Spectral-based mesh segmentation

In design and manufacturing, mesh segmentation is required for FACE construction in boundary representation (BRep), which in turn is central for featurebased design, machining, parametric CAD and reverse engineering, among others -- Although mesh segmentation is dictated by geometry and topology, this article focuses on the topological aspect (graph spectrum), as we consider that this tool has not been fully exploited -- We preprocess the mesh to obtain a edgelength homogeneous triangle set and its Graph Laplacian is calculated -- We then produce a monotonically increasing permutation of the Fiedler vector (2nd eigenvector of Graph Laplacian) for encoding the connectivity among part feature submeshes -- Within the mutated vector, discontinuities larger than a threshold (interactively set by a human) determine the partition of the original mesh -- We present tests of our method on large complex meshes, which show results which mostly adjust to BRep FACE partition -- The achieved segmentations properly locate most manufacturing features, although it requires human interaction to avoid over segmentation -- Future work includes an iterative application of this algorithm to progressively sever features of the mesh left from previous submesh removal

Dynamic Customization and Validation of Product Data Models Using Semantic Web Tools

Part 7: Web, Semantics and Ontologies for PLMInternational audienceProduct Lifecycle Management (PLM) has always required robust solutions for representing product data models. Product data models enable information exchange across different organizations, actors, processes and stages in the product lifecycle. In this context, standardization of models plays a key role, since it ensures interoperability between the different systems that support information exchange. These standard models need to support diverse domain-specific requirements from the multitude of disciplines involved during a product’s lifecycle. Due to this diversity, challenges are to (1) develop multidisciplinary reusable models, (2) extend them to support new requirements over time (new products, new regulations, new materials, new processes) and (3) implement the resulting gigantic information models. ISO 10303, the reference standard for PLM-related data models provides two mechanisms that enable specialization of generic product data to address some of these challenges. In this paper we introduce the need for dynamic PLM-related information models, detail the existing ISO 10303 method and identify its limitations. We then present a methodology for enhancing that method using the Web Ontology Language (OWL) and ontologies for representing product data models and the SPARQL Inference Notation (SPIN), a new Semantic Web technology, for validating product data and overcoming OWL limitations

A data-driven optimization framework for routing mobile medical facilities

We study the delivery of mobile medical services and in particular, the optimization of the joint stop location selection and routing of the mobile vehicles over a repetitive schedule consisting of multiple days. Considering the problem from the perspective of a mobile service provider company, we aim to provide the most revenue to the company by bringing the services closer to potential customers. Each customer location is associated with a score, which can be fully or partially covered based on the proximity of the mobile facility during the planning horizon. The problem is a variant of the team orienteering problem with prizes coming from covered scores. In addition to maximizing total covered score, a secondary criterion involves minimizing total travel distance/cost. We propose a data-driven optimization approach for this problem in which data analyses feed a mathematical programming model. We utilize a year-long transaction data originating from the customer banking activities of a major bank in Turkey. We analyze this dataset to first determine the potential service and customer locations in Istanbul by an unsupervised learning approach. We assign a score to each representative potential customer location based on the distances that the residents have taken for their past medical expenses. We set the coverage parameters by a spatial analysis. We formulate a mixed integer linear programming model and solve it to near-optimality using Cplex. We quantify the trade-off between capacity and service level. We also compare the results of several models differing in their coverage parameters to demonstrate the flexibility of our model and show the impact of accounting for full and partial coverage