I.I. Esat

Load analysis with varying mesh stiffness

Abstract The design of proper modification of tooth flanks or mismatch of a pair of gears relies on an accurate computation of the loading and stressing distribution over the gearing surfaces at any phase of mesh. The existing methods, however, are either too simplified or too computationally expensive. This paper introduces a new approach to analysis of the loading and stressing distribution for spur and helical gears accounting for the varying meshing stiffness, geometric modification and elastic deflection of the engaged gears. Combining a discretized gear model with finite element analysis (FEA) it has both good computational accuracy and efficiency.

A theory of complete force and moment balancing of planer linkage mechanisms

Abstract Tepper and Lowen have shown that complete force balancing of planar linkage is possible using simple counterweights provided that from every point on the linkage there exists a contour to the ground by way of revolute joints only. This paper shows that if a linkage can be fully force balanced using the criterion of Tepper and Lowen, then it can be fully force and moment balanced using geared counter-inertias. Complete mathematical proof of this theory is given.

Gear condition monitoring by a new application of the Kolmogorov—Smirnov test

Abstract This paper introduces a new technique for the vibration condition monitoring of a set of spur gears. This technique, the Kolmogorov—Smirnov (KS) test, is based on a statistical comparison of two vibration signatures, which tests the ‘null hypotheses that the cumulative density function (CDF) of a target distribution is statistically similar to the CDF of a reference distribution’. In practice, the KS test is a time-domain signal processing technique that compares two signals and returns the likelihood that the two signals are statistically similar (i.e. have the same probability distribution function). Consequently, by comparing a given vibration signature with a number of template signatures for known gear conditions, it is possible to state which is the most likely condition of the gear under analysis. It must be emphasized that this is not a moment technique as it uses the whole CDF instead of sections of the CDF. In this work, the KS test is applied to the specific problem of direct spur gear condition monitoring. It is shown that this test not only successfully identifies the condition of the gear under analysis (brand new, normal, faulty and worn out), but also gives an indication of the advancement of the crack. Furthermore, this technique identifies cracks that are not identified by popular methods based on the statistical moment and/or time-frequency (TF) analysis and the vibration signature. This shows that, despite its simplicity, the KS test is an extremely powerful method that effectively classifies different vibration signatures, allowing for its safe use as another condition monitoring technique.

Real-Coded Quantum Inspired Evolution Algorithm Applied to Engineering Optimization Problems

A novel evolutionary algorithm based on quantum computing concepts is applied to engineering optimization problems. Quantum computing mimics behavior of atoms in processing information. Quantum inspired algorithm is a concept, which employs certain elements of quantum computing to use in a wider class of search and optimization problems. The main parts of a quantum-inspired algorithm are the qubits (quantum equivalent of bits) and the quantum gates. Qubits hold the information in a superposition of all the states, while the quantum gates evolve the qubit to achieve the desired objective, which is, in optimization the maximum or the minimum. The paper addresses the ability of the Quantum-Inspired Evolution Algorithm (QIEA) to solve practical engineering optimization problems. QIEA, which is developed by authors, is based on their previous work and it is extended to acquire the floating-point representation of the information. The RQIEA is tested on a four benchmark engineering optimization problems. The results showed that the new algorithm has the ability to compete and even overcome other well known methods.

Numerical and experimental evaluation of SIF for threaded connectors

Abstract This paper presents a methodology for fatigue crack growth analysis in tubular threaded connectors. A solution for stress intensity factor for semi-elliptical surface cracks emanating from a thread root in a screw connector is also discussed in the paper. The solution is based on a mixed approach incorporating weight function and finite element methods. The weight functions used are the universal functions for cracks in mode I and these are linked with a thread through-thickness stress distribution obtained from finite element analysis to produce a stress intensity factor for a crack at the critical tooth of a thread. The resulting crack growth data are then validated experimentally.

A hybrid model for analysis of complex stress distribution in threaded connectors

Abstract This paper describes a hybrid modelling technique for the analysis of load and stress distribution in screw threaded connectors, subjected to axial as well as bending loads. The technique is based on combining a linear spring model with a finite element sub-model. Bending loads are dealt with by the addition of a rotational degree of freedom in the spring model and the use of non-linear Fourier loading in the finite element axisymmetric sub-model. The hybrid model is then validated against the results obtained from a full axisymmetric finite element model, a full three-dimensional finite element model with helix angle represented and a published photo-elastic model.

