Automated Posture Positioning for High Precision 3D Scanning of a Freeform Design using Bayesian Optimization

Three-dimensional scanning is widely used for the dimension measurements of physical objects with freeform designs. The output point cloud is flexible enough to provide a detailed geometric description for these objects. However, geometric accuracy and precision are still debatable for this scanning process. Uncertainties are ubiquitous in geometric measurement due to many physical factors. One potential factor is the object’s posture in the scanning region. The posture of target positioning on the scanning platform could influence the normal of the scanning points, which could further affect the measurement variances. This paper first investigates the geometric and spatial factors that could potentially influence scanning variance. This functional relationship is modeled as a Bayesian extreme learning model, which is later utilized to find the object’s optimal posture for variance reduction. A Bayesian optimization approach is proposed to solve this minimization problem. Case studies are presented to validate the proposed methodology

On The Use Of Students For Developing Engineering Laboratories

This paper describes a unique and innovative approach that solved the dual problem of starting up a new engineering instructional laboratory in a timely manner, and for teaching engineering students advanced skills in Automatic Data Collection. Students enrolled in a special pilot course were used to develop and startup an Automatic Data Collection laboratory. These students were assigned individual Automatic Data Collection technologies of interest and given total responsibility for the successful startup of the laboratory. The organization and structure of the course modeled the typical team oriented project development efforts in industry. Feedback from students showed the course to be better than a typical lecture/laboratory/demonstration type course in the following ways: 1) students believed they had greater amount of contact with equipment; 2) their experience on the project was more realistic than more traditional courses; 3) they believed they gained a more thorough understanding of the technology under study; and 4) they believed they improved their professional skills making them more marketable to potential employers. With respect to the laboratory itself, startup time was reduced from an estimated 18 months to 14 weeks with the help of the student teams. 1995 American Society for Engineering Educatio

Minimax Registration for Point Cloud Alignment

The alignment, or rigid registration, of three-dimensional (3D) point clouds plays an important role in many applications, such as robotics and computer vision. Recently, with the improvement in high precision and automated 3D scanners, the registration algorithm has become critical in a manufacturing setting for tolerance analysis, quality inspection, or reverse engineering purposes. Most of the currently developed registration algorithms focus on aligning the point clouds by minimizing the average squared deviations. However, in manufacturing practices, especially those involving the assembly of multiple parts, an envelope principle is widely used, which is based on minimax criteria. Our present work models the registration as a minimization problem of the maximum deviation between two point clouds, which can be recast as a second-order cone program. Variants for both pairwise and multiple point clouds registrations are discussed. We compared the performance of the proposed algorithm with other well-known registration algorithms, such as iterative closest point and partial Procrustes registration, on a variety of simulation studies and scanned data. Case studies in both additive manufacturing and reverse engineering applications are presented to demonstrate the usage of the proposed method

Colombia & the New Global Economy: Implications of Tratado de Libre Comercio for Colombian Industry, Engineers and Engineering Educators

The landscape of the world economy has changed significantly over the last twenty five years. The inter-connectedness of national economies, the rapid ascent of the BRIC countries (Brazil, Russia, India, China) in the global engineering environment and the pro-active role of organizations such as the World Trade Organization, regional alliances such as the EU, and Mercosur are factors that have synergized this movement towards a new order. The completion of the Tratado de Libre Comercio (TLC) agreement is a major milestone for the Colombian economy. These developments have serious and opportunistic implications for organizations, engineers, and engineering educators. We focus here on the drivers and consequences for engineering practitioners and educators. Corporate strategies, along with the need for engineering curriculum reform to ensure that Colombian engineers will effectively compete in the global marketplace, are detailed

A Survey of Smart Manufacturing for High-Mix Low-Volume Production in Defense and Aerospace Industries

Defense and aerospace industries usually possess unique high-mix low-volume production characteristics. This uniqueness generally calls for prohibitive production costs and long production lead-time. One of the major trends in advanced, smart manufacturing is to be more responsive and better readiness while ensuring the same or higher production quality and lower cost. This study reviews the state-of-the-art manufacturing technologies to solve these issues and previews two levels of flexibility, i.e., system and process, that could potentially reduce the costs while increasing the production volume in such a scenario. The main contribution of the work includes an assessment of the current solutions for HMLV scenarios, especially within the defense of aerospace sectors, and a survey of the current and potential future practices focusing on smart production process planning and flexible assembly plan driven by emerging techniques

OPTIMAL PITCH MAP GENERATION FOR SCANNING PITCH DESIGN IN SELECTIVE SAMPLING

The reverse engineering process represents one of the best known methodologies for creating three-dimensional (3D) virtual models starting from physical ones. Even if in the last few years its usage has significantly increased, the remarkable involvement of the operator has until now represented a significant constraint for its growth. Having regard to the fact that this process, and in particular its first step (that is the acquisition phase), strongly depends on the operator's ability and expertise, this paper aims at proposing a strategy for automatically supporting an "optimal" acquisition phase. Moreover, the acquisition phase represents the only moment in which there is a direct contact between the virtual model and the physical model. For this reason, designing an "optimal" acquisition phase will provide as output an efficient set of morphological data, which will turn out to be extremely useful for the following reverse engineering passages (pre-processing, segmentation, fitting, …). This scenario drives the researcher to use a selectivesampling plan, whose grid dimensions are correlated with the complexity of the local surface region analyzed, instead of a constant one. As a consequence, this work proposes a complete operative strategy which, starting from a first raw preliminary acquisition, will provide a new selectivesampling plan during the acquisition phase, in order to allow a deeper and more efficient new scansion. The proposed solution does not require the creation of any intermediate model and relies exclusively on the analysis of the metrological performances of the 3D scanner device and of the morphological behaviour of the surface acquired

Attribute-level Neighbor Hierarchy Construction Using Evolved Pattern-based Knowledge Induction

Neighbor knowledge construction is the foundation for the development of cooperative query answering systems capable of searching for close match or approximate answers when exact match answers are not available. This paper presents a technique for developing neighbor hierarchies at the attribute level. The proposed technique is called the evolved Pattern-based Knowledge Induction (ePKI) technique and allows construction of neighbor hierarchies for nonunique attributes based upon confidences, popularities, and clustering correlations of inferential relationships among attribute values. The technique is applicable for both categorical and numerical (discrete and continuous) attribute values. Attribute value neighbor hierarchies generated by the ePKI technique allow a cooperative query answering system to search for approximate answers by relaxing each individual query condition separately. Consequently, users can search for approximate answers even when the exact match answers do not exist in the database (i.e., searching for existing similar parts as part of the implementation of the concepts of rapid prototyping). Several experiments were conducted to assess the performance of the ePKI in constructing attribute-level neighbor hierarchies. Results indicate that the ePKI technique produces accurate neighbor hierarchies when strong inferential relationships appear among data. © 2006 IEEE