L. Di Angelo

Bilateral symmetry estimation of human face

This paper proposes a new method for the identification of the symmetry plane of the human face, working from 3D high-density scanned data. The method being proposed is an original variant of a typical mirroring and registration method. This method is validated by analysing some specifically designed test cases. The obtained results show that the method is quite insensitive to local asymmetries, whether they be near or far from the symmetry plane, and is also repeatable and slightly conditioned by the acquisition process.

Automatic evaluation of form errors in high-density acquired surfaces

In this paper the authors present an original methodology aiming at the automation of the geometric inspection, starting from a high-density acquired surface. The concept of intrinsic nominal reference is herein introduced in order to evaluate geometric errors. Starting from these concepts, a new specification language, which is based on recognisable geometric entities, is defined. This work also proposes some surface differential properties, such as the intrinsic nominal references, from which new categories of form errors can be introduced. Well-defined rules are then necessary for the unambiguous identification of these intrinsic nominal references. These rules are an integral part of the tolerance specification. This new approach requires that a recognition process be performed on the acquired model so as to automatically identify the already-mentioned intrinsic nominal references. The assessable errors refer to recognisable geometric entities and their evaluation leaves the nominal reference specification aside since they can be intrinsically associated with a recognised geometric shape. Tolerance specification is defined based on the error categories which can be automatically evaluated and which are an integral part of the specification language.

Early-Injection and Time-Resolved Evolution of a Spray for GDI Engines

An extensive experimental study on sprays from an injector for gasoline direct injection (GDI) engines has been performed. Spatial and temporal evolution measurements of a large cone-angle jet, emerging from a high pressure swirled injector, have been carried out in an optically accessible vessel. The spray has been lightened, both along the spray axis and in cross sections perpendicular to it, by a 532 nm Nd-YAG pulsed laser sheet, 80 mm thickness and 12 ns duration. The scattered light has been collected at 90° with respect to the sheet direction by a digital CCD camera with a frame grabber synchronized with the injection command and the laser pulse. A digital delay system has provided a fine temporal shift (up to microseconds range) of the images acquisition with respect to the start of the injection (SOI). Finally, a digital image processing system has provided analysing the images collected by the CCD camera. The emerging spray has been acquired with three spatial scales, providing both the global and local spray behaviour, and with a detailed temporal resolution to characterize the early stage of the jet formation. The initial phase of the spray is characterized by a strong axial component of the velocity with respect to the radial one, resulting in a pre-spray or slug phase. It produces a cylindrical shape of the jet with the characteristic mushroom shape. Large droplets with high momentum are produced, travelling downstream in advance to the main spray. At later time the radial velocity component controls the process and it gives up to the classical hollow-cone shape with a strong interaction with the gas in the vessel. The images give evidence in time of the collapse of the hollow-cone structure hence resulting in a full cone spray. This behaviour is confirmed by the cross section measurements through the spray, carried out in the range 10–40 mm from the nozzle tip. These measurements make evidence of the refilling of the cone with the presence of strong vortexes on the boundary of the jet. The effects of the fuel injection pressure, injection duration and air-flow field interaction on the structure and evolution of the spray have been studied in details. The digital image processing system also has allowed to reconstruct the spray profile and to determine a refilling index.Copyright

A new method for the automatic identification of the dimensional features of vertebrae

In this paper a new automatic approach to determine the accurate measure of human vertebrae is proposed. The aim is to speed up the measurement process and to reduce the uncertainties that typically affect the measurement carried out by traditional approaches. The proposed method uses a 3D model of the vertebra obtained from CT-scans or 3D scanning, from which some characteristic dimensions are detected. For this purpose, specific rules to identify morphological features, from which to detect dimensional features unambiguously and accurately, are put forward and implemented in original software. The automatic method which is here proposed is verified by analysing real vertebrae and is then compared with the state-of-the-art methods for vertebra measurement.This paper proposes a new approach to determine the measure of human vertebrae.Typical approaches perform measurements with lack of repeatability and reproducibility.The proposed method is based on morphological features recognition from 3D high point density model of the vertebrae.The paper proposes unambiguous rules to identify geometric references and the associated dimensions.Compared to typical approaches, the proposed method proved to be more repeatable and reproducible. In this paper a new automatic approach to determine the accurate measure of human vertebrae is proposed. The aim is to speed up the measurement process and to reduce the uncertainties that typically affect the measurement carried out by traditional approaches. The proposed method uses a 3D model of the vertebra obtained from CT-scans or 3D scanning, from which some characteristic dimensions are detected. For this purpose, specific rules to identify morphological features, from which to detect dimensional features unambiguously and accurately, are put forward and implemented in original software. The automatic method which is here proposed is verified by analysing real vertebrae and is then compared with the state-of-the-art methods for vertebra measurement.

