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Accepted Manuscript Reliability and Cost Optimization for Remanufacturing Process Planning Zhigang Jiang, Tingting Zhou, Hua Zhang, Yan Wang, Huajun Cao, Guangdong Tian PII:

S0959-6526(15)01677-7

DOI:

10.1016/j.jclepro.2015.11.037

Reference:

JCLP 6406

To appear in:

Journal of Cleaner Production

Received Date: 18 March 2015 Revised Date:

3 September 2015

Accepted Date: 16 November 2015

Please cite this article as: Jiang Z, Zhou T, Zhang H, Wang Y, Cao H, Tian G, Reliability and Cost Optimization for Remanufacturing Process Planning, Journal of Cleaner Production (2016), doi: 10.1016/ j.jclepro.2015.11.037. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Reliability and Cost Optimization for Remanufacturing Process Planning Zhigang Jiang1, Tingting Zhou1, Hua Zhang1, Yan Wang2, Huajun Cao3, Guangdong Tian4 College of Machinery and Automation, Wuhan University of Science & Technology, Wuhan 430081, China 2 Department of Computing, Engineering and Mathematics, University of Brighton, Brighton BN2 4GJ, United Kingdom 3 State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China 4 Transportation College, Northeast Forestry University, Harbin 150040, China

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Abstract: Remanufacturing is a practice of growing importance as it returns the end of life products back to conditions that is as good as or better than new ones. The increasingly strigent environmental legistation and economic demands has led to the rapid development of remanufacturing industry in the world. Remanufacturing is still at its infantry. One of the majoy challenges faced by remanufacturing is to guarantee the reliability of remanufactured products since they came from cores with varying condition. Process planning plays a critical role in realizing a successful remanufacturing strategy since it directly affects the success rate of remanufacturing as well as reliability and cost. To do so, this work presents an optimization method for remanufacturing process planning in which reliability and cost are taken into consideration. In this method, reliability is represented by failure rate of remanufacturing operations which is influenced by the quality of returned used products (cores), whilst process cost includes machine cost and tool cost. The multi-objective optimization problem is solved by a genetic algorithm. To assess the usefulness and practicality of the proposed method, an illustrative example is given to illustrate the proposed models and the effectiveness of the proposed algorithm. The results showed that the proposed method is effective for improving reliability and reducing cost. Keywords: Remanufacturing; Process planning; Reliability; Genetic algorithm 1.Introduction Rapid technological innovation and changes in the global market accelerate the speed of products replacement, causing an exponential increase in the production of waste, and saturating landfills (Bulmus et al. 2014). Remanufacture adds value to waste streams by returning them to working order rather than reducing them to their raw material value only (Karamouzian et al. 2011). Further, re-integrating the used products into the production process reduces disposal (and hence landfill), processing, and virgin material extraction (Li et al. 2013, Gehin et al. 2008, and Tian et al. 2014). Remanufacture returns used products to an “as good as new” functional state with warranty to match, thus releasing residual value in these components (Guide et al.

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1999). Remanufacturing represents an industrial process that reuses resources and in many cases a means to business benefits (Jiang et al.2011). The practice gives products of equal quality to conventional manufacturing for reduction up to 60% energy, 70% materials, 50% cost, 80% air pollution and is considered to be an important part of circular economy.

