Big Data Analytics for Processing Time Analysis in an IoT-enabled manufacturing Shop Floor

被引：17

作者：

Kho, Daniel D. ^{[1
]}

Lee, Seungmin ^{[1
]}

Zhong, Ray Y. ^{[1
]}

机构：

[1] Univ Auckland, Dept Mech Engn, Auckland, New Zealand

来源：

46TH SME NORTH AMERICAN MANUFACTURING RESEARCH CONFERENCE, NAMRC 46 | 2018年 / 26卷

关键词：

Big Data Analytics; RFID; IoT; Manufacturing; Shop Floor; EXECUTION SYSTEM; MANAGEMENT; INTERNET; SERVICE;

D O I：

10.1016/j.promfg.2018.07.107

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Internet of things (IoT) technology has been widely used in manufacturing where great myriad of data have been generated. This paper introduces a big data analytics for Internet of Things (IoT)-enabled manufacturing shop floor which uses radio frequency identification (RFID) technology for capturing the real-time production data. This involves two machine learning techniques: k-means clustering and gradient descent optimization for the data from the manufacturing sites. In particular, this research deals with the real RFID data related to various processes for individual batches and processing time. This could produce valid predictions about expected overall manufacturing time for a given number of manufacturing batch inputs. (C) 2018 The Authors. Published by Elsevier B.V.

引用

页码：1411 / 1420

页数：10

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