Identification of product's design characteristics for remanufacturing using failure modes feedback and quality function deployment

Journal of Cleaner Production 239 (2019) 117967

Contents lists available at ScienceDirect

Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro

Identification of product's design characteristics for remanufacturing using failure modes feedback and quality function deployment Xiufen Zhang a, b, c, *, Shuyou Zhang b, Lichun Zhang c, Junfang Xue a, Rina Sa a, b, c, Hai Liu a a

College of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot, 010051, PR China State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, Hangzhou, 310027, PR China c Canny Elevator Company Limited, Suzhou, 215213, PR China b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 3 April 2019 Received in revised form 6 August 2019 Accepted 7 August 2019 Available online 9 August 2019

Remanufacture is one of the profitable means of product disposition. Design for Remanufacture (DfRem) plays a significant role in enhancing the remanufacturability of product, which further improves the efficiency and effectiveness of remanufacturing. Identification of key design factors that influence a product's remanufacturability is a big challenge in DfRem area because of difficulty in data collection. Because the failure mode is closely related to remanufacturing, a two-phase QFD model is proposed by extending the traditional quality function deployment (QFD) frame and reusing failure modes feedback from end-of-life (EOL) product. The comprehensive list of design factors of products is developed by literature review, which includes engineering characteristics and DfRem guidelines. For the fuzzy and imprecise information, the fuzzy comprehensive evaluation method is used to determine the weight of customer requirements translated from failure modes. Furthermore, the key engineering characteristics and design guidelines are identified based on the two-stage QFD model gradually. In order to overcome the difficulty of vagueness and imprecision expression of linguistic variables in implementing the twophase QFD model, the triangle fuzzy numbers are integrated. Finally, a case study of the automotive engine crankshaft validates the feasibility of the method proposed. The main contribution of the proposed method lies in finding the DfRem improvement direction accurately based on the failure modes. © 2019 Elsevier Ltd. All rights reserved.

^ as de Handling editor: Cecilia Maria Villas Bo Almeida Keywords: Design for remanufacture (DfRem) Failure modes Feedback Design characteristics Quality function deployment (QFD) Fuzzy logic

1. Introduction Product remanufacture is one of profitable means of product disposition, both ecologically and economically, as the geometrical form of the product is retained and its associated economic and environmental value is preserved (Hatcher et al., 2011; Tchertchian et al., 2013; Ikeda, 2017). However, one of the key remanufacturing barriers is poor remanufacturability of many current products. Research has shown that the efficiency and effectiveness of the remanufacturing process greatly depend upon decisions made during the design process (Ijomah et al., 2007; Chakraborty et al., 2017). That is, products’ remanufacturability should be evaluated and improved in the design process in order to make the remanufacture of products easier and more efficient. Design for Remanufacture (DfRem) is one of the strategies which may enhance

* Corresponding author. College of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot, 010051, PR China. E-mail address: [email protected] (X. Zhang). https://doi.org/10.1016/j.jclepro.2019.117967 0959-6526/© 2019 Elsevier Ltd. All rights reserved.

reusability and remanufacturability of products. Meanwhile, DfRem was considered as one of the major drivers that influence remanufacture (Subramoniam et al., 2009). However, one of the challenges of DfRem is the identification of design factors (e.g., product characteristics, guidelines) which can be compiled to steer a design towards better remanufacturability. It is widely believed that only the product with higher remanufacturability should be completely disassembled, easily cleaned, inspected and refurbished. Many researchers identified the design factors by analyzing the relationship between different product characteristics and specific remanufacturing steps, case study or interview with experts (Sundin, 2004; Sundin and Bras, 2005; Amezquita et al., 1995; Chakraborty et al., 2017). Due to the number of alternative factors is large, it is hard and costly to consider all of these design factors in DfRem. Therefore, DfRem has been performed by considering only a few factors, e.g., materials selection (Yang et al., 2017), fasteners and joints (Shu and Flowers, 1999), or modularity (Tchertchian et al., 2013). Different products should focus on different points. There is a lack of proper method to find the key design factors more effectively.

