A Relational Database Management System Approach for Data Integration in Manufacturing Process

In this paper we introduce a data integration system by implementing a function into the context of PostgreSQL. The aim of this work is to collect files to process from two different data sources (a platform of Physical Testing Software (PTS) and another one of Physical Simulation Software (PSS)), in order to retrieve specific records through a query and integrate them. Both these platforms contain a large amount of files in semi-structured or unstructured format. This approach allows analysing data from different sources and creating a database remaining always in the PostgresSQL context. Indeed, the code is modular and it can be customizable for the specific scope. This approach reduces the information exchange with the client and the computational cost, because the instructions are applied all in once (on-the-fly) to the files stored in the Network File System (NFS). Furthermore, the integration outputs and the related data can be stored within it. One of the objectives of this work is to perform a new product introduction (NPI) for multi-sources data retrieval and integration. The result is a modular approach, customizable and suitable for the most of the integration and retrieval issues. The architecture proposed allows all the authorized users to access to the data in parallel, independently and on the same device, by running query straightly on the file through the Structured Query Language (SQL)

Development of a supervisory internet of things (IoT) system for factories of the future

Big data is of great importance to stakeholders, including manufacturers, business partners, consumers, government. It leads to many benefits, including improving productivity and reducing the cost of products by using digitalised automation equipment and manufacturing information systems. Some other benefits include using social media to build the agile cooperation between suppliers and retailers, product designers and production engineers, timely tracking customers’ feedbacks, reducing environmental impacts by using Internet of Things (IoT) sensors to monitor energy consumption and noise level. However, manufacturing big data integration has been neglected. Many open-source big data software provides complicated capabilities to manage big data software for various data-driven applications for manufacturing. In this research, a manufacturing big data integration system, named as Data Control Module (DCM) has been designed and developed. The system can securely integrate data silos from various manufacturing systems and control the data for different manufacturing applications. Firstly, the architecture of manufacturing big data system has been proposed, including three parts: manufacturing data source, manufacturing big data ecosystem and manufacturing applications. Secondly, nine essential components have been identified in the big data ecosystem to build various manufacturing big data solutions. Thirdly, a conceptual framework is proposed based on the big data ecosystem for the aim of DCM. Moreover, the DCM has been designed and developed with the selected big data software to integrate all the three varieties of manufacturing data, including non-structured, semi-structured and structured. The DCM has been validated on three general manufacturing domains, including product design and development, production and business. The DCM cannot only be used for the legacy manufacturing software but may also be used in emerging areas such as digital twin and digital thread. The limitations of DCM have been analysed, and further research directions have also been discussed