Peer to peer metrological data sharing model

Abstract

Present manufacturing systems often generate enormous amounts of data, that are often forgotten or lost. A major reason for ignoring such data is the heterogeneity of data. This research focuses on the heterogeneity between the manufacturing machine’s capacity parameters and part design. In manufacturing factories, the machine capacity data is available in form of machine specifications, while part data is stored in 2D or 3D-CAD models. In this thesis, a framework is proposed to provide guidelines and strategies for acquiring, pre-processing, and storing manufacturing capacity data in the form of structured table-oriented database systems. The framework also proposes the extraction, pre-processing, and storage of dimensional data of Computer-Aided Design (CAD) part models into feature-based-logical storage within XML files. Such a database storage system can improve vendor search using advanced predictive modeling. Such a system is beneficial for small-medium scale machine shops for quantifying their manufacturing capability and constraints and linking such with a prospective pool of manufacturing part’s designs.