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Remanufacturing process involves complete disassembly of the cores, followed by cleaning, reconditioning or recovery processes like repair, rework, refurbish and replacement and reassembly and inspection (Lee et al. 2010). The prices of the remanufactured items are generally 30–40% of that of new ones (Mukherjee et al. 2009). Thus, remanufacturing not only provides economic benefits for customers, but also utilizes minimum resource and reduce waste disposal (Sundin and Bras, 2005). Despite the tremendous advantages offerred by remanufacturing, customers’s perception of remanufactured products are very low as it is often believed that the quality of remanufactured goods is inferior to that of new ones and this in turn affects their willingness to pay for these products (Hazen et al. 2012). Since quality is a broad term embracing availability and reliability aspects, which have caused many issues in remanufacturing, especially in reliability of remanufactured products (Du et al. 2014). Thus, overcoming challenge of reliability is one of the major concerns for proliferation of remanufacturing in the market. The reliability of remanufacturing requires consideration of several factors like quality issues for returned products or components, and operating processes such as disassembly and reassembly. These factors deeply affect to some the extent to the reliability of remanufactured products (Ortegon et al. 2013). While not necessarily a technical issue, the success of a remanufacturing business is yet very dependent on the acquisition of high quality used products or cores in order to satisfy the reliability for remanufactured products (Jiang et al. 2011).Thus, developing a product recovery system and estimating influence of the reverse logistics network to the reliability of remanufacturing is an initial step in remanufacturing process planning. Hatefi and Jolai (2014) proposed a reliable model for reverse logistics network based on robust optimization method to protect the network against uncertainty. Vahdani et al. (2012) developed a bi-objective mathematical programming formulation to construct a model for a reliable facility network design. Lin et al. (2010) focused on the optimal carrier selection problem based on network reliability criterion, and a GA-based algorithm is developed to determine the optimal carrier selection. Once the acquisition of returned items in the reverse logistics network is completed, the performance of the remanufacturing process including disassembly and assembly is another challenge for remanufacturing reliability. Zussman and Zhou (1999) proposed a mathematically sound disassembly Petri net that can be modified to deal with the reliability of resources performing the operations. Song and Wang (2014) analyzed the disassembly process of LCD and simulated its reliability. Suzuki et al. (2013) introduced an effective new design for quality tool that visualizes assembly fault potential based on product reliability. Jiang et al. (2010) defined the reliability of assembly connection and proposed a model to assign the assembly reliability to the parts and sub-systems. Although significant

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amount of research has been conducted in reliability related to the quality of used cores, disassembly and assembly, not much effort is reported reliability on the impact of remanufacturing process plan optimization to reliability.

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Remanufacturing process planning is a vital step in remanufacturing which provide a pivotal link between used products and remanufactured products. It is focused on specifying the machines, operations, operation sequences, tools required to recovery the used products as good as new ones (Yin et al. 2014). Due to its significant important of remanufacturing process planning, a great deal of research related to it has been undertaken. Kernbaum et al. (2009) presented a mixed integer programming approach to optimize remanufacturing process plan in terms of cleaning, injecting, disassembly, repairing and reassembly. Parkinson and Thompson (2004) put out a systematic approach for the planning and execution of product remanufacturing based upon the failure mode and effect analysis method. Li et al. (2011) adopted a Graphical Evaluation and Review Technique method for remanufacturing process plan decisions, which considered the uncertainty in the quality of incoming used components. Li et al. (2013) established remanufacturing process model based on a fuzzy Petri net in view of the uncertainties in remanufacturing process routing and process time. Undoubtedly, these studies provided a very useful reference for remanufacturing process planning. However, there are relatively few studies considering the reliability of remanufacturing due to the uncertainty of returned used products. High reliability of remanufacturing is a vital target for the process planning to perform remanufacturing operations, the parameters of these operations and the order in which they will be executed (Kin et al. 2014).

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In addition to the reliability, the cost in remanufacturing is another criterion that should be taken into consideration for process planning. High initial investment to build up the infrastructure and equipment for carrying out remanufacturing is required when starting a remanufacturing business. Remanufacturers can go for this only if they can make profits from the remanufacturing activities (Sabharwal and Garg, 2014). Thus, another key challenge of remanufacturing process planning is to optimize a process plan by guaranteeing the reliability of remanufacturing whist keeping the cost at minimum. With respects to the goal of improving reliability and reducing process cost, this paper presents a novel optimization method for remanufacturing process planning integrating these two objectives for the first time to the best knowledge of the authors. The method has two principal characteristics: (1) establishing the reliability and cost model of remanufacturing process planning by taking the faults of returned products into consideration; and (2) genetic algorithm (GA) is used to solve the optimization problem of remanufacturing process planning. Compared to other optimization method, GA mimics the process of natural evolution to optimize multi-dimensional nonlinear problems, and has been reported by many researchers. For instance, Ismail et al. (2008) devised and employed two modified genetic algorithms to provide the

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best approximate process planning solution. Fan et al. (2012) established a multi-objective optimization function and applied GA for process routing decision. Wang et al. (2004) developed an optimization method for process routing by using GA to determine the operation sequence with consideration of the sequence constraints. Thus, GA is a well-recognized multi objective optimization method and it is also feasible to be used to select and order the alternative operations in remanufacturing process planning. The most important aspect in this optimization method is that it considers the entire reliability and cost problem from the faults of returned products. The incorporation of faults impacts into process planning certainly will provide more efficient solutions for remanufacturing.

