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13 Kasım 2018

Intelligent Manufacturing – Literature Review

Intelligent Manufacturing
Özgür Acar
Yildiz Technical University
Control and Automation Engineering
Turkey, Istanbul
info@ozguracar.com.tr


Abstract—Information technologies grow rapidly nowadays
with the internet of things(IOT) trend. This growth affects
several fields, like industrial automation and erp systems. This
publication describes a general and flexible architecture for
integration manufacturing execution system function (MES) to
automation system and erp system by using internet of things.
These features are achieved by using internet of things abilities
like cloud and webservers. With deployment of this solution,
MES functions may gain more availability and efficiency.
And this solution name is Intelligent Manufacturing in this
paper. This research makes a study of Achieving Intelligent
Manufacturing: automation and erp communication by internet
of things technology for the implementation of a manufacturing
execution system.


keywords: Intelligent Manufacturing, Industrial Internet
of Things, Manufacturing Execution System


I. INTRODUCTION
It is now an inevitable reality that the use of the Internet has significantly changed our daily life by increasing communication, information sharing and mutual interaction among people. The new technological concept, called Internet of Things (IOT), is defined as the intelligent connection of
intelligent devices through objects that perceive and communicate
with each other. With this technology, it is possible to monitor almost all the events that occur in the environment we live in (houses, schools, workplaces, factories, cities, etc.) and collect data by using sensor devices which all are in small size and using wireless technology. Likewise, using
the required data that can be transferred from industrial systems related to production/manufacturing units into the Information System has also revealed another concept called Industrial IoT (IIoT). Real-time continuous data flow from the sensing devices and the production data terminals is made available for the Information Systems through the storage,
database and application services given by Cloud Service Providers in the internet environment. IoT into the industrial production, discussed its positive contributions in different application examples such as automatic storage, preventive maintenance, underground mining, smart environmental systems and given additional information about the open research issues . Intelligent Manufacturing Systems requires advanced
and efficient manufacturing technologies, management and
procedures in order to achieve value creation in global markets.
E-Manufacturing is the set of information technologies that allows companies to achieve on demand manufacturing through the integration of e-business applications. [3] MES (Manufacturing Execution System) puts plans given by enterprise system such as ERP(Enterprise Resource Planning) into practice in the shop floor. As contemporary companies are
faced with challenge represented by customer-centric market
environment and intense competitions, it becomes increasingly
important for companies to cope with dynamic customers demand and shop floor situation rapidly. Even though current MESs gets some improvement on collecting raw data, they
fall short of expectation on analyzing data and takes action
by cooperating various manufacturing management functions.
Furthermore, even though many researches treat data collection,
data analysis based on shop floor situation, little number
of previous researches deals with collaboration among various
functionalities provided by MES. [1]


II. DESIGNING FOR INTELLIGENT MANUFACTURING
Intelligent Manufacturing is the MES that can recognize and
deal with shop floor situation in real-time manner with having
3 things: (1) shorten response time when dealing with shop
floor situation or stakeholder requirement, (2) provide user
convenience with consistent response to shop floor situation
because user doesnt need to know all of details on collaboration
of functionalities, (3) cope with varying operation
schedule flexibly. Furthermore, it can be one of approaches
on contemporary industrial innovation campaign other than
American initiative Industrial Internet and German initiative
Industry 4.0.
A. Problems
 Workers should record data by hand which coming from machine controller and type into MES or Database.
 Its difficult for MES operator to track the work-in-piece in real-time.
 Allocating right preventive maintenance schedule is challenging
one since it should consider resource status and production operation schedule. It causes delay on making both operation schedule and preventive maintenance schedule and requires lots of communication load
 Generally, its hard to recognize what kind of failure happened in machine. It mostly depends on workers tacit knowledge to get around. If user does not have experience and insight, then itll take lots of time to figure out problem. That is, there exists deviation among workers.
 Its hard to figure out what kind of quality problem on product happened, what brought about that problem and when that problem started to happen. Even though manual inspection is conducted, it also depends on insight or expertise of workers so it can cause deviation among workers.
 Estimating material consumption for production is barely linked with supply chain management, which means that demand prediction relies on experience of manager and feedback work is mainly done call conversation, which causes lots of communication load and reduction of work efficiency.
 In manufacturing company, production performance analysis mainly manages the amount of production.
 Data integration /synchronization between MES and Enterprise
information system isnt fully established. It can pose lots of communication loads and manual work since one system cant utilize the data from other systems, which causes reduction of work efficiency.
B. Design consideration for Intelligent Manufacturing
Design consideration in the perspective of data collection,
integration, analysis, and collaboration among various MES
functions.

 Real-time data acquisition via sensor technology: To realize real-time data analysis and response, its essential to gather shop floor data to recognize shop floor situation. It can contribute to reduce time gap on data value between shop floor and Intelligent Manufacturing.
 Reliability of data generated from shop floor: Since analysis and judgement are based on shop floor data, its crucial to ensure the reliability of data. In general, accuracy and reliability is two important factors for measurement. Reliability has precedence over accuracy because error
can be adjusted using several software filter such as Kalman filter
 Communication means for various sensors and controllers:
Even though PLC can retrieve sensor data from sensor,controller and then send to MES
 Close connection with enterprise information system:
Communication between enterprise information system and MES should be established so that information can be exchanged effectively between them.
 Data storage in distributed database: Not all data collected from shop floor cant be handled immediately due to processing capability of system. To get around, temporary storage space is needed. Using relational database is not good option because manufacturing data is complex that relational database cant deal with it efficiently. Distributed database is an appropriate option because of high performance, efficiency, scalability.
 Data analysis methodology: Gathering shop floor data only isnt enough. Some methodology like data mining is needed to extract some useful information. Supporting functions of data mining include prediction, classification, clustering etc
 Visualization / Report for analysis result: Big data analysis
result is needed to be organized so that Intelligent Manufacturing operator or other users can understand the behavior of shop floor.
 Pervasive access to analysis result and execution result of MES functions from shop floor and user devices: Pervasive access to analysis result is a basis for accomplishing pervasive use for the result.

