Smart Factory of Industry 4.0: Connection Infrastructure, Data Acquisition, Data Processing, and Applications

2019 ◽  
Author(s):  
Zhao Zhiqiang ◽  
Chua Wei Quan ◽  
Ding Xiaoming ◽  
Prabhu Vinayak Ashok
Keyword(s):  
Data Processing ◽  
Data Acquisition ◽  
Real Time ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Discrete Systems ◽  
Smart Factory ◽  
Hierarchical Architecture ◽  
Support Functions ◽  
Control Functions

Abstract Smart factory adopts cyber-physical technologies integrating independent discrete systems into a context-sensitive manufacturing environment to optimize manufacturing processes using decentralized information and real-time communication. This paper presents our work in the realization of a smart factory, which comprises of a four-layer hierarchical architecture, i.e. connection infrastructure, data acquisition, data processing and smart applications. In the connection infrastructure layer, all shopfloor machines are connected through diverse protocols, IoT sensors, PLC interfaces and DNC connectors. A centralized IoT gateway supports such a scalable and adaptable connection and ensures a reliable communication among all heterogeneous manufacturing systems. In the data acquisition layer, the real-time machine and job data are acquired from shopfloor systems. Machine data indicates machines’ working condition and job data reveals the production information. The data processing layer comprises of three modules, i.e. shopfloor monitoring, data visualization and data analytics, which monitor and visualize shopfloor activities and analyze the semantics of various data using AI-based TPM engines providing the scientific indicators for next-step decisions. The smart application layer provides with several decision-making and remote control functions for shopfloor productivity and efficiency, such as predictive maintenance, shopfloor management, machine & job optimization and digital twin. The smart factory system has been implemented in the manufacturing shopfloor at Nanyang Polytechnic. The results and validation show that the system can simultaneously collect and analyze the manufacturing data from shopfloor systems, and further communicate with and control the shopfloor systems with decision-support functions. The overall shopfloor efficiency and flexibility can be significantly improved towards a smart factory of Industry 4.0.

Distributed real time data processing architecture for the TJ-II data acquisition system

2004 ◽  
Vol 75 (10) ◽  
pp. 4261-4264 ◽  
Author(s):  
M. Ruiz ◽  
E. Barrera ◽  
S. López ◽  
D. Machón ◽  
J. Vega ◽  
...  
Keyword(s):  
Data Processing ◽  
Data Acquisition ◽  
Real Time ◽  
Data Acquisition System ◽  
Acquisition System ◽  
Time Data ◽  
Real Time Data ◽  
Real Time Data Processing ◽  
Processing Architecture

scholarly journals Data processing pipeline for serial femtosecond crystallography at SACLA

2016 ◽  
Vol 49 (3) ◽  
pp. 1035-1041 ◽  
Author(s):  
Takanori Nakane ◽  
Yasumasa Joti ◽  
Kensuke Tono ◽  
Makina Yabashi ◽  
Eriko Nango ◽  
...  
Keyword(s):  
Data Processing ◽  
Data Acquisition ◽  
Real Time ◽  
Data Set ◽  
Hit Rate ◽  
Processing Pipeline ◽  
Structure Solution ◽  
Two Stages ◽  
Serial Femtosecond Crystallography ◽  
Detector Saturation

A data processing pipeline for serial femtosecond crystallography at SACLA was developed, based onCheetah[Bartyet al.(2014).J. Appl. Cryst.47, 1118–1131] andCrystFEL[Whiteet al.(2016).J. Appl. Cryst.49, 680–689]. The original programs were adapted for data acquisition through the SACLA API, thread and inter-node parallelization, and efficient image handling. The pipeline consists of two stages: The first, online stage can analyse all images in real time, with a latency of less than a few seconds, to provide feedback on hit rate and detector saturation. The second, offline stage converts hit images into HDF5 files and runsCrystFELfor indexing and integration. The size of the filtered compressed output is comparable to that of a synchrotron data set. The pipeline enables real-time feedback and rapid structure solution during beamtime.