2

X. Zhang et al. / Journal of Cleaner Production 239 (2019) 117967

The remanufacturability of product is closely related to its parts’ failure modes (Sherwood and Shu, 2000a,b). For example, a part with slight failure will have better remanufacturability than that with severity failure. In engineering practice, the research of the remanufacturability based on defect is valuable (Wang et al., 2017a). It has received increasing attention. The failure analysis and design against failure was implemented based on expert and knowledge-based systems (Graham-Jones and Mellor, 1995). The optimal remanufacturing process planning was generated based on fault feature characterization (Wang et al., 2017b). The corrosion fault judgment rules were established based on fault features (Rao et al., 1999). Hence, to address the above problem, the primary objective of this article is to identify the key product's engineering characteristics and design guidelines to support design for remanufacture (DfRem) based on failure modes feedback. In this study, an analysis of literature followed by identification of major research issues in remanufacture has been used to determine the major design factors influencing product remanufacture at its EoL phase. These design factors comprise engineering characteristics and guidelines. A two-phase QFD model is presented to translate failure modes into engineering characteristics and DfRem guidelines gradually. At the first stage of the model, the product's engineering characteristics are ranked according to the failure modes of the product. At the second stage, the certain DfRem guidelines are identified by the screening key engineering characteristics to improve the next generation product's remanufacturability. Generally, the relationships between key factors and failure modes are hard to measure with crisp numbers but always in a form of imprecise and linguistic labels. In order to overcome the difficulty of vagueness and imprecision expression of linguistic variables in implementing the QFD, the fuzzy comprehensive evaluation method is used to determine the weight of failure modes, and the triangle fuzzy numbers are integrated. The remainder of this paper is organized as follows: Section 2 discusses the general background and motivations. Section 3 illustrates the research methodology of this paper. Section 4 describes the construction of the two-phase QFD model for identification of design characteristics for DfRem. Section 5 validates the proposed method with a case study of crankshaft. And section 6 offers concluding remarks. 2. Background and motivations 2.1. Design for remanufacture (DfRem) DfRem has been a popular subject within the field of remanufacturing research (Hammond et al., 1998; Hatcher et al., 2011, 2013). The literature on DfRem can be broadly classified into two groups. Firstly, the literature is on identification of design factors. There are many researchers focused on this topic. Some of them consider that the design factors can be identified by analyzing the relationship between different product properties and specific remanufacturing steps. And some methods, such as the ‘RemPro Matrix'diagram (Sundin, 2004; Sundin and Bras, 2005), fastening and joining selection (Shu and Flowers, 1999; Jeandin and Mascle, 2016), have been presented. According to Sherwood and Shu (2000b), Williams and Shu (2000), Amezquita et al. (1995), Ijomah et al. (2007); Saavedra et al. (2013), the DfRem factors also can be deduced by case study and interview or workshop methodology. For example, the waste streams of three automotive (Sherwood and Shu 2000a,b) and Toner-Cartridge (Williams and Shu, 2000) remanufacturers were analyzed to determine factors that impede the reuse or remanufacture of parts. And Amezquita

identified the characteristics of remanufacturing through a case study of automobile door and interviewing remanufacturers and product designers (Amezquita et al., 1995). The identified design factors refer to product features and characteristic, such as ease of access, ease of separation, ease of disassembly, modular components, fasteners, and so on. These factors are named as technical factors, and those operational factors that affect the integration of DfRem into a company's design process are also important (Hatcher et al., 2013). The external and internal operational factors include management commitment, design priorities, OEM-remanufacturer relationships and designer motivation (Hatcher et al., 2014). Furthermore, Saavedra et al. (2013) identified four main remanufacturing characteristics by literature review, and they are remanufacturing operation, marketing of the remanufactured product, reverse logistics and relationship with used product suppliers. Then two case studies were conducted on the current remanufacturing scenario within the Brazilian automotive sector from the perspective of original equipment manufacturer (OEM) and independent manufacturer. The design factors also can be considered as the product design criteria and their criticality (Chakraborty et al., 2017). The detailed design factors identified from literature are expressed in Table 1. Secondly, the literature is on various methodologies and tools to improve the product's remanufacturability. Some researchers considered that the product's remanufacturability can be enhanced by improving any of the remanufacturing steps (Bras and McIntosh, 1999; Shu and Flowers, 1999; Fang et al., 2016). Among these steps disassembly and cleaning are prerequisite and unique for remanufacturing (Ikeda, 2017). Design for Disassembly (DfD) enables the removal of parts without damage and can therefore reduce the remediation process and requirements for new, replacement parts. The study on DfD has been started from the early 1990s.The research topics include disassembly sequence planning (DSP) (Zhang et al., 2014; Tseng et al., 2018), disassembly line balancing (Kalayci and Gupta, 2013), DfD methods and tool (Lee et al., 2010; Favi et al.,2016, 2019; Soh et al., 2014),etc. For example, an EOL decision model for remanufacturing options was developed to get possible design changes (Lee et al., 2010). Recently, a novel software tool, namely LeanDfD, was proposed to assess the disassemblability and recyclability of mechatronic products during the product development process (Favi et al., 2019). However, there is a consensus among many researchers that DfRem is a collection of many tasks or considerations (e.g., material type, structure, fastening and joining method, durability, company's decision, etc.). Thus, design guidelines seem to be the most efficient and effective approach to perform DfRem by address the barriers during the remanufacturing process (Yang et al., 2016). According to the DfRem guidelines, Shu (2004) made the failureprone features into separate parts to facilitate remanufacture. And Mabee et al. (1999) presented a structured approach that the cross-functional teams fill out a series of design charts to integrate remanufacturing requirements into the product development process. From the point of materials selection, Yang et al. designed the automotive components for remanufacture during the early design stage (Yang et al.,2017). There are hundreds of DfRem guidelines so far. Unfortunately, it is impossible for designers to consider all these guidelines simultaneously and some of them are even contradictory (Yang et al., 2015). Only a few of guidelines can be integrated into the product design process. Besides DfRem guidelines, at the same time, more and more other DfRem methodologies and tools have been proposed. A tool named Repro2 (Remanufacturing with Product Profiles) based on remanufacturable product profiles (RPP) encapsulating the knowledge on both remanufacturing contexts and remanufactured

