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The paper aims to introduce an efficient contact detection algorithm for smooth convex particles.

The contact points of adjacent particles are defined according to the common‐normal concept. The problem of contact detection is formulated as 2D unconstrained optimization problem that is solved by a combination of Newton's method and a Levenberg‐Marquardt method.

The contact detection algorithm is efficient in terms of the number of iterations required to reach a high accuracy. In the case of non‐penetrating particles, a penetration can be ruled out in the course of the iterative solution before convergence is reached.

The algorithm is only applicable to smooth convex particles, where a bijective relation between the surface points and the surface normals exists.

By a new kind of formulation, the problem of contact detection between 3D particles can be reduced to a 2D unconstrained optimization problem. This formulation enables fast contact exclusions in the case of non‐penetrating particles.

This paper aims to present the dielectrophoresis (DEP) force (FDEP), defined as microelectrofluidics mechanism capabilities in performing selective detection and rapid manipulation of blood components such as red blood cells (RBC) and platelets. The purpose of this investigation is to understand FDEP correlation to the variation of dynamic dielectric properties of cells under an applied voltage bias.

In this paper, tapered design DEP microelectrodes are used and explained. To perform the characterization and optimization by analysing the DEP polarization factor, the change in dynamic dielectric properties of blood components are observed according to the crossover frequency (f_xo) and adjustment frequency (f_adj) variation for selective detection and rapid manipulation.

Experimental observation of dynamic dielectric properties change shows clear correlation to DEP polarization factor when performing selective detection and rapid manipulation. These tapered DEP microelectrodes demonstrate an in situ DEP patterning efficiency more than 95%.

The capabilities of tapered DEP microelectrode devices are introduced in this paper. However, they are not yet mature in medical research studies for various purposes such as identifying cells and bio-molecules for detection, isolation and manipulation application. This is because of biological property variations that require further DEP characterization and optimization.

The introduction of microelectrofluidics using DEP microelectrodes operate by selective detecting and rapid manipulating via lateral and vertical forces. This can be implemented on precision health-care development for lab-on-a-chip application in microfluidic diagnostic and prognostic devices.

This study introduces a new concept to understand the dynamic dielectric properties change. This is useful for rapid, label free and precise methods to conduct selective detection and rapid manipulation of mixtures of RBC and platelets. Further, potential applications that can be considered are for protein, toxin, cancer cell and bacteria detections and manipulation. Implementation of tapered DEP microelectrodes can be used based on the understanding of dynamic dielectric properties of polarization factor analysis.

The purpose of this paper is to help understand the mathematical model of inductive sensor and to improve the sensitivity of nonferrous metal particle detection.

The expression of impedance change is established, while the distribution regularities of the magnetic field inside and outside the metal particle are obtained based on the Maxwell equations in complex forms, the analytic solution of the electromagnetic field is obtained and the experiment validation is implemented.

The expression of impedance and the analytic solution of the electromagnetic field are obtained. It is shown that the inductance change is more obvious than resistance change for the iron particles, but for copper particles, resistance change is more obvious and the resistance change increases with the frequency. In this work, copper particles (size: 20 µm) are detected at 2 MHz excitation frequency, and the imaginary part of impedance changes without adding any device, which is provided with a prominent guideline for detection of nonferrous particles of size less than 100 µm.

The expression of impedance change is established, the analytic solution of the electromagnetic field is obtained and copper particles (size: 20 µm) are detected at 2 MHz excitation frequency, and the imaginary part of impedance change without adding any device, which is provided with a prominent guideline for detection of nonferrous particles of size less than 100 µm.

This paper aims to present a methodology for the detection and categorisation of metal powder particles that are partially attached to additively manufactured lattice structures. It proposes a software algorithm to process micro computed tomography (µCT) image data, thereby providing a systematic and formal basis for the design and certification of powder bed fusion lattice structures, as is required for the certification of medical implants.

This paper details the design and development of a software algorithm for the analysis of µCT image data. The algorithm was designed to allow statistical probability of results based on key independent variables. Three data sets with a single unique parameter were input through the algorithm to allow for characterisation and analysis of like data sets.

This paper demonstrates the application of the proposed algorithm with three data sets, presenting a detailed visual rendering derived from the input image data, with the partially attached particles highlighted. Histograms for various geometric attributes are output, and a continuous trend between the three different data sets is highlighted based on the single unique parameter.

This paper presents a novel methodology for non-destructive algorithmic detection and categorisation of partially attached metal powder particles, of which no formal methods exist. This material is available to download as a part of a provided GitHub repository.

When simulating the behaviour of granular assemblies and multi‐body systems using a discrete element analysis, the shape representation of the bodies and the contact detection algorithm greatly influence the flexibility, accuracy and efficiency of the simulation. Several geometrical shape descriptors of two and three dimensional arbitrary rigid bodies are reviewed and a flexible 3‐D descriptor introduced. The aim is to identify appropriate shape descriptors which allow a variety of types of bodies to be investigated while ensuring accurate and efficient detection of inter‐particle contacts. Polygons/polyhedrons, and continuous and discrete function representations are examined. The investigation favours discrete representations due to their efficiency and flexibility, but illustrates the elegance and efficiency of using a continuous function representation, e.g. a superquadric, to generate the discrete representation and simplify the contact detection process.

