Login Register Cart 0
Generic placeholder image

Recent Patents on Mechanical Engineering

Editor-in-Chief

ISSN (Print): 2212-7976
ISSN (Online): 1874-477X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Product Quality Tracing in Manufacturing Supply Chain Based on Big Data Technology

Author(s): Yin Huang*, Shumin Huang, Yichen Zhang, Xue Yang and Runda Liu

Volume 13, Issue 4, 2020

Page: [340 - 351] Pages: 12

DOI: 10.2174/2212797613999200525135351

Price: $65

Abstract

Background: Big data technology has been widely used in manufacturing supply chain management. However, traditional big data technology has some limitations, and it cannot achieve the continuous improvement of whole-process product quality tracing.

Objective: The purpose of this study is to overcome the limitations by patents analysis and provide new big data technology and technical modes to make the continuous improvements of whole-process product quality tracing for achieving effective product lifecycle management based on big data technology.

Methods: The research method, patent analysis, and comparative analysis are employed in this study to analyze product quality tracing in the manufacturing supply chain based on big data technology. Moreover, the procedure and steps of the new big data technology - Product Digital Twin (PDT), and its technical modes are designed by process design methods. Its key technologies are also analyzed and compared with traditional big data technology by the comparative study.

Results: The research achieves the continuous improvements of whole-process product quality tracing based on new big data technology - PDT by patent analysis. The formation process and behavior of manufactured products in the realistic environment are simulated, monitored, diagnosed, predicted, and controlled. In this way, the high-efficient coordination in various stages of the product lifecycle is propelled fundamentally and the continuous improvements of the whole-process product quality tracing based on big data technology is analyzed.

Conclusion: Three new technical modes based on big data technology are predicted for future researches and patents, namely, the immersive development mode integrating big data and the virtual reality technology, the knowledge-based multivariant coordinated development mode, and the lifecycle extended development model based on multi-domain interoperability.

Keywords: Big data technology, manufacturing supply chain, Product Digital Twin (PDT), Product Lifecycle Management (PLM), product quality tracing, technical mode.

« Previous Next »
[1]
Gandomi A, Haider M. Beyond the hype: Big data concepts, methods, and analytics. Int J Inform Manage 2015; 35(2): 137-44.
[http://dx.doi.org/10.1016/j.ijinfomgt.2014.10.007]
[2]
Li J, Tao F, Cheng Y. Big Data in product lifecycle management. Int J Adv Manuf Technol 2015; 81(1-4): 667-84.
[http://dx.doi.org/10.1007/s00170-015-7151-x]
[3]
Zhang J, Qin W, Bao JS. (2016).Manufacturing big data. Shanghai: Shanghai science & technology press..
[http://dx.doi.org/10.1016/j.inffus.2015.08.005] [PMID: 32288689]
[4]
MacGregor JF, Bruwer MJ, Miletec I, Cardin M, Liu Z. Latent variable models and big data in the process industries. IFAC 2015; 48(8): 520-4.
[http://dx.doi.org/10.1016/j.ifacol.2015.09.020]
[5]
Hazen BT, Boone CA, Jones-Farmer LA, Ezell JD. Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. Int J Prod Econ 2014; 154: 72-80.
[http://dx.doi.org/10.1016/j.ijpe.2014.04.018]
[6]
Zhuang CB, Liu JH, Xiong H, Ding XY, Liu SL, Weng G. Connotation, architecture and trends of product digital twin. CIMS 2017; 23(4): 753-68.
[7]
Babiceanu RF, Seker R. Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook. Comput Ind 2016; 81(C): 128-37.
[http://dx.doi.org/10.1016/j.compind.2016.02.004]
[8]
Topchiyski K, Ivanova A, Sabev H, Pavlov V. Enterprise Javabeans metadata model. US20080270974 (2008).
[9]
Pfeifer W, Said B, Kazmaier GS. Metadata model repository. US20100161682 (2010).
[10]
Bird C, Yusuf KL. Template model for metadata capture. US20100042978 (2010).
[11]
Martinez TR, Zeng XC. Instance weighted learning machine learning model. US20140180975 (2014).
[12]
Miao X, and Chu CT. Personalized machine learning system. US20150242760 (2015).
[13]
Lee J, Lapira E, Bagheri B, Kao H-A. Recent advances and trends in predictive manufacturing systems in big data environment. Manuf Lett 2013; 1(1): 38-41.
[http://dx.doi.org/10.1016/j.mfglet.2013.09.005]
[14]
Lee J, Siegel D, Lapira ER. Development of a predictive and preventive maintenance demonstration system for a semiconductor etching tool. ECS Trans 2013; 52(1): 913-27.
[http://dx.doi.org/10.1149/05201.0913ecst]
[15]
Huang Y, Liu C, Kumar K, Kalgaonkar KP, Gong Y. Modular deep learning model. US20170256254 (2017).
[16]
Mnih V, Puigdomènech Badia A, Graves AB, Harley TJA, Silver D, Kavukcuoglu K. Asynchronous deep reinforcement learning. US10346741 (2019).
[17]
Tao F, Zhang M, Cheng JF, Qi QL. Digital twin workshop: A new paradigm for future workshop. CIMS 2017; 23(1): 1-9.
[18]
Lund AM, Mochel K, Lin J-W, et al. Digital twin interface for operating wind farm. US20160333854 (2016).
[19]
Song Z, Canedo AM. Digital twin for energy efficient asset maintenance. US20160247129 (2016).
[20]
Hershey JE, Wheeler FW, Nielsen MC, Johnson CD, Dell'anno MJ, Joykutti J. Digital twin of twinned physical system. US20170286572 (2017).
[21]
Yu Y, Fan ST, Peng GW, Dai S, Zhao G. Study on application of digital twin model in product configuration management. Aeronaut Manuf Technol 2017; 526(7): 41-5.
[22]
Tan JR, et al. Intelligent Manufacturing: Key Technologies Applications. Machine Press: Beijing, China 2017.
[23]
Shuai CL, Chen XM, Qiu SG. Thinking and prospect of virtual reality application in aerospace intelligent manufacturing. Aeronaut Manuf Technol 2016; 511(16): 26-33.
[24]
Daniel C. Contextual virtual reality interaction. US20170263033 (2017).
[25]
Walthers B, Ramachandran M, Li L, Kulkarni A. Knowledge management. US20190325323 (2019).
[26]
William JC, Matthew WF, Kenneth AN. Supply chain management system. US20180260755 (2018).
[27]
Jung EKY, Levien RA, Malamud MA, Rinaldo Jr. JD. Supplychain side assistance. US20160314511 (2016).

Rights & Permissions Print Cite
Article Metrics
16
© 2024 Bentham Science Publishers | Privacy Policy