Abstract

Effective and efficient modern manufacturing operations require the acceptance and incorporation of the fourth industrial revolution, also known as Industry 4.0. Traditional shop floors are evolving their production into smart factories. To continue this trend, a specific architecture for the cyber-physical system is required, as well as a systematic approach to automate the application of algorithms and transform the acquired data into useful information. This work makes use of an approach that distinguishes three layers that are part of the existing Industry 4.0 paradigm: edge, fog, and cloud. Each of the layers performs computational operations, transforming the data produced in the smart factory into useful information. Trained or untrained methods for data analytics can be incorporated into the architecture. A case study is presented in which a real-time statistical control process algorithm based on control charts was implemented. The algorithm automatically detects changes in the material being processed in a computerized numerical control (CNC) machine. The algorithm implemented in the proposed architecture yielded short response times. The performance was effective since it automatically adapted to the machining of aluminum and then detected when the material was switched to steel. The data were backed up in a database that would allow traceability to the line of g-code that performed the machining.

References

1.
Lynn
,
R.
,
Louhichi
,
W.
,
Parto
,
M.
,
Wescoat
,
E.
, and
Kurfess
,
T.
,
2017
, “
Rapidly Deployable MTConnect-Based Machine Tool Monitoring Systems
,”
2017 ASME Manufacturing Science and Engineering Conference
,
Los Angeles, CA
,
June 4–8
, pp.
1
10
.
2.
Liu
,
X. F.
,
Shahriar
,
M. R.
,
Al Sunny
,
S. M. N.
,
Leu
,
M. C.
, and
Hu
,
L.
,
2017
, “
Cyber-Physical Manufacturing Cloud: Architecture, Virtualization, Communication, and Testbed
,”
J. Manuf. Syst.
,
43
, pp.
352
364
.
3.
Mourtzis
,
D.
,
Milas
,
N.
, and
Athinaios
,
N.
,
2018
, “
Towards Machine Shop 4.0: A General Machine Model for CNC Machine-Tools Through OPC-UA
,”
Procedia CIRP
,
78
, pp.
301
306
.
4.
Kim
,
D.-H.
,
Kim
,
T. J. Y.
,
Wang
,
X.
,
Kim
,
M.
,
Quan
,
Y.-J.
,
Oh
,
J. W.
,
Min
,
S.-H.
,
Kim
,
H.
,
Bhandari
,
B.
,
Yang
,
I.
, and
Ahn
,
S.-H.
,
2018
, “
Smart Machining Process Using Machine Learning: A Review and Perspective on Machining Industry
,”
Int. J. Precis. Eng. Manuf. Green Technol.
,
5
(
4
), pp.
555
568
.
5.
Jurkovic
,
Z.
,
Cukor
,
G.
,
Brezocnik
,
M.
, and
Brajkovic
,
T.
,
2018
, “
A Comparison of Machine Learning Methods for Cutting Parameters Prediction in High Speed Turning Process
,”
J. Intell. Manuf.
,
29
(
8
), pp.
1683
1693
.
6.
Wu
,
D.
,
Jennings
,
C.
,
Terpenny
,
J.
,
Gao
,
R. X.
, and
Kumara
,
S.
,
2017
, “
A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests
,”
J. Manuf. Sci. Eng.
,
139
(
7
), p.
071018
.
7.
Qin
,
J.
,
Liu
,
Y.
, and
Grosvenor
,
R.
,
2016
, “
A Categorical Framework of Manufacturing for Industry 4.0 and Beyond
,”
Procedia CIRP
,
52
, pp.
173
178
.
8.
Lee
,
J.
,
Bagheri
,
B.
, and
Jin
,
C.
,
2016
, “
Introduction to Cyber Manufacturing
,”
Manuf. Lett.
,
8
, pp.
11
15
.
9.
Lee
,
J.
,
Bagheri
,
B.
, and
Kao
,
H. A.
,
2015
, “
A Cyber-Physical Systems Architecture for Industry 4.0-Based Manufacturing Systems
,”
Manuf. Lett.
,
3
, pp.
18
23
.
10.
Monostori
,
L.
,
Kádár
,
B.
,
Bauernhansl
,
T.
,
Kondoh
,
S.
,
Kumara
,
S.
,
Reinhart
,
G.
,
Sauer
,
O.
,
Schuh
,
G.
,
Sihn
,
W.
, and
Ueda
,
K.
,
2016
, “
Cyber-Physical Systems in Manufacturing
,”
CIRP Ann.
,
65
(
2
), pp.
621
641
.
11.
Li
,
L.
,
Ota
,
K.
, and
Dong
,
M.
,
2018
, “
Deep Learning for Smart Industry: Efficient Manufacture Inspection System With Fog Computing
,”
IEEE Trans. Ind. Inf.
,
14
(
10
), pp.
4665
4673
.
12.
Qi
,
Q.
, and
Tao
,
F.
,
2019
, “
A Smart Manufacturing Service System Based on Edge Computing, Fog Computing, and Cloud Computing
