Ghulam Ishaq Khan Institute of Engineering Sciences and Technology

About: Ghulam Ishaq Khan Institute of Engineering Sciences and Technology is a education organization based out in Topi, Pakistan. It is known for research contribution in the topics: Thin film & Quantum efficiency. The organization has 618 authors who have published 940 publications receiving 10674 citations.

Determination of optimized non-gasketed flange geometry for safe stress and no leak condition: Finite-element analysis approach:

Abstract: Performance of a flange joint is characterized mainly due to its ‘strength’ and ‘sealing capability’. Available design rules for flange joints are mainly concerned with the strength of the flanges and do not sufficiently consider for their sealing. Being continuous problems faced by the gasketed flange joints for strength and sealing based on concluded ‘dynamic mode of load’, non-gasketed joints are recommended as an alternative. The current paper presents three-dimensional non-linear finite-element analysis of non-gasketed flange joint with different flange surface profiles (positive taper, negative taper, and zero taper angles) to find out the most optimal flange geometry for safe stress and no-leak conditions under bolt up and internal pressure loading. Based on the results, optimum flange geometry is selected and further analysed under proof test pressure. A ‘static mode of load’ in positive taper angle flange is concluded, ensuring proper joint sealing, and strength.

Wear Behaviour of HVOF Sprayed WC-Cobalt Coatings

Abstract: Tungsten carbide cobalt coating has been extensively used for cutting and mining tools, aerospace, automotive and other wear resistance applications. These coatings not only have superior mechanical properties like high hardness, toughness and compressive strength but have also excellent controllable tribological properties. In this paper a comparison of wear properties and structural phases has been presented to consider for tribological applications. It is found that nanocrystalline duplex coatings have shown much superior properties as compared to the microcrystalline coatings. Evidence of clusters of WC particles was found in microcrystalline coating as compared to homogeneous dense coating structure observed in the nanocrystalline coating. These results are discussed to assess their suitability for super hard wear resistance applications.

Hamilton Connectivity of Convex Polytopes with Applications to Their Detour Index

Abstract: A connected graph is called Hamilton-connected if there exists a Hamiltonian path between any pair of its vertices. Determining whether a graph is Hamilton-connected is an NP-complete problem. Hamiltonian and Hamilton-connected graphs have diverse applications in computer science and electrical engineering. The detour index of a graph is defined to be the sum of lengths of detours between all the unordered pairs of vertices. The detour index has diverse applications in chemistry. Computing the detour index for a graph is also an NP-complete problem. In this paper, we study the Hamilton-connectivity of convex polytopes. We construct three infinite families of convex polytopes and show that they are Hamilton-connected. An infinite family of non-Hamilton-connected convex polytopes is also constructed, which, in turn, shows that not all convex polytopes are Hamilton-connected. By using Hamilton connectivity of these families of graphs, we compute exact analytical formulas of their detour index.

Analyzing Image Focus using Deep Neural Network for 3D Shape Recovery

Abstract: Shape from Focus is one of the passive optical techniques for 3D shape recovery, from a set of differently focused 2D images. It utilizes the focus information present in the image to find 3D shape of the object in consideration. First, a stack of images is obtained by moving the object along the optical axis. Then a Focus Measure operator is applied leading to the focus curves which are then maximized to obtain the best focused positions. Conventionally, the previously proposed focus measures are computationally expensive since they have to process huge amounts of data. In this paper, Deep Neural Networks (DNN) have been employed to measure the amount of focus in the image stack. The results are compared with commonly used FMs by employing RMSE, Correlation and $Q$ index. The comparison establishes that the proposed method is not only only efficient but more accurate.