Sustainability Benefits Analysis of CyberManufacturing Systems

Confronted with growing sustainability awareness, mounting environmental pressure, meeting modern customers’ demand and the need to develop stronger market competitiveness, the manufacturing industry is striving to address sustainability-related issues in manufacturing. A new manufacturing system called CyberManufacturing System (CMS) has a great potential in addressing sustainability issues by handling manufacturing tasks differently and better than traditional manufacturing systems. CMS is an advanced manufacturing system where physical components are fully integrated and seamlessly networked with computational processes. The recent developments in Internet of Things, Cloud Computing, Fog Computing, Service-Oriented Technologies, etc., all contribute to the development of CMS. Under the context of this new manufacturing paradigm, every manufacturing resource or capability is digitized, registered and shared with all the networked users and stakeholders directly or through the Internet. CMS infrastructure enables intelligent behaviors of manufacturing components and systems such as self-monitoring, self-awareness, self-prediction, self-optimization, self-configuration, self-scalability, self-remediating and self-reusing. Sustainability benefits of CMS are generally mentioned in the existing researches. However, the existing sustainability studies of CMS focus a narrow scope of CMS (e.g., standalone machines and specific industrial domains) or partial aspects of sustainability analysis (e.g., solely from energy consumption or material consumption perspectives), and thus no research has comprehensively addressed the sustainability analysis of CMS. The proposed research intends to address these gaps by developing a comprehensive definition, architecture, functionality study of CMS for sustainability benefits analysis. A sustainability assessment framework based on Distance-to-Target methodology is developed to comprehensively and objectively evaluate manufacturing systems’ sustainability performance. Three practical cases are captured as examples for instantiating all CMS functions and analyzing the advancements of CMS in addressing concrete sustainability issues. As a result, CMS has proven to deliver substantial sustainability benefits in terms of (i) the increment of productivity, production quality, profitability & facility utilization and (ii) the reduction in Working-In-Process (WIP) inventory level & material consumption compared with the alternative traditional manufacturing system paradigms

Simulation Modeling for Sustainability: A Review of the Literature

This article is a review of work published in various journals and conference proceedings on the topics of Simulation Modelling for Sustainability between January 2000 and May 2015. A total of 192 papers are reviewed. The article intends to serve three goals. First, it will be useful to researchers who wish to know what kinds of questions have been raised and how they have been addressed in the areas of simulation modelling for sustainability. Second, the article will be a useful resource for searching research topics. Third, it will serve as a comprehensive bibliography of the papers published during the period. The literature is analysed for application areas, simulation methods and dimensions of the triple bottom line model of sustainable development

Adaptivität und semantische Interoperabilität von Manufacturing Execution Systemen (MES)

MES (Manufacturing Execution Systems) are situated between automation and management level and are affected from changes of the production. Therefore their adaptivity within the lifecycle of production plants is mission critical. Furthermore MES act as data and information hub. This means that they have to work together with other systems in an efficient and seamless way. MES must be interoperable and must have semantics under control. The present publication faces both aspects

Process design and supervision: A next generation simulation approach to digitalised manufacturing

Modern processes will increasingly have a digital counterpart which is an interactive representation of the physical system integrated into a digital environment. At the heart of this digital counterpart are simulators that use raw data and calculation models to automate supervision tasks and increase process autonomy. As such, simulators have become a critical part of process digitalisation. But, despite the exponential increase of digitalisation related research simulators have not evolved to fully utilise the latest practices for data value extraction. This research work examines the current role of simulation within digitalised systems, identifies state-of-the-art simulator structural components and proposes a design architecture for next generation simulators. The proposed architecture provides a structured way to develop next generation simulation systems. At the same time, it embeds the latest data science related technologies into the simulator and enables the integration of the simulator with modern edge or cloud systems. To achieve that, the simulator is broken down into five elements and the function of each element is specified based on system performance, digital environment compatibility and development ease. To demonstrate the effectiveness of the architecture, the author developed a vertical machining centre simulator that uses a mesh-based method to represent the process and the latest automated machine learning techniques to generate knowledge from the information extracted by the monitoring data. To verify the capabilities of the simulator a series of experiments were performed on a vertical machining system with a focus on spindle load measurement. The results show that the developed simulator estimates spindle load accurately despite input data noise and within the time restrictions occurring in real-time applications. All generated knowledge is stored and accessible for future simulator runs and finally, the system demonstrates its ability to extract value from all available data while reducing the raw data storage needs

Simulation Data Architecture for Sustainable Development

Reducing costs, improving quality, shortening the time-to-market, and at the same time act and think sus-tainable are major challenges for manufacturing industries. To strive towards these objectives, discrete event simulation (DES) has proven to be an effective tool for production system decision support. Large companies continuously log raw data, and are therefore able to collect large quantities of re-source event information. However, usually it is difficult to reuse data for future DES projects. Thus, the aim of this paper is to describe how to facilitate data sharing between data sources and DES models. A test implementation of a simulation data architecture has been realized. A data processing tool, a database and an interface were created, which provide reusable resource event data to pave the way for sustainable resource information in DES projects. The entirety data exchange is provided by standard XML documents following the latest Core Manufacturing Simulation Data recommendations

Simulation Data Architecture for Sustainable Development

Reducing costs, improving quality, shortening the time-to-market, and at the same time act and think sus-tainable are major challenges for manufacturing industries. To strive towards these objectives, discrete event simulation (DES) has proven to be an effective tool for production system decision support. Large companies continuously log raw data, and are therefore able to collect large quantities of re-source event information. However, usually it is difficult to reuse data for future DES projects. Thus, the aim of this paper is to describe how to facilitate data sharing between data sources and DES models. A test implementation of a simulation data architecture has been realized. A data processing tool, a database and an interface were created, which provide reusable resource event data to pave the way for sustainable resource information in DES projects. The entirety data exchange is provided by standard XML documents following the latest Core Manufacturing Simulation Data recommendations