A novel approach for remanufacturing process planning considering uncertain and fuzzy information

doi:10.1007/s11465-021-0639-1

Frontiers of Mechanical Engineering

2021, Vol. 16

Issue (3): 546-558 https://doi.org/10.1007/s11465-021-0639-1

本期目录

A novel approach for remanufacturing process planning considering uncertain and fuzzy information

Yan LV¹, Congbo LI¹(

), Xikun ZHAO¹, Lingling LI², Juan LI¹

¹. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China
². College of Engineering and Technology, Southwest University, Chongqing 400715, China

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Abstract：

Remanufacturing, as one of the optimal disposals of end-of-life products, can bring tremendous economic and ecological benefits. Remanufacturing process planning is facing an immense challenge due to uncertainties and fuzziness of recoverable products in damage conditions and remanufacturing quality requirements. Although researchers have studied the influence of uncertainties on remanufacturing process planning, very few of them comprehensively studied the interactions among damage conditions and quality requirements that involve uncertain, fuzzy information. Hence, this challenge in the context of uncertain, fuzzy information is undertaken in this paper, and a method for remanufacturing process planning is presented to maximize remanufacturing efficiency and minimize cost. In particular, the characteristics of uncertainties and fuzziness involved in the remanufacturing processes are explicitly analyzed. An optimization model is then developed to minimize remanufacturing time and cost. The solution is provided through an improved Takagi–Sugeno fuzzy neural network (T-S FNN) method. The effectiveness of the proposed approach is exemplified and elucidated by a case study. Results show that the training speed and accuracy of the improved T-S FNN method are 23.5% and 82.5% higher on average than those of the original method, respectively.

Key words： remanufacturing uncertain and fuzzy information process planning T-S FNN

收稿日期: 2020-11-18 出版日期: 2021-09-24

Corresponding Author(s): Congbo LI

引用本文:

. [J]. Frontiers of Mechanical Engineering, 2021, 16(3): 546-558.
Yan LV, Congbo LI, Xikun ZHAO, Lingling LI, Juan LI. A novel approach for remanufacturing process planning considering uncertain and fuzzy information. Front. Mech. Eng., 2021, 16(3): 546-558.

链接本文:

https://academic.hep.com.cn/fme/CN/10.1007/s11465-021-0639-1
https://academic.hep.com.cn/fme/CN/Y2021/V16/I3/546

Fig.1

Damage form	Damage amount interval	Damage degree	Quantified score interval	Calculation formula of score ( Q)
Abrasion	0	None	0	?
	0 < χ (wear amount) ≤ 0.6 mm	Slight	(0, 5)	$Q = 5 ? 0.6 ? χ 0.6 ? 0 × 5$
	0.6 mm < χ < 2.0 mm	Medium	[5, 10)	$Q = 10 ? 2 ? χ 2 ? 0.6 × 5$
	χ ≥ 2.0 mm	Severe	10	?
Corrosion	0	None	0	?
	0< δ (depth) ≤0.1 ?	Slight	(0, 5)	$Q = 5 ? 0.1 ? ? δ 0.1 ? ? 0 × 5$
	0.1 ? < δ < 0.2 ?	Medium	[5, 10)	$Q = 10 ? 0.2 ? ? δ 0.2 ? ? 0.1 ? × 5$
	δ ≥ 0.2 ?	Severe	10	?
Crack	0	None	0	?
	0 < θ (length) < 0.1 ?	General	(0, 10)	$Q = 10 ? 0.1 ? ? θ 0.1 ? ? 0 × 10$
	θ ≥ 0.1 ?	Severe	10	?
Deformation	0	None	0	?
	0 < ? (bending amount) ≤ 0.01 L	Slight	(0, 5)	$Q = 5 ? 0.01 L ? ? 0.01 L ? 0 × 5$
	0.01 L< ? < 0.02 L	Medium	[5, 10)	$Q = 10 ? 0.02 L ? ? 0.02 L ? 0.01 L × 5$
	? ≥ 0.02 L	Severe	10	?

