Exploring self-organization and self-adaption for smart manufacturing complex networks

doi:10.1007/s42524-022-0225-1

Front. Eng

2023, Vol. 10

Issue (2) : 206-222 https://doi.org/10.1007/s42524-022-0225-1

RESEARCH ARTICLE

Exploring self-organization and self-adaption for smart manufacturing complex networks

Zhengang GUO¹, Yingfeng ZHANG²(

), Sichao LIU³, Xi Vincent WANG³, Lihui WANG³

¹. Key Laboratory of Industrial Engineering and Intelligent Manufacturing of Ministry of Industry and Information Technology, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China; Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
². Key Laboratory of Industrial Engineering and Intelligent Manufacturing of Ministry of Industry and Information Technology, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China
³. Department of Production Engineering, KTH Royal Institute of Technology, Stockholm 100 44, Sweden

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Abstract

Trends toward the globalization of the manufacturing industry and the increasing demands for small-batch, short-cycle, and highly customized products result in complexities and fluctuations in both external and internal manufacturing environments, which poses great challenges to manufacturing enterprises. Fortunately, recent advances in the Industrial Internet of Things (IIoT) and the widespread use of embedded processors and sensors in factories enable collecting real-time manufacturing status data and building cyber–physical systems for smart, flexible, and resilient manufacturing systems. In this context, this paper investigates the mechanisms and methodology of self-organization and self-adaption to tackle exceptions and disturbances in discrete manufacturing processes. Specifically, a general model of smart manufacturing complex networks is constructed using scale-free networks to interconnect heterogeneous manufacturing resources represented by network vertices at multiple levels. Moreover, the capabilities of physical manufacturing resources are encapsulated into virtual manufacturing services using cloud technology, which can be added to or removed from the networks in a plug-and-play manner. Materials, information, and financial assets are passed through interactive links across the networks. Subsequently, analytical target cascading is used to formulate the processes of self-organizing optimal configuration and self-adaptive collaborative control for multilevel key manufacturing resources while particle swarm optimization is used to solve local problems on network vertices. Consequently, an industrial case based on a Chinese engine factory demonstrates the feasibility and efficiency of the proposed model and method in handling typical exceptions. The simulation results show that the proposed mechanism and method outperform the event-triggered rescheduling method, reducing manufacturing cost, manufacturing time, waiting time, and energy consumption, with reasonable computational time. This work potentially enables managers and practitioners to implement active perception, active response, self-organization, and self-adaption solutions in discrete manufacturing enterprises.

Keywords cyber–physical systems Industrial Internet of Things smart manufacturing complex networks self-organization and self-adaption analytical target cascading collaborative optimization

Corresponding Author(s): Yingfeng ZHANG

Just Accepted Date: 20 October 2022 Online First Date: 18 November 2022 Issue Date: 29 May 2023

Cite this article:

Zhengang GUO,Yingfeng ZHANG,Sichao LIU, et al. Exploring self-organization and self-adaption for smart manufacturing complex networks[J]. Front. Eng, 2023, 10(2): 206-222.

URL:

https://academic.hep.com.cn/fem/EN/10.1007/s42524-022-0225-1
https://academic.hep.com.cn/fem/EN/Y2023/V10/I2/206

Fig.1 Overall architecture of smart manufacturing complex networks.

Fig.2 Mechanisms of self-organization and self-adaption.

Fig.3 Network topology of smart manufacturing complex networks.

Fig.4 Information flow of the proposed analytical target cascading model (notes: t represents for the targets, and r stands for the responses).

Fig.5 Proof-of-concept prototype systems.

