies argued that the accomplishment of the global business depends on successful cross cultural communication. This paper studies the role of cross cultural communication and the barriers that obstruct cross cultural effectiveness in business. The paper especially focuses on the cultural stereotype and ethnocentrism as the most powerful barriers in cross cultural communication and proposes solutions towards the relevance of soft skills in overcoming the barriers and ensuring success in business in unprecedented ways and their development in cross cultural management.
This paper has emphasized several sounds for Bird Species Recognition based on their vocalization. Although various techniques have been designed with good equipment for identifying different birds’ sounds, still it has limitations to classify or recognize different birds’ sounds at dissimilar moments of traveling. Thus, this paper presents the model for recognizing the bird’s sound in several situations using Spiking Neural Network (SNN). In this model, Permutation Pair Frequency Matrix Coefficients are considered for the features representing the bird sound frames, after the application of pre-processing method on the moment of recordings. These features are applied as input to the three-layered SNN and subsequently to the hidden layer, which performs the work of spiking if there is any change in the feature. The spikes activate any one of the neuron’s outputs, that produces a higher probability class. Further, the SNN along with the Distance Fuzzy Co-clustering algorithm technique used on these bird sounds to achieve optimum results. The performance of this model achieved more than 94
Electro-discharge machining (EDM) is a common non-conventional machining technique for cutting a variety of conducting materials. In this study, the machining performances of Ti-6Al-4V, SS316, and WC work pieces were examined using EDM while considering various thermophysical parameters. Surface roughness, material removal rate, tool wear rate, radial overcut, surface fracture density, and surface hardness have all been investigated using different input parameters such as discharge current, pulse on time, and pulse off time. There is a wide range of performance characteristics among the materials used in the workpieces. In comparison to Ti-6Al-4V and Tungsten Carbide materials, the rate of rise in MRR, TWR, and radial overcut in SS316 is quite high as current increases.
Electro-discharge machining (EDM) is a useful non-conventional machining operation frequently applied to make different complex geometries in any conducting material. The objectives of the present paper are to study the effect of a variation of thermo-physical properties (TPP) of three different tool materials on EDM performances. The different performances compared in this paper are: material removal rate (MRR), tool-wear rate (TWR), surface roughness (SR), radial overcut (ROC), surface-crack density (SCD) and surface hardness. Two of the most widely used work piece materials, such as corrosion-resistant austenitic stainless steel (SS316) and high strength corrosion-resistance titanium alloy (Ti-6Al-4V), are machined with the help of three different tools by varying input current and maintaining constant pulse-on time, pulse-off time and flushing pressure. Microstructural studies of the tool tip surface after machining have also been carried out. It is found that among these three tool materials, the copper tool showed the best machining performance with respect to material removal rate, radial overcut, surface finish and surface-crack density. This work will help industry personnel to choose a suitable tool for a specific work piece material.
Metal matrix composites (MMC) are suitable replacements not only for steels but also for aluminum alloys in various automotive systems and components. There are many ways to get lightweight materials without compromising their strength and safety requirements. Tests on dry sliding or unlubricated pin-on-disc tests can be performed to examine the wear characteristics of metal matrix composites based on aluminum. In the present study, we studied the behavior of wear resistance during sliding of metal matrix aluminum composites (MMC) at sliding speeds of 1.5 m/s and loads 10, 20, 30, and 40 N in a normal environment, and experiments have been conducted using a pin-on-disc tribometer (Make: DUCOM tribometer). The results show that the wear rates of the composites are lower than those of the matrix alloy and decrease further with increasing SiC content. Further percentage of SiC and applied load are considered as input to the fuzzy system and wear rate as the output. The output from the developed fuzzy model is compared with experimental results, and the deviation is found to be within 2.5 percent.