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2.The proposed framework of remanufacturing process planning The goal of the proposed strategy for remanufacturing process planning is to identify optimal remanufacturing process plan in dynamic environments. In seeking an optimal solution, it is proposed to employ a multi-criteria optimization method to achieve the goal of improving reliability and reducing cost of remanufacturing process. The proposed optimization method is divided into three sequential phrases: (1) characteristic framework of remanufacturing process planning; (2) evaluation criterion for remanufacturing process planning; (3) optimization model of remanufacturing process planning based on GA.

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2.1 Characteristic framework of remanufacturing process planning In remanufacturing process planning, the returned (cores) are first disassembled into individual parts the quality of which are subsequently inspected and evaluated. Since the parts have different quality levels which are infected by various damages (wear, corrosion, fatigue, etc.) at different level (slight, medium and serious) and varying location. In responding to the type and level of damage of the parts that needs recovery, a variety of operations, e.g., material addition (welding, thermal spraying, etc.), material removal (machining, laser cutting, etc.), and surface treating (heat treating, anodizing, etc.) are employed. For instance, if a used lathe guide is in slight wear condition, chrome plating may be used for the recovery whist moderate wear condition, machining operationss may be employed. The relationship between faults and its corresponding recovering process operations is depicted in Figure.1.

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Recovery operations required

Disassembly and Inspection

Additive operation P

The returned products P

C1

A2

A1

A1

Chromium plating

Grinding

A2

Arc welding

Turnning

B1

Cold welding

Grinding

C1

Laster cladding

Grinding

C2

Thermal spraying

Turnning

C

B

C2

B1

A1: Wear, Slight. A2:Wear, Medium B1:Fatigue crack, Slight. C1:Corrosion, Slight C2::Corrosion, Medium.

The divers condition of the remanufacturing conponents

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A

Remove operation

The alternative remanufacturing process routs The depletion of

The reliability of remanufacturing process

machines and tools

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Figure.1 The relationship framework of different cores with different faults and the corresponding remanufacturing process operations

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In fact, the faults of the cores strongly affects the type, sequence and time of remanufacturing processes, resulting in various remanufacturing process plans, which in turn have great influences on reliability and cost. Reliability of remanufacturing process is the ability to accomplish recovery mission of returned products in the specified processing time and condition (Zhang and Liang, 2013). The processing time and condition are mainly determined by the degradation failure rate of machine (lathe, tools, etc.). However, the machining resources decision are subject to process planning, which cannot be specified until operation sequencing for recovery the returned cores is performed (Huang et al. 2012). In addition, the cost for each process plan is different due to the alternative operators and machines which affected by the returned cores in different failure attribution.

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2.2 Evaluation criterion of remanufacturing process planning Remanufacturing process planning has different consideration for different objects. For a used core, a process plan provides detailed information about the selection of remanufacturing operations and resources to guarantee the reliability of remanufactured parts. For a remanufacturer, a process plan is a method to return a used product to an as-good-as condition at a price that is significantly less than a new product. Thus, the criterion of max reliability and minimum production cost is adopted for remanufacturing process planning. 2.2.1 Reliability model For the remanufactured products, reliability is a key concern of customers. The remanufactured products must be of high reliability and quality to meet the same specifications of new products in the market. As the reliability model of remanufacturing process is a series system, the processing resources (machine tools, cutting tools, etc) of the remanufacturing process system are sequential and the failure of each process is independent (Tavakkoli-Moghaddam

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et al. 2008).The failure rate of procedure obeys index distribution (He and Shen, 2014). Thus the reliability for the procedure k can be calculated as follows:   =   ∙ (1) Where   is the failure rate for processing resources in time t. For the faults of returned cores can range from minor scratches to extensive damage, the quality deviations of returned components may result in the increasing depletion of machines and tools. Thus,   can be formulated as follows: (2)   =   +  −     Where   is the initial failure rate,  is the evaluated quality of the returned cores,  is the desire quality of the returned cores for the remanufacturer, and  is the correction factor.