X. Zhang et al. / Journal of Cleaner Production 239 (2019) 117967

3

Table 1 The design factors identified from literature. References

Design factors for DfRem

Methodology

Amezquita et al.,1995

Ecological, legislative, and economic factors. Design for remanufacturing guidelines, i.e., ease of disassembly, ease of cleaning, ease of inspection, ease of part replacement, ease of reassembly, reusable components, standardization (e.g., modular components, fasteners, and interfaces). Joining and fastening methods.

The design characteristics facilitating remanufacturing are obtained through a case study of automobile door and interviewing remanufacturers and product designers.

Shu and Flowers,1999

Sundin, 2004; Sundin and Bras, 2005

Mabee et al., 1999

Ease of identification, ease of access, wear resistance, ease of handling, ease of verification, ease of separation, ease of securing, ease of alignment, ease of stacking. Guidelines for disassembly, sorting, cleaning, refurbishment, and reassembly.

Jeandin and Mascle, 2016

Fasteners' selection for disassembly.

Sherwood and Shu,2000a,b

Failure and scrap modes.

Ijomah et al., 2007; Hammond et al., 1998

Product features (e.g., surface, material, shape, corrosion resistance, fasteners and joints, modularity, standard parts, wear resistance) and characteristics (e.g., fashion and styling, legislation, obsolescence, remanufacture time and expense, ownership, technology stability, maintenance, consumer acceptance and demand, core available, cyclic nature, technology available). Five external operational factors (i.e., customer demand, products naturally suited to remanufacture, sustainability, competitiveness and profit) and four internal operational factors (i.e., socio-psychological, business, OEMRemanufacturer relationship and design process). Remanufacturing operation, marketing of the remanufactured product, reverse logistics and relationship with used product suppliers.

Hatcher et al., 2013,Hatcher et al., 2014

Saavedra et al. (2013)

Yang et al., 2016

…

Material selection, material joining method (e.g. bolts, screws, adhesive joints and welding, etc.), structure design and surface coating method. …

product properties was developed through case study of products successfully remanufactured (Zwolinski et al., 2006). In order to make a compromise between DfRem and other DfX methodologies, a design for remanufacturing and remanufacturability assessment (DRRA) tool was presented based on Fuzzy TOPSIS (Yang et al., 2016). In addition, to meet with the uncertainties in quality and quantity of cores in reactive remanufacturing, Song et al. demonstrated the theory of design for proactive remanufacturing (Song et al., 2016). The DfRem can also be achieved by applying quality function deployment (QFD) methodology (Yüksel, 2010; Yang et al., 2013), platform design strategy (King and Burgess, 2006)), modified Failure Mode and Effects Analysis (FMEA) (Sherwood and Shu, 2000) and modularization methodology (Tchertchian et al., 2013). The DfRem based on QFD can be more easily integrated into a company's design process (Hatcher et al., 2013).

