scholarly journals A framework for data-driven digital twins for smart manufacturing

2022 ◽  
Vol 136 ◽  
pp. 103586
Author(s):  
Jonas Friederich ◽  
Deena P. Francis ◽  
Sanja Lazarova-Molnar ◽  
Nader Mohamed
Keyword(s):  
Data Driven ◽  
Smart Manufacturing ◽  
Digital Twins

scholarly journals Data-Driven Intelligent 3D Surface Measurement in Smart Manufacturing: Review and Outlook

Machines ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 13
Author(s):  
Yuhang Yang ◽  
Zhiqiao Dong ◽  
Yuquan Meng ◽  
Chenhui Shao
Keyword(s):  
High Resolution ◽  
High Performance ◽  
Sampling Design ◽  
Three Dimensional ◽  
Measurement Data ◽  
Data Driven ◽  
Surface Measurement ◽  
Smart Manufacturing ◽  
Future Research ◽  
3D Surface

High-fidelity characterization and effective monitoring of spatial and spatiotemporal processes are crucial for high-performance quality control of many manufacturing processes and systems in the era of smart manufacturing. Although the recent development in measurement technologies has made it possible to acquire high-resolution three-dimensional (3D) surface measurement data, it is generally expensive and time-consuming to use such technologies in real-world production settings. Data-driven approaches that stem from statistics and machine learning can potentially enable intelligent, cost-effective surface measurement and thus allow manufacturers to use high-resolution surface data for better decision-making without introducing substantial production cost induced by data acquisition. Among these methods, spatial and spatiotemporal interpolation techniques can draw inferences about unmeasured locations on a surface using the measurement of other locations, thus decreasing the measurement cost and time. However, interpolation methods are very sensitive to the availability of measurement data, and their performances largely depend on the measurement scheme or the sampling design, i.e., how to allocate measurement efforts. As such, sampling design is considered to be another important field that enables intelligent surface measurement. This paper reviews and summarizes the state-of-the-art research in interpolation and sampling design for surface measurement in varied manufacturing applications. Research gaps and future research directions are also identified and can serve as a fundamental guideline to industrial practitioners and researchers for future studies in these areas.

scholarly journals Sustainable, Smart, and Sensing Technologies for Cyber-Physical Manufacturing Systems: A Systematic Literature Review

2021 ◽  
Vol 13 (10) ◽  
pp. 5495
Author(s):  
Mihai Andronie ◽  
George Lăzăroiu ◽  
Roxana Ștefănescu ◽  
Cristian Uță ◽  
Irina Dijmărescu
Keyword(s):  
Decision Making ◽  
Internet Of Things ◽  
Literature Review ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Production Networks ◽  
Production Logistics ◽  
Time Production

With growing evidence of the operational performance of cyber-physical manufacturing systems, there is a pivotal need for comprehending sustainable, smart, and sensing technologies underpinning data-driven decision-making processes. In this research, previous findings were cumulated showing that cyber-physical production networks operate automatically and smoothly with artificial intelligence-based decision-making algorithms in a sustainable manner and contribute to the literature by indicating that sustainable Internet of Things-based manufacturing systems function in an automated, robust, and flexible manner. Throughout October 2020 and April 2021, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “Internet of Things-based real-time production logistics”, “sustainable smart manufacturing”, “cyber-physical production system”, “industrial big data”, “sustainable organizational performance”, “cyber-physical smart manufacturing system”, and “sustainable Internet of Things-based manufacturing system”. As research published between 2018 and 2021 was inspected, and only 426 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 174 mainly empirical sources. Further developments should entail how cyber-physical production networks and Internet of Things-based real-time production logistics, by use of cognitive decision-making algorithms, enable the advancement of data-driven sustainable smart manufacturing.

scholarly journals Digital Twins Collaboration for Automatic Erratic Operational Data Detection in Industry 4.0

2021 ◽  
Vol 11 (7) ◽  
pp. 3186
Author(s):  
Radhya Sahal ◽  
Saeed H. Alsamhi ◽  
John G. Breslin ◽  
Kenneth N. Brown ◽  
Muhammad Intizar Ali
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Interaction Mechanism ◽  
Semantic Knowledge ◽  
Smart Manufacturing ◽  
Data Detection ◽  
Workflow Model ◽  
Digital Twins ◽  
Potential Failure ◽  
Operational Data

Digital twin (DT) plays a pivotal role in the vision of Industry 4.0. The idea is that the real product and its virtual counterpart are twins that travel a parallel journey from design and development to production and service life. The intelligence that comes from DTs’ operational data supports the interactions between the DTs to pave the way for the cyber-physical integration of smart manufacturing. This paper presents a conceptual framework for digital twins collaboration to provide an auto-detection of erratic operational data by utilizing operational data intelligence in the manufacturing systems. The proposed framework provide an interaction mechanism to understand the DT status, interact with other DTs, learn from each other DTs, and share common semantic knowledge. In addition, it can detect the anomalies and understand the overall picture and conditions of the operational environments. Furthermore, the proposed framework is described in the workflow model, which breaks down into four phases: information extraction, change detection, synchronization, and notification. A use case of Energy 4.0 fault diagnosis for wind turbines is described to present the use of the proposed framework and DTs collaboration to identify and diagnose the potential failure, e.g., malfunctioning nodes within the energy industry.

scholarly journals Data-driven diagnostics of positioning deviations in multi-axis robots for smart manufacturing

2020 ◽  
Vol 53 (2) ◽  
pp. 10330-10335
Author(s):  
M. Soualhi ◽  
K. Nguyen ◽  
K. Medjaher ◽  
D. Lebel ◽  
D. Cazaban
Keyword(s):  
Data Driven ◽  
Smart Manufacturing

Architecture and use cases of digital twins towards smart manufacturing

2021 ◽  
Vol 8 (3) ◽  
pp. 264
Author(s):  
Nibhrita Tiwari ◽  
Maninder Jeet Kaur ◽  
Ved Prakash Mishra
Keyword(s):  
Use Cases ◽  
Smart Manufacturing ◽  
Digital Twins

Digital Twin for Smart Manufacturing: The Practitioner’s Perspective

2020 ◽  
Author(s):  
Maja Bärring ◽  
Björn Johansson ◽  
Goudong Shao
Keyword(s):  
Conceptual Development ◽  
Production Systems ◽  
Manufacturing Sector ◽  
Smart Manufacturing ◽  
Digital Twin ◽  
Adoption And Implementation ◽  
New Information ◽  
Digital Twins ◽  
Discrete Manufacturing ◽  
Design Production

Abstract The manufacturing sector is experiencing a technological paradigm shift, where new information technology (IT) concepts can help digitize product design, production systems, and manufacturing processes. One of such concepts is Digital Twin and researchers have made some advancement on both its conceptual development and technological implementations. However, in practice, there are many different definitions of the digital-twin concept. These different definitions have created a lot of confusion for practitioners, especially small- and medium-sized enterprises (SMEs). Therefore, the adoption and implementation of the digital-twin concept in manufacturing have been difficult and slow. In this paper, we report our findings from a survey of companies (both large and small) regarding their understanding and acceptance of the digital-twin concept. Five supply-chain companies from discrete manufacturing and one trade organization representing suppliers in the automotive business were interviewed. Their operations have been studied to understand their current digital maturity levels and articulate their needs for digital solutions to stay competitive. This paper presents the results of the research including the viewpoints of these companies in terms of opportunities and challenges for implementing digital twins.

Sign in / Sign up

Export Citation Format

Share Document