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In order to calculate the reliability of remanufacturing process, the quality evaluation of returned cores may be conducted firstly. The quality of returned components is affected by the faults F =  ,  , ⋯ ,  , ⋯ ,  . The type of faults can be classified as the inside defect, dimension, surface roughness, geometric tolerance and non-working surface damage and so on (Jones et al. 1997, Zhou et al., 2012). These faults can present different quality levels by the types, degree and location of damage. The influence of the each faulty on the reliability is presented by a method of weight. To obtain the weight of each fault, entropy weight method is used. The main steps of the entropy weight method include the formation and standardization of the evaluation matrix, and the calculation of the entropy and the entropy weight. a. The formation of the evaluation matrix. The number of faults is , the number of marking criteria is m, and then a matrix A = #$ %× , #$ is the influence value of the i() type of fault on the marking criteria j to the quality of returned cores. The degree of damage can be represented quantitatively by a number between 1 to 10 as shown in Table 1. Among these values, the bigger it is, the more influence it is on the quality. If the influence value is 10, the quality of returned core is extremely low, and may not be suitable for remanufacturing.

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Table 1 Quantitative values assigned to attributes

Qualitative measure of attributes Exceptionally low

1

Extremely low

2

Very low

3

Below average

4

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Assigned value of #$

Average

5

Above average

6

High

7

Very high

8

Extremely high

9

Exceptionally high

10

The standardization of the evaluation matrix. The matrix A = #$ %× , is normalized to form a new matrix P = ,$ %× via the Equation (3).

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r$ = c.

%./0.12 3 .12

(3)

%./0.12 3 %0.12 3

The calculation of the entropy. For marking element j, the entropy value for i() type of fault is defined as follows: h$ = −5 ∑$: 7$ ln 7$

(4)

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Where k = 1⁄ln , 7$ = >?@ ⁄∑@=1 >?@ , and when 7$ = 0, 7$ ln 7$ = 0.

The calculation of the entropy and the entropy weight. The weight for marking criterion j can be calculated as follows:  C2

ω$ = % ∑D

1EF C2

00 ≤ ω$ ≤ 1, ∑% : ω$ = 13

(5)

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H = ∑% : #$ I$  H$ = ∑$: #$ I

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The weight of fault type ω$ , can be calculated based on the entropy method, the comprehensive evaluation for the i() type of fault and the j() marking criterion are calculated by Equations. (6) and (7) respectively: (6) (7)

The weight of the faults comprehensive impact on the quality of returned core is calculated as follows:  = ∑: H I ⋅ ∑% $: H$ I$

(8)

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Similarly, the desired quality  of returned cores can be calculated. The reliability of the procedure k can be calculated by Equations. (1)- (8), and is shown as follows: R   = L,−M  + N   −   O ⋅  (9) Then, the reliability of the remanufacturing process can be calculated as follows R   = ∏Q (10) :   Where N is the total number of remanufacturing process operation.

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2.2.2 Cost model The total cost of remanufacturing process planning contains the expense of machine tools and cutting tools, the cost for machine tools and cutting tools transformed, labor cost and so on. In order to illuminate the influence from the faults of returned products to cost in the process plan, the expense of machine tools and cutting tools is a key consideration in this work. (1) The total cost of machine (TMC) in remanufacturing process can be written as follows: Y TMC = ∑Q (11) : VWX ×  Y Where VWX is the machine cost for processing a core per unit time; and  is the machine time for machine to finish the i() process. (2) The total cost of tool (TTC) in remanufacturing process can be expressed as follows: Y (12) TTC = ∑Q : ZWX ×  Where ZWX is the tool cost for processing a core per unit time. However, the processing time  Y for the i() process is largely depended on the quality of returned components. The different quality between returned products and the ideal recovery components determine the processing time. Let the rated time for

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machine and tool in i() process be t  , and the relationship between processing time and returned products satisfies the following function:  Y =  + [ −   (13) Where η is a certain coefficient which can be achieved by the experimental method.  is the evaluated quality of the returned core, and  is the ideal quality of the remanufactured core. Thus, the total cost of the remanufacturing process can be calculated as follows: C = ∑Q :VWX + ZWX  ⋅ M + [ −  O