2.2. Quality function deployment The Quality function deployment (QFD) was developed by Japanese researcher Dr. Yoji Akao in 1960s (Akao, 1990). QFD is an effective planning tool for product development planning, in which the vaguely expressed voice of customer is transformed into engineering characteristics for a product or service. And the “quality” is considered as “a function of different properties” of a product

A framework is developed to evaluate the effect of joint design in such life cycles as manufacturing, assembly, maintenance, and scrap material recycling. Develop a‘RemPro Matrix’ diagram to describe the relationship between certain product properties and specific remanufacturing steps. The design characteristics are obtained by searching the general literature as well as consulting numerous manufacturing and remanufacturing representatives. A new selection method of fasteners was presented based on ANP to provide a guide for designers during the decision making. The waste streams of automotive and Toner-Cartridge remanufacturers were analyzed to determine factors that impede the reuse or remanufacture of parts. The design factors are developed by workshop methodology.

The case studies of three original equipment manufacturers (OEMs) were conducted to identify the operational factors affecting DfRem integration.

Two case studies of original equipment manufacturer (OEM) and independent manufacturer were conducted to analyze the current remanufacturing scenario and its main characteristics within the Brazilian automotive sector. Select subjectively

…

(Fargnoli and Pighini, 2002). Generally, the QFD is a four stages model including four matrices: engineering planning, component deployment, process planning, and production planning. The application of QFD in product's design can improve reliability and revenues as well as reduce design time and costs (Carnevalli and Cauchick, 2008). Furthermore, the QFD has been augmented in Design for X (DfX) which was reviewed by Holt and Barnes (2010). For example, Yüksel (2010) used the conventional QFD methodology in the design of automobile engines for remanufacture. In addition, the traditional QFD framework was extended to be applied in other fields, such as the Water Quality Function Deployment (Zaitsev and Dror, 2013), maintainability (Pramod et al., 2006), and so on. To investigate these new applications of QFD within design activities, Fargnoli and Sakao (2017) took a grass trimmer case as an example to light exhaustively the main characteristics of these selected methods: Hazard analysis function deployment (HAFD), Safety function deployment (SFD), Quality of function deployment for environment (QFDE), Quality function deployment for maintenance (QFDM),Green-quality function deployment (G-QFD), QFD for product/service system, Intervention chart (IC).

2.3. Triangular fuzzy numbers Fuzzy set theory is designed by Zadeh to deal with the

4

X. Zhang et al. / Journal of Cleaner Production 239 (2019) 117967

vagueness and imprecision problems. Triangle fuzzy numbers are ~ ¼ (L,M,U) on R, special fuzzy set. Given a triangular fuzzy number M and its membership function fb ðxÞ : R/½0; 1 is defined as follows (Kaufmann and Gupta,1991; Wu and Ho, 2015):

8 > > > > xL > > x2½L; M > > M L > < xU fb ðxÞ ¼ x2½M; U > > > M U > > > > > > : 0 otherwise

(1)

Where the support of b is the set of elements fx2RjL < x < Ug. The fuzzy operational laws are listed as follows:

~ ¼ ðL þ L ; M þ M ; U þ U Þ ~ þM M 1 2 1 2 1 2 1 2

(2)

~ M ~ 2 ¼ ðL1 L2 ; M1 M2 ; U1 U2 Þ 15M

(3)

lf M 1 ¼ ðlL1 ; lM1 ; lU1 Þ; l > 0; l2R

(4)

 ~ ~ M 1 M 2 ¼ ðL1 =L2 ; M1 =M2 ; U1 =U2 Þ

(5)

The triangular fuzzy numbers can be translated into the crisp numbers by defuzzification. There are many different strategies. This paper adapted the method defined by Yager in 1981. Assuming ~ is m ~ ðx Þ and fuzzy that the membership function of fuzzy set D D i ~ ¼ ðd ; d ; d Þ. The equation is (Yager, 1981): number D 1 2 3

x¼

ðd1 þ 2d2 þ d3 Þ 4

(6)

2.4. Study motivations Nowadays remanufacture has risen to an important way to address the environmental problems. As stated in the above studies, DfRem is one of the key strategies to enhance the remanufacturability of products. However, most of these DfRem methods require many technical data, which is hard to be collected at the early design stage. So various design factors were determined by remanufacturing steps analysis, case study or interview with experts (Sundin, 2004; Sundin and Bras, 2005; Amezquita et al., 1995; Chakraborty et al., 2017). Beside these, it is hard and costly to consider all of these design factors in DfRem simultaneously for product designers. In a word, three main difficulties that are fundamental for the success of DfRem implement efficiently can be summarized as follows: (1) the collection and identification of key DfRem factors that affect a product's remanufacturability; (2) the integration of the key factors into the product's design process; (3) and the relationships between key factors and failure modes are hard to measure with crisp numbers but always in a form of imprecise and linguistic labels. Therefore, the aim of this work is to address these issues. Failure mode or fault feature of used products has an impact on the remanufacturing process and remanufacturability. For example, Subramoniam et al. (2009) highlighted the importance of availability of durable remanufacture cores for the remanufacturing.

