RFID-enabled real-time manufacturing execution system for discrete manufacturing: Software design and implementation

Discrete manufacturing (DM) refers to produce products in non-sequential processes so as to respond to market and customer requirements quickly under limited lead-time. However, in shop-floor management, DM companies usually confront challenges such as information gaps between different manufacturing units, slow responsiveness to customer changes, and poor visualization. The main reasons are lacking of efficient manufacturing data collection manners and software to support shop-floor management. This paper introduces an RFID-enabled real-time manufacturing execution system (RT-MES) for improving DM shop-floor management level in the perspective of illustrating the RT-MES software design and implementation. Several contributions from this paper are significant. First, a framework of RFID-enabled RT-MES is proposed, which is generic and helpful for enterprise information system (EIS) construction. Second, a plug-universal database-aided design (PUDAD) concept and its realization are elaborated, which could reduce RT-MES development and implementation cycle. Third, an interface middleware is reported to enable RT-MES real-time intercommunication with other EISs such as SAP ERP. Fourth, a real-life case study describes how RT-MES to enhance a typical DM firm's shop-floor management, which can be referenced by other DM companies when they initiate and implement RFID-enabled solutions. © 2011 IEEE.published_or_final_versionThe 2011 IEEE International Conference on Networking, Sensing and Control (ICNSC 2011), Delft, the Netherlands, 11-13 April 2011. In Proceedings of ICNSC, 2011, p. 311-31

A framework for smart production-logistics systems based on CPS and industrial IoT

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems

HP Brazil: Journey Towards Industrial Internet of Things Within Industry 4.0 Context

This paper features a case study of Hewlett Packard Brazil’s deployment of radio frequency identification-enabled Exceler8 platform to support its product assembly using Flextronics in Sorocaba, Sao Paulo and distribution by DHL. The study also identifies the stage HP Brazil belongs to in its journey to being a full smart factory using the framework of Odwazny et al. (2018). The case study and content analysis methods are used in analyzing the concepts prescribed by the Industry 4.0, smart factory, and Industrial Internet of Things (IIOT) frameworks to HP Brazil’s RFID system. The Odwazny et al. (2018) framework identifies HP Brazil as being in the maturity stage, with selected attributes of the “smart factory” stage since its Exceler8 platform supports vertical integration in its assembly, distribution, and recycling sites. Hopefully, empirical work will be pursued with vigor in the future to gain an understanding of the actual conditions that support the successful deployment of both Industry 4.0 and IIOT initiatives. Firms interested in applying Industry 4.0 and IIOT concepts within their production environments would be guided by this study. Applying the German Industry 4.0 model, their Industry 4.0 initiative would seek to (1) enable collaboration between humans and machines; (2) produce customized products in small batches; (3) optimize high automation; and (4) deploy devices in flexible and eco-friendly production processes to meet customization requirements

Enabling Communication Technologies for Automated Unmanned Vehicles in Industry 4.0

Within the context of Industry 4.0, mobile robot systems such as automated guided vehicles (AGVs) and unmanned aerial vehicles (UAVs) are one of the major areas challenging current communication and localization technologies. Due to stringent requirements on latency and reliability, several of the existing solutions are not capable of meeting the performance required by industrial automation applications. Additionally, the disparity in types and applications of unmanned vehicle (UV) calls for more flexible communication technologies in order to address their specific requirements. In this paper, we propose several use cases for UVs within the context of Industry 4.0 and consider their respective requirements. We also identify wireless technologies that support the deployment of UVs as envisioned in Industry 4.0 scenarios.Comment: 7 pages, 1 figure, 1 tabl

TechNews digests: Jan - Nov 2008

TechNews is a technology, news and analysis service aimed at anyone in the education sector keen to stay informed about technology developments, trends and issues. TechNews focuses on emerging technologies and other technology news. TechNews service : digests september 2004 till May 2010 Analysis pieces and News combined publish every 2 to 3 month

Intelligent decision support for maintenance: an overview and future trends

The changing nature of manufacturing, in recent years, is evident in industry’s willingness to adopt network-connected intelligent machines in their factory development plans. A number of joint corporate/government initiatives also describe and encourage the adoption of Artificial Intelligence (AI) in the operation and management of production lines. Machine learning will have a significant role to play in the delivery of automated and intelligently supported maintenance decision-making systems. While e-maintenance practice provides aframework for internet-connected operation of maintenance practice the advent of IoT has changed the scale of internetworking and new architectures and tools are needed. While advances in sensors and sensor fusion techniques have been significant in recent years, the possibilities brought by IoT create new challenges in the scale of data and its analysis. The development of audit trail style practice for the collection of data and the provision of acomprehensive framework for its processing, analysis and use should be avaluable contribution in addressing the new data analytics challenges for maintenance created by internet connected devices. This paper proposes that further research should be conducted into audit trail collection of maintenance data, allowing future systems to enable ‘Human in the loop’ interactions

Discrete event simulation and virtual reality use in industry: new opportunities and future trends

This paper reviews the area of combined discrete event simulation (DES) and virtual reality (VR) use within industry. While establishing a state of the art for progress in this area, this paper makes the case for VR DES as the vehicle of choice for complex data analysis through interactive simulation models, highlighting both its advantages and current limitations. This paper reviews active research topics such as VR and DES real-time integration, communication protocols, system design considerations, model validation, and applications of VR and DES. While summarizing future research directions for this technology combination, the case is made for smart factory adoption of VR DES as a new platform for scenario testing and decision making. It is put that in order for VR DES to fully meet the visualization requirements of both Industry 4.0 and Industrial Internet visions of digital manufacturing, further research is required in the areas of lower latency image processing, DES delivery as a service, gesture recognition for VR DES interaction, and linkage of DES to real-time data streams and Big Data sets