Feature-based Data Model Research Articles

Multi-level structuralized model-based definition model based on machining features for manufacturing reuse of mechanical parts

Model-based definition (MBD) is a new strategy of managing engineering and business processes using 3D models as complete information sources. However, due to lack of feature-based data model representation approach, current MBD models cannot be applied well in manufacturing domain. In this paper, a new multi-level structuralized MBD model based on machining features for manufacturing reuse is presented to capture the abstract information, detailed feature interaction information, and machining semantics information involved. Firstly, the machining features are recognized from an MBD model, which are taken as the machining semantics carrier. Then, the coupled machining feature cluster is introduced to construct the greater granularity’s independent structural element than machining feature, which describes the feature interactions. Finally, the MBD model is structured based on machining features and coupled machining feature clusters in a hierarchical way. In the experiments, some aircraft structural parts are utilized to verify our approach for manufacturing reuse, and the experimental results are analyzed at length to show the application effectiveness of the multi-level structuralized MBD model.

Setup Planning Automation of Turned Parts Based on STEP-NC Standard

STEP-NC(ISO 14649), the extension of STEP(ISO 10303) standard developed for CNC controllers, is a feature-based data model. STEP-NC develops the step neutral data standard for CAD data, and uses the modern geometric constructs to define device independent tool paths, and CAM independent volume removal features. This paper will present an automatic setup planning module integrated in a CAPP system for rotational parts to be machined on a lathe. The developed system will determine the possible setup combinations that are necessary for a complete machining of the part. The applied methodology will take into consideration constraints such as the geometry of both the stock and the final part, the geometry and the capacity of the chuck, and the part tolerances. Finally, the analysis of tolerances charts will be implemented for the sets of surfaces to be machined within each setup. The output can be then used to augment the STEP-NC physical file.

Defining, recognizing and representing feature interactions in a feature-based data model

This research takes a feature data model, i.e. STEP-NC as input and determines interactions among the features in the model. The feature interaction data are then appended to the original data model for subsequent uses such as process planning. The method is generic by defining and utilising a set of interaction entities such as removal volumes, common volumes, connection face and bridge faces. This feature interaction data model is developed using the EXPRESS language, making it compatible with the STEP-NC data model. The interaction data are presented in both 3D and feature interaction graph (FIG) format in a developed software system assisted by the Open CASCADE solid modelling kernel.

Dealing with feature interactions for prismatic parts in STEP-NC

Determining the precedence of machining features is a critical issue in feature-based process planning. It becomes more complex when geometric interaction occurs between machining features. STEP-NC, the extension of STEP (ISO 10303) standard developed for CNC controllers, is a feature-based data model. It represents all the geometric and topological product data minus feature interactions. In this paper, machining precedence of interactive and non-interactive STEP-NC features is discussed. Local and global precedence of machining features are defined on the basis of geometric constraints, such as geometric interaction of features and feature approach face and technological constraint such as access direction of the cutting tool. A software tool has been developed to visualize the STEP-NC part model and to generate the graphs of feature interaction and feature precedence. The output can be then used to augment the STEP-NC data in order to generate the optimal sequence of operations.