(14)

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2.3 The optimization model for remanufacturing process planning based on GA. The proposed remanufacturing process planning method is divided into three steps, as shown in Figure 2: failure feature identification in which the fault set is identified by analyzing the returned components, operation generation where feasible operations is identified, and process plan generation based on GA, in which multi-objectives (reliability and cost) is used to search for the process plan for the maximization fitness function value. Used product

Failure type

Failure location

Failure degree

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Identify the failure attribution of used product

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Alternative remanufacturing operations

GA operators

The fitness function

Initial process plan

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Alternative operations

Figure.2 The optimization steps of remanufacturing process planning

Step 1: Identification of faults of the used component. Major faults in the components are identified to obtain the fault set 7 , 7 , 7] , ⋯ , 7  which is mapped with remanufacturing operations based on the type, degree and location of damages. Step 2: Determination of the alternative remanufacturing operations. According to the previous definition of the fault, a part can be decommissioned due to a set of faults. If these attributions can be recovered by sequence of operation, the part

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remanufacturing would be fully completed. Based on the characteristics of i() failure attribution, a set of alternative remanufacturing operation set ^, , ^, , ⋯ , ^,%  can be determined.

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Step 3: Generate a process plan using GA. Owing to the faults of used components and the various choices for process, remanufacturing process plan is a NP-hard (Non-deterministic polynomial hard) problem with the objective to improve the reliability of remanufactured products and reduce the process cost. A process plan will be generated based on GA that utilizes a function with multiple objectives.

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A representation scheme based on the faults of cores, with corresponding genetic operations and fitness evaluation functions is described in the following subsections.

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(1) Encoding. The generation of remanufacturing process and the search ability for GA is deeply influenced by solution encoding. In GA, chromosomes is very important for the representation of the attribution of remanufacturing process plan. In this paper, the attribution of remanufacturing process includes the faults of a used component, the operations selected to recover faults and selection of machines and tools for each operation, thus the gene encoding is shown in Table 2. Table 2 the gene encode Operation number

1 Crack

1Cold welding

1 Crack 2 Wear

Machine number

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Fault number

Tool number

Gene

M1

1110

2Grinding

M2

T2

1222

3Grinding

M2

T1

2321

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(2) Initial population. After encoding, the initial population is set using random solutions to search for any solution in a search space. However, the process plan cannot be generated randomly due to some constraints, e.g., the degree of the faults, the operation sequence. When generating the candidate process plan, the constraints should be met, as shown in Figure.3.

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Set the initial population size N

N

Satisfy the precedence relationship of remanufacturing process Y

Add to the initial population

Y

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Randomly generated chromosomes

Figure. 3 Algorithm flow of initial population

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(3) Fitness function. The fitness function is used to select the best individuals. The remanufacturing process planning optimization problem carries out two objective functions. The objective optimization of remanufacturing process planning is to maximize reliability Rx and minimize cost Cx. Assuming that, at the k () generation, across the entire population of solution points, find the minimum cost C``% and maximum reliability R ``%./ , and the reliability and cost for chromosome j are RL $ and CL $ . A selection function for chromosome is formulated as follows: 7 x$ = R ``%./ − RL$ , RL$ < R ``%./ (15) 7 x$ = CL $ − C``% , CL $ > C``% (16) The fitness value for chromosome j can be calculated as follows: FL  =  7 x$ +  7 x$ (17)   Where  ,  ∈ 0,1 ,  +  = 1. The fitness function can be formulated as follows: gx = minFx (18) (4) Genetic operators. The design of appropriate genetic operators, including the reproduction, crossover, and mutation, plays a crucial role in the successful implementation of GA (Sardinas et al. 2006). Reproduction is used to guarantee the selection of optimal individual. In this operator, “elitism” strategy is applied by copping the best individual of the population (the one with highest fitness value) to the next generation and then the “roulette wheel” method is applied to the remain strings. Crossover contains reproduction procedure from parent’s chromosome, which is essential in GA. Crossover is applied, at a given probability, to create new generations resulting from two parent strings exchanged randomly between the two crossover points. For example, two strings are selected in Table 3.There are two crossover sites: x = 3, y = 7. An offspring child chromosome is generated based on crossover operation, as shown

