Big Data Analytics for Manufacturing Internet of Things: Opportunities, Challenges and Enabling Technologies

The recent advances in information and communication technology (ICT) have promoted the evolution of conventional computer-aided manufacturing industry to smart data-driven manufacturing. Data analytics in massive manufacturing data can extract huge business values while can also result in research challenges due to the heterogeneous data types, enormous volume and real-time velocity of manufacturing data. This paper provides an overview on big data analytics in manufacturing Internet of Things (MIoT). This paper first starts with a discussion on necessities and challenges of big data analytics in manufacturing data of MIoT. Then, the enabling technologies of big data analytics of manufacturing data are surveyed and discussed. Moreover, this paper also outlines the future directions in this promising area.Comment: 14 pages, 6 figures, 3 table

IDEAS-1997-2021-Final-Programs

This document records the final program for each of the 26 meetings of the International Database and Engineering Application Symposium from 1997 through 2021. These meetings were organized in various locations on three continents. Most of the papers published during these years are in the digital libraries of IEEE(1997-2007) or ACM(2008-2021)

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

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