A new computational method for automatic dental measurement

This paper proposes a new automatic approach to determine the accurate measure of human teeth. The aim of the proposed computer based method is to reduce inaccuracy of measurement with respect to traditional approaches. Starting from a 3D model of the teeth which is obtained from 3D scanning, the method algorithmically evaluates the most important dimensional features detectable in central incisors. For this purpose, specific rules are put forward and implemented in original software with a view to identifying repere points, from which to detect dimensional features both unambiguously and accurately. The automatic method which is proposed here is verified by means of the analysis of real teeth and is then compared with the current state-of-the-art methods for teeth measurement.

The RGM data structure: a nominal interpretation of an acquired high point density model for automatic tolerance inspection

In a previous paper (Di Angelo, L., Di Stefano, P. and Morabito, A., 2011. Automatic evaluation of form errors in high-density acquired surfaces. International Journal of Production Research, 49 (7), 2061–2082) we proposed an original methodology for the automation of the geometric inspection, starting from an acquired high-density surface. That approach performed a recognition process on the acquired data aiming at the identification of some intrinsic nominal references. An intrinsic nominal reference was detected when a geometric property was recognised to be common to a set of adjacent points in the 3D data set representing the acquired object. The recognition of these properties was carried out based on some rules. Starting from these concepts, a new specification language was defined, which is based on recognisable geometric entities. This paper expands the category of intrinsic nominal references to include new mutual intrinsic orientation, location and dimensional properties pertaining to 3D features. This approach involves the automatic construction of a geometric reference model for a scanned workpiece, called recognised geometric model (RGM). The domain of the representable entities within the RGM strictly depends on the rules used for the recognition of the intrinsic properties. In particular, this paper focuses on the rules for the recognition of the orientation and location properties between non-ideal features. When using the RGM, tolerances are specified according to the set of available and recognisable intrinsic nominal references. Based on the geometric product specification, the RGM data structure can be queried to capture some quantitative information concerning special intrinsic geometric parameters and/or non-idealities.

An evolutionary geometric primitive for automatic design synthesis of functional shapes: The case of airfoils

A novel self-adaptive geometric primitive for functional geometric shape synthesis is presented. This novel geometric primitive, for CAD use, is specifically designed to reproduce geometric shapes with functional requirements, such as the aerodynamic and hydrodynamic ones, once the functional parameters are furnished. It produces a typical CAD representation of a functional profile: a set of Bezier curves. The proposed primitive follows a generate-and-test approach and takes advantage of the use of a properly designed artificial neural network (BNN). It combines the properties of a geometric primitive and the capability to manage the engineering knowledge in a specific field of application. The proposed evolutionary primitive is applied to a real engineering application: the automatic synthesis of airfoils. Some examples are simulated in order to test the effectiveness of the proposed method. The results obtained by an original prototypal software are presented and critically discussed.

Neural network based geometric primitive for airfoil design

A geometric primitive for CAD implementation is presented (Bezier neural network, BNN). It is specifically designed to reproduce geometric shapes with functional requirements such as aerodynamic and hydrodynamic profiles. This primitive can be useful when a known and well defined map between functional requirements and geometric data does not exist, and it have to be deduced by a physical or numerical experimental analysis. BNN gives rise to a typical CAD representation, a Bezier curve, of a functional profile, once the functional parameters are supplied. In BNN the capability of neural network to approximate very complex and non-linear function has been combined with the capability of Bezier functions to describe geometry, in a unique neural network. In this work BNN is used in the representation of aerodynamic profiles starting to their typical functional parameters: lift and drag coefficients, Reynolds number and angle of attack. BNN is tested in reproducing the wing profile of the 4-digit NACA series. The output of BNN is compared with the results of a fluid-dynamic analysis performed by commercial software.

Product model for Dimensioning, Tolerancing and Inspection

This paper presents a new methodology whose goals are on the one hand the formulation of a tolerance specification that is consistent with the functional, technological and control needs and, on the other, the automatic control of tolerance. The key aspect of the methodology is the digital model of the product, referred to as GMT (Geometric Model of Tolerancing), which gives a complete, consistent and efficient description of its geometrical and dimensional properties with the aim of being able to specify, simulate, manufacture and inspect them. By means a real test case, the potentialities of a first implementation of the proposed methodology are critically discussed.

Segmentation of secondary features from high-density acquired surfaces

A new method for secondary features segmentation, performed in high-density acquired geometric models, is proposed. Four types of secondary features are considered: fillets, rounds, grooves and sharp edges. The method is based on an algorithm that analyzes the principal curvatures. The nodes, potentially attributable to a fillet of given geometry, are those with a certain value for maximum principal curvature. Since the deterministic application of this simple working principle shows several problems due to the uncertainties in the curvature estimation, a fuzzy approach is proposed. In order to segment the nodes of a tessellated model that pertain to the same secondary features, proper membership functions are evaluated as function of some parameters, which affect the quality of the curvature estimation. A region growing algorithm connects the nodes pertaining to the same secondary feature. The method is applied and verified for some test cases.