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in Table 4. The mutation operator makes random changes to one or more elements of the string. Mutation operator randomly modifies two or more elements to obtain a new string with a value between 0.001-0.1. If the string is feasible, it will appear in future generations. Table.3 Two viable chromosomes of remanufacturing process 2,5,3,0

2,6,2,0

1,1,1,0

1,2,2,3

3,7,2,1

Chromosome one 2,3,2,2

2,4,3,0

3,10,2,3

3,11,1,0

3,9,2,3

1,1,1,0

Chromosome two Table 4 An offspring child chromosome 2,3,2,1

2,5,3,0

3,10,2,3

3,11,1,0

3,9,2,3

1,1,1,0

3,8,4,1

3,9,2,2

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2,3,2,1

1,2,2,4

0,0,0,0

3,8,4,1

3,9,2,2

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Based on the above method, the optimal remanufacturing process plan will be obtained. The process planning optimization method in this paper is obtained based on the reliability and cost functions. The precious data about the reliability and cost of process impacted by the quality of used cores can be obtained by the function which can guide remanufacturer in making better decisions.

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4. Case study This section describes the reliability and cost optimization of remanufacturing process planning for a lathe bed based on GA. The lathe bed, as a typical electromechanical product, is of great potential for remanufacturing. A remanufactured lathe may cost only 40%-60% of that of a new lathe whist offering better machining accuracy and productin efficient (Du et al. 2013, Zhou et al. 2014). To illustrate the proposed method, the remanufacturing process planning for a used C6132 lathe bed is presented.

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4.1 Faults Inspection of lathe bed According to real circumstances, the repairable components of C6132 lathe bed include guide, saddle and spindle. The lathe bed is fault product because of some defects in guide, saddle and spindle. The location, degree and type of these defects make it possible to have different remanufacturing operations. Thus, inspection is the first step to assess the quality of the returned cores. The inspection results of guide, saddle and spindle are shown in Table 5. Table 5 The inspection results of lathe bed

Components

Guide

Faults’ Type

Inside defect

Damage

crack

saddle

Spindle

Surface

Non-working

roughness

surface damage

wear On the surface

corrosion Outside the

Location

On the surface

Inspect result

0.05mm

1.2mm

0.2mm depth

Degree

slight

medium

medium

surface

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Table 6 The alternative operators of lathe bed Faults

Op1

Op2

Crack

Cold welding 1

Grinding 2

Wear

Grinding 3

Op3

electrodeposited chromium 4

3-4

Thermal spraying 8

Grinding 10

Cold welding 11

Fine Grinding 6

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Milling 7

process 1-2

Laser cladding 5 Corrosion

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4.2 Determination of alternative operations From the defect inspection for the lathe bed in Table 5, crack, wear and corrosion are the main faults to be considered. Based on the remanufacturing requirements, the alternative operations to recovery the faults are shown in Table 6. The available machine and available tool information related to the operators are given in Tables 7 and 8. Based on international field study, the process information for lathe bed in this remanufacturing enterprise is shown in Table 9.

Milling 9

7-8-9 10-11-9

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Table 7 Available machine information Number

Machine Type

MCI

M1

Electrospark welding machine

10

M2

Guideway grinder

40

M3

Chrome-plating equipment

35

M4

CNC vertical miller

25

Vertical miller

20

M6

Semiconductor laser cladding equipment

40

M7

Plasma spraying machine

50

M8

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M5

Arc thermal spraying equipment

30

Table 8 Available tool information

Number

Tool Type

Welding tool

8

T2

Grinding wheel

20

T3

Guideway grinder

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T1

TCI

15

sharpener Milling tool 1

10

T5

Milling tool 2

20

T6

Milling tool 3

10

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Table 9 The alternative machines, cutting tools, the initial failure rate and process time of each process stage