Tab.1

Tab.2

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Tab.3

Tab.4

CNC	Computer numerical control
FCM	Fuzzy c-means
FNN	Fuzzy neural network
T-S FNN	Takagi–Sugeno fuzzy neural network
A _?0 and B _?0	Constant coefficients
A _?j and B _?j	Coefficients of x _?ij
c	Number of clusters of the simple
c ^N	Total number of nodes in the layer
D _i	Density index of the sample X _i
f ₁(?)	Remanufacturing time function
f ₂(?)	Remanufacturing cost function
h	Weight index
J(?) and J ^{( t)}	Objective function of FCM
k	Number of clusters
L	Length of the machine tool spindle (mm)
M _μ	Total number of components of type μ
N _c	Number of the fuzzification layer nodes
N _μ	Total number of attributes of components of type μ
P and P _w	Remanufacturing process plan
P _η	ηth optional remanufacturing process plan
$P η ?$	Optimal remanufacturing process plan
p _ηd	dth quality requirement parameter value of the optional remanufacturing process plan
Q and Q _l	Damage score
R and R _d	Standard value of quality requirement parameters
R ^{N× M}	N× M-dimensional real number space
r _a and r _b	Field radius
U and U ^{( t)}	Membership matrix
u _ki	Fuzzy membership degree
V and V ^{( t)}	Clustering center matrix
V _k ^*	Best clustering center matrix
V ⁽⁰⁾	Initial clustering center vector
$V ~ k ?$	Clustering center closest to $V k ?$
v _ki	Clustering center
$v k N ?$	Best clustering center
X	Used component sample
$X k ?$	Location of the initial clustering center of the kth sample
X _i	ith used component
X _?i	ith used component with type ?

x _ij	Attribute value of X _i (including damage score and parameter value of quality requirement)
x _?ij	jth attribute value of X _?i
y _s	Estimates of the remanufacturing time and cost
$y s k o$	Output value of the second layer of consequent network
y _η1	Remanufacturing time of the ηth process plan (h)
y _η2	Remanufacturing cost of the ηth process plan (CNY)
Z ^*	Minimum comprehensive objective value
Z _η	Comprehensive objective value of the remanufacturing time and cost
$α k o$	Applicability of each rule to x _ij
$α ˉ k o$	Normalized value of the applicability of each rule
β	Network learning rate
γ	Overlap coefficient
δ	Corrosion depth (mm)
ε	Permissible tolerance
η	Optional remanufacturing process plan number
θ	Crack length (mm)
μ ^k	Fuzzy membership degree matrix
$μ N k$	Fuzzy membership degree
$ρ k o j s$	Connection weight
σ _k	Width of the Gaussian function
φ _k	Gaussian function center matrix
φ _kN	Gaussian function center
?	Diameter of the machine tool spindle (mm)
$Ω$	Domain matrix
ω ₁	Weight value of remanufacturing time
ω ₂	Weight value of remanufacturing cost
?	Component type number
χ	Wear amount (mm)
?	Bending amount