Vertex	Parent vertex	Manuf. service	Manuf. cost ($)	Setup time (s)	Manuf. time (s)	Waiting time (s)	Energy cons. (kW)
$m 1$	$c 1$	$m s m 1, 1$	48	185	1436	1087	34
		$m s m 1, 2$	62	208	1720	909	23
		$m s m 1, 3$	35	137	1818	1187	31
$m 2$	$c 1$	$m s m 2, 1$	93	234	2142	1102	42
$m 2$	$c 1$	$m s m 2, 2$	41	91	2170	1396	32
$m 3$	$c 2$	$m s m 3, 1$	46	210	1574	822	54
$m 3$	$c 2$	$m s m 3, 2$	43	172	2216	823	35
$m 4$	$c 2$	$m s m 4, 1$	52	156	1596	1022	31
$m 4$	$c 2$	$m s m 4, 2$	35	184	2278	1267	49
···	···	···	···	···	···	···	···
$m 10$	$c 5$	$m s m 10, 1$	78	98	1445	726	24
$m 10$	$c 5$	$m s m 10, 2$	21	147	1210	993	30
$m 11$	$c 5$	$m s m 11, 1$	71	153	2634	889	53
$m 11$	$c 5$	$m s m 11, 2$	37	145	2355	1289	41
···	···	···	···	···	···	···	···
$m 21$	$c 10$	$m s m 21, 1$	23	218	975	790	26
$m 21$	$c 10$	$m s m 21, 2$	45	226	1774	1324	23
$m 22$	$c 10$	$m s m 22, 1$	92	185	2644	1261	33
$m 22$	$c 10$	$m s m 22, 2$	69	144	2007	939	36
$m 23$	$c 10$	$m s m 23, 1$	45	92	1915	1058	34
$m 23$	$c 10$	$m s m 23, 2$	63	223	2603	968	30
$m 24$	$c 11$	$m s m 24, 1$	99	174	1008	1432	55
		$m s m 24, 2$	58	101	2090	1281	40
		$m s m 24, 3$	91	211	2469	1465	33
$v 1$	$s 1$	$m s v 1, 1$	15	61	498	127	2.8
$v 1$	$s 1$	$m s v 1, 2$	7	64	494	179	2.9
$v 2$	$s 1$	$m s v 2, 1$	9	80	388	112	2.3
$v 2$	$s 1$	$m s v 2, 2$	6	76	373	202	2.5
$v 3$	$s 1$	$m s v 3, 1$	5	65	351	175	1.9
$v 3$	$s 1$	$m s v 3, 2$	8	74	436	112	2.4
···	···	···	···	···	···	···	···
$v 6$	$s 2$	$m s v 6, 1$	13	87	383	134	2.5
$v 6$	$s 2$	$m s v 6, 2$	13	64	337	167	1.9
$v 7$	$s 3$	$m s v 7, 1$	12	63	481	180	2.4
		$m s v 7, 2$	11	90	453	100	1.5
		$m s v 7, 3$	9	74	455	100	2.0
$v 8$	$s 4$	$m s v 8, 1$	14	88	315	134	1.8
$v 8$	$s 4$	$m s v 8, 2$	12	60	378	130	1.6
$v 9$	$s 4$	$m s v 9, 1$	9	80	489	157	2.4
$v 9$	$s 4$	$m s v 9, 2$	8	67	444	168	1.9
$v 10$	$s 4$	$m s v 10, 1$	9	69	431	235	2.6
$v 10$	$s 4$	$m s v 10, 2$	11	76	358	204	1.8
$v 11$	$s 5$	$m s v 11, 1$	5	71	369	138	3.0
$v 11$	$s 5$	$m s v 11, 2$	14	60	428	131	2.9
$v 12$	$s 5$	$m s v 12, 1$	13	64	469	175	2.0
$v 12$	$s 5$	$m s v 12, 2$	15	88	436	218	1.5

Tab.1 Real-time information of machine and vehicle vertices

Case number	Vertex	Parent vertex	Manuf. service	Manuf. cost ($)	Setup time (s)	Manuf. time (s)	Waiting time (s)	Energy cons. (kW)
Case 1	$m 25$	$c 1$	$m s m 25, 1$	66	96	1531	558	35
			$m s m 25, 2$	25	114	616	563	27
			$m s m 25, 3$	61	132	860	302	22
Case 2	$m 26$	$c 5$	$m s m 26, 1$	27	100	1577	325	49
Case 2	$m 26$	$c 5$	$m s m 26, 2$	62	119	852	448	28
Case 3	$m 27$	$c 11$	$m s m 27, 1$	61	99	1037	368	37
Case 3	$m 27$	$c 11$	$m s m 27, 2$	72	101	660	414	47
Case 4	$v 13$	$s 3$	$m s v 13, 1$	14	86	290	94	1.8
Case 4	$v 13$	$s 3$	$m s v 13, 2$	10	69	280	96	1.7
Case 5	$v 14$	$s 5$	$m s v 14, 1$	6	62	244	119	1.8
Case 5	$v 14$	$s 5$	$m s v 14, 2$	8	76	277	91	2.0

Tab.2 Real-time information of machine and vehicle vertices added from Cases 1–5

Case number	Vertex	Parent vertex	Failure cause
Case 6	$m 1$	$c 1$	Equipment failure
Case 7	$m 10$	$c 5$	Maintenance
Case 8	$m 21$	$c 10$	Maintenance
Case 9	$v 6$	$s 2$	Equipment failure
Case 10	$v 8$	$s 4$	Maintenance

Tab.3 Real-time information of machine and vehicle vertices removed from Cases 6–10

Fig.6 Comparison experiment results of event-triggered rescheduling and the proposed method.

Tab.4 Numerical analysis of smart manufacturing complex networks in three simulation experiments

Fig.7 Changing processes of the network topology for smart manufacturing complex networks.

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