Fault

Operation

for lathe bed remanufacturing

λ0 ×10-3

1Cold welding

4.0

2 Grinding

3.0

1 Crack

M&T

Time /min

Fault

M1T1

9.4

3

M2T2

5.8

Corrosion

M2T3

7.2

Operation

7

Milling

λ0 ×10-3

3.6

M&T

Time /min

M4T4

13.2

M4T5

10.8

M5T5

14.3

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3 Grinding

10.4

M2T3

9.8 8Thermal spraying

M5T6

17.2

M7

15.9

5.0

chromium 5 Laser cladding

7.0

Wear

3.8

M3

4.6

M8

12.8

M6

3.9

M4T4

15.5

M7

5.8

M4T5

13.6

M5T5

22.3

M5T6

16.8

M2T2

14.7

M2T3

12.8

M1T1

8.9

M2T2

11.7

M2T3

9.5

M4T4

6.8

M4T5

7.6

9 Milling

10 Grinding 11 Cold welding

6.0

4.5

3.4

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6 Fine Grinding

2.8

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4Eletrodeposited

2

M2T2 2.5

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4.3 Quality evaluation for the lathe bed The alternative recovery operation will be selected based on the reliability and cost model by using genetic algorithm. However, the reliability and cost of remanufacturing process are influenced by the quality of used components and the machine that is used to perform the recovery operations. The quality of lathe bed and the desire quality of returned lathe bed are evaluated by entropy weight method, and the scores are in Tables 10 and 11 which can be obtained from Table 1 based on the evaluation of experts.. Table 10 The score for returned lathe bed Type of faults

Damage

Degree

Location

9

4

2

Surface roughness

4

8

1

Non-working surface damage

3

5

3

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Inside defect

Table 11 The score for desire returned lathe bed Damage

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Type of faults

Degree

Location

3

2

2

Surface roughness

3

5

1

Non-working surface damage

2

3

3

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Inside defect

The number of quality factor is 3, and the number of marking criteria is 3, i.e., damage, degree, location, and then the evaluation matrix A = #$ ]×] can be obtained. The evaluation matrix A = #$ ]×] is normalized to obtain a matrix P = ,$ ]×] . The results of weight calculation for faults and marking criteria are shown in Table 12. Table.12 The weight of quality factor and marking criteria weight of faults

Type

Inside defect

Surface roughness

weight of marking criteria Non-working surface damage

Damage

Degree

Location

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Returned lathe bed

0.23

0.23

0.43

0.27

0.48

0.25

0.39

0.34

0.27

0.40

0.32

0.27

Desire returned lathe bed

4.4 Generation of the process plan based on GA.

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For the lathe bed remanufacturing, the correction factor α = 0.001, η = 0.015. Then, the quality for lathe bed, m , and the desired quality, m , can be calculated by Equation. (7), m = 14.38, m = 7.24.

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4.4.1 Encoding. Based on the above information, the gene can be coded as (1,1,1,1) ,

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which represents that the fault of crack (1) is recovered by cold welding (1). The selected machine and tool related to the cold welding are electrospark welding machine (1) and welding tool (1). 4.4.2 Initial population. The lathe bed contains three components, so the operations are divided into three groups which are (1,2) (3,4) or (3,5,6 ) (7,8,9) or (10,11,9). For the each group, the operations sequence cannot be changed, for example, the operation 2 must follow the operation 1 in group (1,2).This relationship of procedures in remanufacturing process constrains the initial population generated randomly.

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4.4.3 Fitness function. Fitness function is an evaluation for the represented solution in remanufacturing process planning optimization, which has been calculated in Eq.17. However, the weight distribution for reliability and cost is variable from different remanufacturing enterprises constrained by their capability in production and technology. For the case study the main focus for enterprise is to improve the reliability. Thus, the weight of reliability (7 ) is 0.65, and the weight of cost (7 ) is 0.35, the fitness function is calculated as bellow: Fx = min M0.657 x$ + 0.357 x$ O (19) 4.4.4 Genetic operators. The genetic algorithm operator of the chromosome reproduction, crossover, and mutation is used to generate optimal remanufacturing process plan. The parameters for GA are set as follows: iteration number is 200, initial population size N is 100, and crossover and mutation probabilities are 0.85 and 0.10, respectively. After finishing the optimization steps, the optimal operation sequence can be obtained as shown in Table 13, and the information for this process plan is shown in Table 14. It is worth to noting that the process plan selected for the lathe bed is incredibly close to the optimal solution and appropriate for the given remanufacturing factory. The used lathe before remanufacturing is shown in Fig.7. The guide ( Part model: C6132D, Size: 2300mm×490mm

is remanufactured through cold welding and grinding.