III. ACHIEVING INTELLIGENT MANUFACTURING
Before proposing architecture, system concept which applies to design of reference architecture are defined. This comes from the design consideration presented and these are big block of architecture. System concept is formed by reflecting design consideration. Adoption of real-time communication environment: Its an infrastructure that connects Intelligent Manufacturing, shop floor, enterprise information system, ERP, user. It is necessary to have this infrastructure so that shop floor data and enterprise system information can be flowed lively. Architecture can examine with 3 section: IIOT,MES,MES-ERP integration. It seems on Figure 1. IIOT section: It is about communication of online users with machines. Online user interface communicates with cloud, cloud communicates with local server in factory. Cloud and local server have common synchronised databases. If when local server changed, gives data to cloud within around minutes
periods. Local server communicates with PLC and server takes data of machines from PLC. In MES section: In addition to IIOT section, local server takes other shop floor parametric values from local user interface. So, local server knows all manufacturing values. MES-ERP integration section: Local
Server and ERP system have common database tables to communicate
with eachother. They can give needed manufacturing values eachother. For example: defected product numbers, raw material values, energy consumption, maintenance times,etc.

Fig. 1: Intelligent Manufacturing architecture


IV. EXAMPLE SCENARIO BASED ON DESIGN:INTELLIGENT
MANUFACTURING APPLICATION ON FEEDMILL PROSESS

Example scenario on that architecture is presented in Figure 2 and Figure 3.


Fig. 2: Feedmill factory production operations


In feed production shop floor gives raw materials status, dosing, pelleting,milling values for quality diagnosing. Every production has batch number for saving all process raw materials to packing. Batch number has these informations to increase quality of feed: raw material supplier, raw materials temperature and humidty values, dosing receipe’s target and
actual values, pelleting and milling parameters, production’s time, operator’s information and customer’s informations, etc. Also ERP can give receipes for dosing operation. Online user interface communicates with cloud, cloud communicates with local server in Feedmill factory. Cloud and local server have common synchronised databases. Local server
communicates with PLC and server takes data of machines from PLC. Online user interface has two login type producer and customer. All these values in reporting offline and online systems.
Producer can examine all shop floor and product values. Also
customers can follow their goods. Feedmill Factories has
accessibility feature by Intelligent Manufacturing integration
to automation system and ERP.


Fig. 3: Intelligent Manufacturing integration to feedmill


V. CONCLUSION
In this paper, firstly, industrial internet of things, manufacturing
execution system, erp systems are described. Then, the problem for current MES system by pointing out lack of environment for analysing and interpreting, collecting shop floor data in real-time manner are described. System of interest named Intelligent Manufacturing is defined and requirement and design consideration is defined for architecture
design. Then,example scenario making use of Intelligent Manufacturing
showed that Intelligent Manufacturing can comprehensively
and effectively deal with shop floor situation in real-time
manner. Furthermore, In this paper described and examined Intelligent
Manufacturing, and this Intelligent Manufacturing method can
be improved by artificial intelligence that decides increasing
of production capacity and efficiency.


ACKNOWLEDGMENT
This research was supported by Yildiz Technical University
Departmant of Control Engineering. Thanks to Associate
Professor Seref Naci Engin who provided insight and expertise
that greatly assisted the research of this paper.


REFERENCES
[1] Achieving eManufacturing: multihead control and web technology for the
implementation of a manufacturing execution system
Arturo Molina Armando Ramrez Santaella Received: February 2005 /
Accepted: January 2006 Springer Science+Business Media, LLC 2006
[2] Byeong Woo Jeon, Jumyung Um, Soo Cheol Yoon and Suh Suk-
Hwan (2017) An architecture design for smart manufacturing execution
system, Computer-Aided Design and Applications, 14:4, 472-485, DOI:
10.1080/16864360.2016.1257189
[3] AKU J. Sci. Eng. 16 (2016) 035102 (599-607) DOI: 10.5578/fmbd.43411
Endstride Nesnelerin Interneti (IoT) Uygulamalar Tuncay Ercan, Mahir
Kutay
[4] Wael M. Mohammed, Borja Ramis Ferrer, Sergii Iarovyi, Elisa Negri,
Luca Fumagalli, Andrei Lobov and Jose L. Martinez Lastra
(2018) Generic platform for manufacturing execution system functions
in knowledge-driven manufacturing systems, International Journal
of Computer Integrated Manufacturing, 31:3, 262-274, DOI:
10.1080/0951192X.2017.1407874
[5] Jrgen Kletti, Manufacturing Execution System MES book ISBN 978-3-
540-49743-1 Springer Berlin Heidelberg New York
[6] Manufacturing Execution System for Process Improvement Shankar
Venkatachalam, Vasanth.D and Sreekanth Bammidi Ashok Leyland Ltd.
2009-01-2855
[7] Trends in Intelligent Robotics, Automation and Manufacturing, First International
Conference, IRAM 2012 Kuala Lumpur, Malaysia, November
2012 Proceedings
[8] Advances in Production Management Systems,Innovative Production
Management Towards Sustainable Growth,IFIP WG 5.7 International
Conference, APMS 2015 Tokyo, Japan, September 79, 2015 Proceedings,
Part II

1 Response

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