,”
IEEE Access
,
32
(
4–5
), pp.
86769
86777
.
13.
Wang
,
J.
, and
Li
,
D.
,
2019
, “
Task Scheduling Based on a Hybrid Heuristic Algorithm for Smart Production Line With Fog Computing
,”
Sensors
,
19
(
5
), pp.
1
13
.
14.
Wang
,
J.
,
Zheng
,
P.
,
Lv
,
Y.
,
Bao
,
J.
, and
Zhang
,
J.
,
2019
, “
Fog-IBDIS: Industrial Big Data Integration and Sharing With Fog Computing for Manufacturing Systems
,”
Engineering
,
5
(
4
), pp.
662
670
.
15.
Chen
,
B.
,
Wan
,
J.
,
Shu
,
L.
,
Li
,
P.
,
Mukherjee
,
M.
, and
Yin
,
B.
,
2017
, “
Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges
,”
IEEE Access
,
6
, pp.
6505
6519
.
16.
Beregi
,
R.
,
Pedone
,
G.
, and
Mezgár
,
I.
,
2019
, “
A Novel Fluid Architecture for Cyber-Physical Production Systems
,”
Int. J. Comput. Integr. Manuf.
,
32
(
4–5
), pp.
340
351
.
17.
Nikolakis
,
N.
,
Senington
,
R.
,
Sipsas
,
K.
,
Syberfeldt
,
A.
, and
Makris
,
S.
,
2020
, “
On a Containerized Approach for the Dynamic Planning and Control of a Cyber-Physical Production System
,”
Rob. Comput. Integr. Manuf.
,
64
, p.
101919
.
18.
Castro-Martin
,
A. P.
,
Ahuett-Garza
,
H.
,
Guamán-Lozada
,
D.
,
Márquez-Alderete
,
M. F.
,
Urbina Coronado
,
P. D.
,
Orta Castañon
,
P. A.
,
Kurfess
,
T. R.
, and
González de Castilla
,
E.
,
2021
, “
Connectivity as a Design Feature for Industry 4.0 Production Equipment: Application for the Development of an In-Line Metrology System
,”
Appl. Sci.
,
11
(
3
), p.
1312
.
19.
Ahuett-Garza
,
H.
, and
Urbina Coronado
,
P. D.
,
2019
, “
A Reference Model for Evolving Digital Twins and Its Application to Cases in the Manufacturing Floor
,”
Smart Sustain. Manuf. Syst.
,
3
(
2
), p.
20190049
.
20.
Road
,
T. H.
,
2001
, “
A Fuzzy Reasoning Based Diagnosis System for X Control Charts
,”
J. Intell. Manuf.
,
12
(
1
), pp.
57
64
.
21.
Ahuett-Garza
,
H.
, and
Kurfess
,
T.
,
2018
, “
A Brief Discussion on the Trends of Habilitating Technologies for Industry 4.0 and Smart Manufacturing
,”
Manuf. Lett.
,
15
, pp.
60
63
.
22.
Zhou
,
P.
,
Zuo
,
D.
,
Hou
,
K. M.
,
Zhang
,
Z.
,
Dong
,
J.
,
Li
,
J.
, and
Zhou
,
H.
,
2019
, “
A Comprehensive Technological Survey on the Dependable Self-Management CPS: From Self-Adaptive Architecture to Self-Management Strategies
,”
Sensors
,
19
(
5
), pp.
1
53
.
23.
Fernández-Caramés
,
T. M.
,
Fraga-Lamas
,
P.
,
Suárez-Albela
,
M.
, and
Díaz-Bouza
,
M. A.
,
2018
, “
A Fog Computing Based Cyber-Physical System for the Automation of Pipe-Related Tasks in the Industry 4.0 Shipyard
,”
Sensors
,
18
(
6
), pp.
1
26
.
24.
Dolui
,
K.
, and
Datta
,
S. K.
,
2017
, “
Comparison of Edge Computing Implementations: Fog Computing, Cloudlet and Mobile Edge Computing
,”
Proceedings of GIoTS 2017—Glob Internet Things Summit
,
Geneva, Switzerland
,
June 6–9
.
25.
Baktir
,
A. C.
,
Ozgovde
,
A.
, and
Ersoy
,
C.
,
2017
, “
How Can Edge Computing Benefit From Software-Defined Networking: A Survey, Use Cases; Future Directions
,”
IEEE Commun. Surv. Tutorials.
,
19
(
4
), pp.
2359
2391
.
26.
Roman
,
R.
,
Lopez
,
J.
, and
Mambo
,
M.
,
2018
, “
Mobile Edge Computing, Fog, et al.: A Survey and Analysis of Security Threats and Challenges
,”
Future Gener. Comput. Syst.
,
78
, pp.
680
698
.
27.
Shirazi
,
S. N.
,
Gouglidis
,
A.
,
Farshad
,
A.
, and
Hutchison
,
D.
,
2017
, “
The Extended Cloud: Review and Analysis of Mobile Edge Computing and Fog From a Security and Resilience Perspective
,”
IEEE J. Sel. Areas Commun.
,
35
(
11
), pp.
1
10
.
28.
Angrish
,
A.
,
Starly
,
B.
,
Lee
,
Y.-S.
, and
Cohen
,
P. H.
,
2017
, “
A Flexible Data Schema and System Architecture for the Virtualization of Manufacturing Machines (VMM)
,”
J. Manuf. Syst.
,
45
, pp.
236
247
.
29.
Esmaeilian
,
B.
,
Behdad
,
S.
, and
Wang
,
B.
,
2016
, “
The Evolution and Future of Manufacturing: A Review
,”
J. Manuf. Syst.
,
39
, pp.
79
100
.
You do not currently have access to this content.