Particle velocity is a critical factor that can affect the deposition quality in manufacturing processes involving the use of a laser source and a powder‐particle delivery nozzle. The purpose of this paper is to propose a method to detect the speed and trajectory of particles during a laser deposition process.

A low‐power laser light sheet technique is used to illuminate particles emerging from a custom designed powder delivery nozzle. Light scattered by the particles is detected by a high‐speed camera. Image processing on the acquired images was performed using both edge detection and Hough transform algorithms.

The experimental data were analyzed and used to estimate particle velocity, trajectory and the velocity profile at the nozzle exit. The results have demonstrated that the particle trajectory remains linear between the nozzle exit and the deposition plate and that the particle velocity can be considered a constant.

The use of low‐power laser light sheet illumination facilitates the detection of isolated particle streaks even in high‐powder flow rate condition. Identification of particle streaks in three subsequent images ensures that particle velocity vectors are in the plane of illumination, and also offers the potential to evaluate in a single measurement both velocity and particle size based on the observed scattered characteristics. The method provides a useful simple tool to investigate particle dynamics in a rapid prototyping application as well as other research fields involving the use of powder delivery nozzles.

The purpose of this study was to perform a complete experimental assessment of a family of oil ferrous wear debris sensor is performed. The family comprised the original sensor and its re-engineered evolution, which is capable of detecting both amount and size of wear debris particles trapped by the sensor and some predefined oil condition properties.

In this work, the first step was to perform a design of experiments for the sensor validation. A specially defined test rig was implemented, and different ferrous wear debris was collected. For each sensor, two different tests were performed. The first test was called a “void test”, where quantified amounts of debris were collided with the sensor without oil. The second one was a dynamic test, where the sensor was installed in the test rig and different amounts of wear debris were added at a constant rate. In addition, specific tests related with oil properties detection were studied.

The results show excellent correlation of the sensor output signal with the amount of wear debris and a satisfactory detection of debris size in all ranges. Also, the dynamic test presented adequate representativeness, and sensors performed well in this scenario.

This paper shows the practical implementation of this type of sensor and the usual detection range and rate of detection for different debris size and quantities.

This work has a great utility for maintenance managers and equipment designers to fully understand the potential of this type of sensor and its suitability for the application required.

The purpose of this paper is to present a simple non‐symmetric shape, the poly‐ellipsoid, to describe particles in discrete element simulations that incur a computational cost similar to ellipsoidal particles.

Particle shapes are derived from joining octants of eight ellipsoids, each having different aspect ratios, across their respective principal planes to produce a compound surface that is continuous in both surface coordinate and normal direction. Because each octant of the poly‐ellipsoid is described as an ellipsoid, the mathematical representation of the particle shape can be in the form of either an implicit function or as parametric equations.

The particle surface is defined by six parameters (vs the 24 parameters required to define the eight component ellipsoids) owing to dependencies among parameters that must be imposed to create continuous intersections. Despite the complexity of the particle shapes, the particle mass, centroid and moment of inertia tensor can all be computed in closed form.

The particle can be implemented in any contact algorithm designed for ellipsoids with minor modifications to determine in which pair of octants the potential contact occurs.

The poly‐ellipsoid particle is a computational device to represent non‐spherical particles in DEM models.

It is a challenging task to analysis oxide wear particles when they are stuck together with other types of wear particles in complex ferrography images. Hence, this paper aims to propose a method of ferrography image segmentation to analysis oxide wear debris in complex ferrography images.

First, ferrography images are segmented with watershed transform. Then, two region merging rules are proposed to improve the initial segmentation results. Finally, the features of each particle are extracted to detect and assess the oxide wear particles.

The results show that the proposed method outperforms other methods of ferrography image segmentation, and the overlapping wear particles in complex ferrography images can be well separated. Moreover, the features of each separated wear particles can be easily extracted to analysis the oxide wear particles.

The proposed method provides a useful approach for the automatic detection and assessment of oxide wear particles in complex ferrography images.

The colours, edges and position information of wear debris are considered in the proposed method to improve the segmentation result. Moreover, the proposed method can not only detect oxide wear particles in ferrography images but also evaluate oxide wear severity in ferrography images.

High-altitude ice crystals (HAICs) are causing one in-flight event or more per month for commercial aircraft. The effects include preventing air data probes (pitot pressure and total air temperature in particular) from functioning correctly and causing engines to roll back and shut down. The purpose of this study is to describe the process used by the National Research Council Canada (NRC) to develop and test a particle detection probe (PDP). The probe mounts on the fuselage of aircraft to sense and quantify the ice crystals in the environment.

The probe was demonstrated on the NRC Convair and Airbus A340 research aircraft as part of the European Union HAIC programme. The probe was ruggedised, adapted for easy installation in standard aircraft fittings and tested in a variety of conditions for longevity and endurance.

Efforts to achieve the safety requirements for flight on aircraft are discussed. The challenges, surprises and opportunities for testing on which the development group is capitalised are also presented.

It was demonstrated that the detectors gave signals proportional to the ice crystal content of clouds, and results demonstrating the functionality of the probe are presented.

This paper describes the multi-year process of developing the NRC PDP from a test cell sensor for detecting engine exhaust contaminants on an aircraft ice crystal detection probe. The work included over 20 flight tests on NRC aircraft and the Airbus HAIC test programme.