1	J Kurilova-Palisaitiene, E Sundin, B Poksinska. Remanufacturing challenges and possible lean improvements. Journal of Cleaner Production, 2018, 172 : 3225– 3236 https://doi.org/10.1016/j.jclepro.2017.11.023
2	S K Jena. Remanufacturing for the circular economy: Study and evaluation of critical factors. Resources, Conservation and Recycling, 2020, 156( 1): 104681–
3	H N Ismail, P Zwolinski, G Mandil. Decision-making system for designing products and production systems for remanufacturing activities. Procedia CIRP, 2017, 61 : 212– 217 https://doi.org/10.1016/j.procir.2016.11.231
4	R Subramoniam, D Huisingh, R B Chinnam. Remanufacturing for the automotive aftermarket-strategic factors: Literature review and future research needs. Journal of Cleaner Production, 2009, 17( 13): 1163– 1174 https://doi.org/10.1016/j.jclepro.2009.03.004
5	Y Du, Y Zheng, G Wu. Decision-making method of heavy-duty machine tool remanufacturing based on AHP-entropy weight and extension theory. Journal of Cleaner Production, 2020, 252 : 119607– https://doi.org/10.1016/j.jclepro.2019.119607
6	A Tighazoui, S Turki, C Sauvey. Optimal design of a manufacturing-remanufacturing-transport system within a reverse logistics chain. International Journal of Advanced Manufacturing, 2019, 101( 5‒8): 1773– 1791 https://doi.org/10.1007/s00170-018-2945-2
7	V T Le, H Paris, G Mandil. Process planning for combined additive and subtractive manufacturing technologies in a remanufacturing context. Journal of Manufacturing Systems, 2017, 44( 1): 243– 254 https://doi.org/10.1016/j.jmsy.2017.06.003
8	G Tian, H Zhang, Y Feng. Operation patterns analysis of automotive components remanufacturing industry development in China. Journal of Cleaner Production, 2017, 164 : 1363– 1375 https://doi.org/10.1016/j.jclepro.2017.07.028
9	W L Ijomah, C A Mcmahon, G P Hammond. Development of design for remanufacturing guidelines to support sustainable manufacturing. Robotics and Computer-integrated Manufacturing, 2007, 23( 6): 712– 719 https://doi.org/10.1016/j.rcim.2007.02.017
10	P Goodall, E Rosamond, J Harding. A review of the state of the art in tools and techniques used to evaluate remanufacturing feasibility. Journal of Cleaner Production, 2014, 81( 7): 1– 15 https://doi.org/10.1016/j.jclepro.2014.06.014
11	R Zhang, S Ong, A Y C Nee. A simulation-based genetic algorithm approach for remanufacturing process planning and scheduling. Applied Soft Computing, 2015, 37 : 521– 532 https://doi.org/10.1016/j.asoc.2015.08.051
12	G Tian, M Zhou, P Li. Disassembly sequence planning considering fuzzy component quality and varying operational cost. IEEE Transactions on Automation Science and Engineering, 2018, 15( 2): 748– 760 https://doi.org/10.1109/TASE.2017.2690802
13	İ Yanıkoğlu, M Denizel. The value of quality grading in remanufacturing under quality level uncertainty. International Journal of Production Research, 2021, 59( 3): 839– 859 https://doi.org/10.1080/00207543.2020.1711983
14	L Cui, K J Wu, M L Tseng. Selecting a remanufacturing quality strategy based on consumer preferences. Journal of Cleaner Production, 2017, 161 : 1308– 1316 https://doi.org/10.1016/j.jclepro.2017.03.056
15	J Um, M Rauch, J Y Hascoët. STEP-NC compliant process planning of additive manufacturing: Remanufacturing. International Journal of Advanced Manufacturing Technology, 2017, 88( 5‒8): 1215– 1230 https://doi.org/10.1007/s00170-016-8791-1
16	H K Aksoy, S M Gupta. Optimal management of remanufacturing systems with server vacations. International Journal of Advanced Manufacturing Technology, 2011, 54( 9‒12): 1199– 1218 https://doi.org/10.1007/s00170-010-3001-z
17	Z Jiang, T Zhou, H Zhang. Reliability and cost optimization for remanufacturing process planning. Journal of Cleaner Production, 2016, 135 : 1602– 1610 https://doi.org/10.1016/j.jclepro.2015.11.037