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Comparison chart between before and after repair for the lathe guide is shown in Fig.6. Saddle (Part model:C6132A1, Size:645mm × 615mm

is remanufactured using

Table 13 The optimal operation sequence 1,1,1,1

1,2,2,2

3,9,5,6

3,10,2,3

3,11,1,1

2,3,2,3

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grinding, laser cladding and fine grinding to recover wear. Spindle is remanufactured using the milling, grinding and cold welding. The used lathe is restored into a new remanufactured lathe with the similar even better performance, as shown in Fig. 8. 2,5,6,0

2,6,4,4

Table.14 The information of the optimal remanufacturing process plan

1 2

Component Guide

3

Crack

1

Cold welding

Crack

2

Grinding

Corrosion

9

Milling

Machine

Tool

M1

T1

M2

T2

M5

T6

Corrosion

10

Grinding

M2

T3

5

Corrosion

11

Cold welding

M1

T1

6

Wear

3

Grinding

M2

T3

Wear

5

Laser cladding

M6

Wear

6

Fine Grinding

M4

7

Spindle

Operation

Saddle

T4

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8

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4

Damage

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Order

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Fig.6 Comparison chart between before and after repair for the lathe guide

Fig.7 The used Lathe before remanufacturing

Fig.8 The remanufactured new lathe

To reveal the performance of the optimization model, the lathe beds remanufactured by two different process plans are compared with that of new ones as shown in Table 15. Scheme 1 is the used C6132 lathe bed remanufactured by the process plan which

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generated with the optimization model. Scheme 2 is the used C6132 lathe bed remanufactured by traditional process plan without considering the reliability. Table 15 Comparison of the remanufactured lathe with the standard of new lathe New C6132

Remanufactured C6132

Remanufactured C6132

value

lathe bed

lathe bed by scheme 1

lathe bed by scheme 2

Roundness(mm)

0.0085

0.0082

0.0080

Flatness(mm)

0.012

0.010

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Accuracy

Items

0.008

Pitch error

0.030

0.028

Surface hardness

55

52

Surface parallelism

0.030

0.026

X axis

0.015

0.012

Y axis

0.020

0.018

0.015

20000

6500

8000

positioning from feed (mm) Cost

50

0.025

0.010

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Repeatability of

0.026

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Obviously, the optimization model has demonstrated significant improvement in terms of reliability and cost of the used C6132 lathe bed remanufacturing. The proposed method can be used to obtain the optimal process route for remanufacturing.

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5. Conclusion This work presented a dual-objective remanufacturing process optimization method to maximize the reliability and minimize process cost for the first time. The critical optimization objective of reliability enhancement in the model is evaluated by the failure rate of process and capability decay of machines and tools which are affected by the quality of returned products. The classical optimization objective in remanufacturing process such as cost is also integrated in the model. For this multi-objective optimization problem, genetic algorithm is used to solve the model. The case study with remanufacturing process planning optimization for the lathe bed is presented to evaluate the feasibility of the model. The results showed that it is more efficient for remanufacturing to improve the reliability and reduce process cost by integrating the quality evaluation of returned products to establish a remanufacturing process model. The work presented herein not only provides a tool to optimize the remanufacturing process planning, but it also provides decision support for high reliability remanufacturing. It may be used for the selection of remanufacturing process plan that is reliable and economical. In addition, future study may be focused on a general framework model integrated the environmental issue in remanufacturing process planning optimization. The research work in this area is expected to be one of the key areas of remanufacturing research in the near future.

Acknowledgements The work described in this paper was supported by the National Natural Science

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Foundation of China (51205295, 71471143, 51405075), Wuhan Youth Chenguang Program of Science and Technology 2014070404010214 , A1202 supported by Science Foundation of Wuhan University of Science and Technology, and Funds for International Cooperation and Exchange of the National Natural Science Foundation of China (51561125002). These financial contributions are gratefully acknowledged.

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