Real-time data acquisition and parallel data processing solution for TJ-II Bolometer arrays diagnostic

2006 ◽  
Vol 81 (15-17) ◽  
pp. 1863-1867
Author(s):  
E. Barrera ◽  
M. Ruiz ◽  
S. López ◽  
D. Machón ◽  
J. Vega ◽  
...  
Keyword(s):  
Data Processing ◽  
Data Acquisition ◽  
Real Time ◽  
Time Data ◽  
Parallel Data ◽  
Real Time Data ◽  
Bolometer Arrays ◽  
Processing Solution

Configuration space–based discrete event system specification formalism for a smart factory with real-time flexibility

2018 ◽  
Vol 26 (3) ◽  
pp. 265-275 ◽  
Author(s):  
Dae S Chang ◽  
Sang C Park
Keyword(s):  
Real Time ◽  
Configuration Space ◽  
Manufacturing Systems ◽  
Discrete Event ◽  
Discrete Event System ◽  
Smart Factory ◽  
System Specification ◽  
Discrete Event System Specification ◽  
Event System ◽  
Effective Description

A manufacturing system consists of various manufacturing devices, and each device has a set of tasks which are triggered by specific commands. Traditionally, simulation has been considered as an essential technology for the evaluation and analysis of manufacturing systems. Although discrete event system specification formalism has been a popular modeling tool for manufacturing systems, it has limitations in describing situations such as sudden cancelation of tasks. Proposed in this article is an extended discrete event system specification formalism for the effective description of a smart factory which requires the intelligence to handle turbulences in real-time production. The extended discrete event system specification formalism incorporates the configuration space concept, which is well-known in classical mechanics. While the conventional discrete event system specification formalism uses only the logical states set to represent the device states, the proposed formalism employs the combination of two sets: a logical states set (sequential states set) and a physical states set (configuration space of the device). As a result, the extended formalism enables the effective description of nondeterministic tasks which may occur frequently in a smart factory.

Multi-Criteria Decision Making in Manufacturing Systems

2019 ◽  
pp. 80-107
Author(s):  
Hande Erdoğan Aktan ◽  
Ömür Tosun
Keyword(s):  
Decision Making ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Decision Makers ◽  
Smart Factory ◽  
Multi Criteria Decision Making ◽  
Structural Modelling ◽  
Location Selection ◽  
The Relationship ◽  
Selection Of

In this study, an Industry 4.0-oriented electronical goods producer company's smart facility location selection problem is analyzed. The proposed problem is evaluated under environmental, economic, social, and technological criteria. The relationship between criteria are analyzed with interpretive structural modelling (ISM) and Matrice d'Impacts Croisés-Multiplication Appliquée á un Classement (MICMAC) methods. ISM method is used to assess the mutual relation of the criteria and their dependencies, whereas the MICMAC method is used to identify the importance of criteria based on their driving and dependence power. It is expected the methods used in this study which are related to evaluation of the criteria affecting the selection of the plant for a smart factory and the results of it will be useful for decision-makers and practitioners to categorize and differentiate the criteria. This study will be one of the first spearheading research to evaluate the criteria for establishing a smart factory.

scholarly journals Practical Use of Robot Manipulators as Intelligent Manufacturing Systems

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2877 ◽  
Author(s):  
Pablo Alhama Blanco ◽  
Fares Abu-Dakka ◽  
Mohamed Abderrahim
Keyword(s):  
Real Time ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Robot Manipulators ◽  
Smart Cities ◽  
Industrial Robot ◽  
Online Communication ◽  
Intelligent Manufacturing ◽  
Communication Tool ◽  
Industrial Big Data

This paper presents features and advanced settings for a robot manipulator controller in a fully interconnected intelligent manufacturing system. Every system is made up of different agents. As also occurs in the Internet of Things and smart cities, the big issue here is to ensure not only that implementation is key, but also that there is better common understanding among the main players. The commitment of all agents is still required to translate that understanding into practice in Industry 4.0. Mutual interactions such as machine-to-machine and man-to-machine are solved in real time with cyber physical capabilities. This paper explores intelligent manufacturing through the context of industrial robot manipulators within a Smart Factory. An online communication algorithm with proven intelligent manufacturing abilities is proposed to solve real-time interactions. The algorithm is developed to manage and control all robot parameters in real-time. The proposed tool in conjunction with the intelligent manufacturing core incorporates data from the robot manipulators into the industrial big data to manage the factory. The novelty is a communication tool that implements the Industry 4.0 standards to allow communications among the required entities in the complete system. It is achieved by the developed tool and implemented in a real robot and simulation

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