Surface alignment based on the moment of inertia and improved least-squares methods

Abstract As part of dimensional inspection and error analysis of components it is usually required to place the component in a fixture where its position can be related to its computer aided design (CAD) nominal coordinate axis and the coordinate frame of the measuring system. The fixturing can be expensive and does not completely eliminate the mathematical matching needed between measured and nominal surfaces. Least-squares minimization is one of the most common methods employed in achieving the required alignment. This method, however, works only if the misalignment between two data sets is very small. Furthermore, there is no measure to establish whether this method is likely to converge or not before performing the actual iteration. The requirement for a small angle implies that this method is only suitable if fixturing is also used. The other technique used in obtaining alignment is by consideration of the mass properties of surfaces. This method is more effective and works irrespective of the degree of alignment. The problem with the mass property approach is that its accuracy diminishes when the error is small. This paper compares the two methods and demonstrates that both the r. m.s. minimization and the mass property methods can be expressed as eigenvalue problems, and both approaches produce identical eigenvectors despite having different eigenvalues (error measurements). A method is proposed to determine whether convergence is expected in the least-squares minimization at the first step of iteration. The proposed method may be used for accelerating the convergence operation.

Quantum-Inspired Evolution Algorithm: Experimental Analysis

Quantum computing mimics behaviour of atoms in processing information. Unfortunately due to restrictive rules of processing imposed by quantum behaviour only few successful algorithms have been developed in quantum computing. Quantum inspired algorithm is a concept, which employs certain elements of quantum computing to use in a wider class of search and optimisation problems. The main parts of a quantum‐inspired algorithm are the qubits (quantum equivalent of bits) and the gates. Qubits hold the information in a superposition of all the states, while the quantum gates evolve the qubit to achieve the desired objective, which is, in optimization the maximum or the minimum. The paper addresses the ability of the Quantum‐Inspired Evolution Algorithm (QIEA) to solve practical engineering problems. QIEA, which is developed by authors, is based on their previous work and it is improved to test a series of unitary gates. A set of experiments were carried out to investigate the performance of QIEA as for speed, accuracy, robustness, simplicity, generality, and innovation. To assess effectiveness of a new algorithm, there are a set of guidelines proposed by [1]. Based on these guidelines, the paper selected three test functions to carry out a benchmark study. The paper also presents a comparative study between QIEA and classical Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) techniques in order to assess the proposed QIEA.

Finite element modelling of anisotropic elastic-viscoplastic behavior of metals

Abstract An implementation of the unified theory of visco-plasticity of Bodner in a three-dimensional finite element program for the analysis of anisotropic inelastic behaviour of selected metals is presented in this paper. A derivation of an effective hardening parameter for the anisotropic (directional) deformation state is also given in this paper using some basic assumptions introduced by Bodner. The effect of the imposed strain rate on the level of the stress–strain curve is also investigated. A comparison of the results of the present finite element model with some published theoretical and experimental results for pure titanium and 2024-T4 aluminium alloy is also made.

Bioinspired Psi intelligent control for autonomous vehicles

The term Psi denotes anomalous processes of information or energy transfer that are currently unexplained in terms of known physical or biological mechanisms [1]. A variant of Psi is precognition which relates to an event or state not yet experienced. Although Psi phenomenon is unexplained, it is an inspiration for the study in the present work. The representation of prediction of future events for motion control is explained in a framework inspired by Psi precognition. In the current research, motion control of an autonomous vehicle is of interest where the future state of the vehicle in a dynamic environment is predicted using a multi-agent/robot or a swarm configuration approach. This research is aimed to address a problem where an agent in a multi-agent/robot or a swarm configuration can inform the vehicle under investigation about the changes in the dynamic environment before the vehicle itself can experience or sense an event. The corresponding parameters and constraints to solve such problem are discussed. A generalized approach inspired by Psi precognition is proposed and the effect of this technique in the system response is studied.