18	M Denizel, M Ferguson, G G C Souza. Multiperiod remanufacturing planning with uncertain quality of inputs. IEEE Transactions on Engineering Management, 2010, 57( 3): 394– 404 https://doi.org/10.1109/TEM.2009.2024506
19	R H Teunter, S D P Flapper. Optimal core acquisition and remanufacturing policies under uncertain core quality fractions. European Journal of Operational Research, 2011, 210( 2): 241– 248 https://doi.org/10.1016/j.ejor.2010.06.015
20	A Shakourloo. A multi-objective stochastic goal programming model for more efficient remanufacturing process. International Journal of Advanced Manufacturing Technology, 2017, 91( 1‒4): 1007– 1021 https://doi.org/10.1007/s00170-016-9779-6
21	V T Le, H Paris, G Mandil. Process planning for combined additive and subtractive manufacturing technologies in a remanufacturing context. Journal of Manufacturing Systems, 2017, 44( 1): 243– 254 https://doi.org/10.1016/j.jmsy.2017.06.003
22	Y He, C Hao, Y Wang. An ontology-based method of knowledge modelling for remanufacturing process planning. Journal of Cleaner Production, 2020, 258 : 120952– https://doi.org/10.1016/j.jclepro.2020.120952
23	Z Jiang, T Zhou, H Zhang. Reliability and cost optimization for remanufacturing process planning. Journal of Cleaner Production, 2016, 135 : 1602– 1610 https://doi.org/10.1016/j.jclepro.2015.11.037
24	Li S, Zhang H, Yan W, et al. A hybrid method of blockchain and case-based reasoning for remanufacturing process planning. Journal of Intelligent Manufacturing, 2021, 32(5): 1389−1399
25	D Chen, Z Jiang, S Zhu. A knowledge-based method for eco-efficiency upgrading of remanufacturing process planning. International Journal of Advanced Manufacturing Technology, 2020, 108( 4): 1153– 1162 https://doi.org/10.1007/s00170-020-05025-2
26	B Zhao, Y Ren, D Gao. Prediction of service life of large centrifugal compressor remanufactured impeller based on clustering rough set and fuzzy Bandelet neural network. Applied Soft Computing, 2019, 78 : 132– 140 https://doi.org/10.1016/j.asoc.2019.02.018
27	S Hou, J Fei, C Chen. Finite-time adaptive fuzzy-neural-network control of active power filter. IEEE Transactions on Power Electronics, 2019, 34( 10): 10298– 10313 https://doi.org/10.1109/TPEL.2019.2893618
28	G Tian, N Hao, M Zhou. Fuzzy grey Choquet integral for evaluation of multicriteria decision making problems with interactive and qualitative indices. IEEE Transactions on Systems, Man, and Cybernetics. Systems, 2021, 51( 3): 1855– 1868 https://doi.org/10.1109/TSMC.2019.2906635
29	A Yazdani-Chamzini, M Razani, S H Yakhchali. Developing a fuzzy model based on subtractive clustering for road header performance prediction. Automation in Construction, 2013, 35 : 111– 120 https://doi.org/10.1016/j.autcon.2013.04.001
30	F Bu. An efficient fuzzy c-means approach based on canonical polyadic decomposition for clustering big data in IoT. Future Generation Computer Systems, 2018, 88 : 675– 682 https://doi.org/10.1016/j.future.2018.04.045
31	S Javadi, M Rameez, M Dahl. Vehicle classification based on multiple fuzzy c-means clustering using dimensions and speed features. Procedia Computer Science, 2018, 126 : 1344– 1350 https://doi.org/10.1016/j.procs.2018.08.085
32	N N Nguyen, W J Zhou, C Quek. GSETSK: A generic self-evolving TSK fuzzy neural network with a novel Hebbian-based rule reduction approach. Applied Soft Computing, 2015, 35 : 29– 42 https://doi.org/10.1016/j.asoc.2015.06.008
33	S Zeng, X Tong, N Sang. Study on multi-center fuzzy C-means algorithm based on transitive closure and spectral clustering. Applied Soft Computing, 2014, 16 : 89– 101 https://doi.org/10.1016/j.asoc.2013.11.020

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