Thus, it is logical to extract the key design factors according to the End-of-Life product's failure modes. By extending the traditional QFD frame, a novel two-phase QFD model is constructed to identify the engineering characteristics and DfRem guidelines based on the failure modes of product, which is still a mostly untapped method. Furthermore, the guidelines that are suitable for a certain failure mode are extracted, which is convenient for performing the DfRem purposefully. As a structured and systematized method, it is more feasible because failure modes can be detected easily. Nevertheless, there are many difficulties in using QFD, which include interpreting customer's fuzzy desires, time-consuming, defining the relationships between demanded quality and quality characteristics, developing teamwork and general lack of knowledge of how to use the method (Carnevalli and Cauchick, 2008; Chen et al., 2013). One of the challenges is the semantic ambiguity and subjectivity in QFD due to the decision makers are always express their judgments on qualitative information by linguistic variables. To deal with this problem, the fuzzy theory was integrated into the QFD, which named as fuzzy QFD (Yang et al., 2013; Wu and Ho, 2015; Zhang, 2019; Bilis¸ik et al.,2019). Similarly, in this paper, the fuzzy theory is integrated with the twophase QFD to deal with the fuzzy information expressed by linguistic variables. This research is immensely important for the Original Equipment Manufacturer (OEM) to obtain the product engineering characteristics and DfRem guidelines that firms should focus on to improve the product's remanufacturability according to the failure modes feedback. 3. Research methodology In this paper, the two-phase QFD model is presented mainly for identifying the key design characteristics for DfRem. Its construction includes four steps. Step 1 Development of the comprehensive list of design factors of products. It is developed by literature review and includes engineering characteristics and DfRem guidelines. Step 2 Determining customer requirements based on failure modes. In order to deal with the fuzziness, uncertainty and subjective in determining the importance of customer requirements, the fuzzy comprehensive evaluation method is used to determine the weighting. Step 3 Construction of the first correlation matrix FE between the customer requirements and engineering characteristics (EC). Then the relative weights of engineering characteristic of product can be calculated. Step 4 Construction of the second correlation matrix EG between the engineering characteristics and DfRem guidelines. And then the relative weights of DfRem guidelines are calculated, which can be used to select the key factors. Fig. 1 illustrates the research methodology of this paper. 4. Two-phase QFD model for identification of design characteristics for DfRem According to Table 1, the DfRem characteristics are compiled and classified into two groups, i.e., guidelines and engineering characteristics, which are shown in Table 2. The product engineering characteristics consist of 16 product features and 11 product characteristics. However, DfRem of products is a complex process with all kinds of factors, which necessitates the identification of the main engineering characteristics for DfRem. Then the DfRem of product can be performed based on the design guidelines. Nevertheless, it is

X. Zhang et al. / Journal of Cleaner Production 239 (2019) 117967

5

Fig. 1. The proposed framework.

Table 2 DfRem characteristics lists. Guidelines

Engineering characteristics Product features

Product characteristics

Ease Ease Ease Ease Ease Ease Ease Ease Ease Ease Ease Ease Ease …

Corrosion resistance (e1 ) Wear resistance (e2 ) Modularity (e3 ) Material (e4 ) Joining and fastening methods (e5 ) Standardization of the parts (e6 ) Part surface (e7 ) Part geometry shape (e8 ) Products naturally suited to remanufacture (e9 ) Weight (e10 ) Volume (e11 ) Number of part (e12 ) Number of types of materials (e13 ) Hardness (e14 ) Position of joints and fasteners (e15 ) Physical lifetime (e16 ) …

Fashion and styling (e17 ) Obsolescence (e18 ) Technology stability (e19 ) Consumer acceptance and demand (e20 ) Technology available (e21 ) Core available (e22 ) Cyclic nature (e23 ) Ownership (OEM-Remanufacturer,others) (e24 ) Maintenance (e25 ) Remanufacture time and expense (e26 ) Competitiveness and profit (e27 ) …

of of of of of of of of of of of of of

disassembly separation alignment stacking cleaning inspection securing part replacement reassembly identification access handling verification

hard to consider all of the guidelines simultaneously because some of them are contradictory. Fortunately, the barriers and factors related to the remanufacturability and reusability of product are hidden in the failure modes. And the failure modes can be observed easily. So to address the above problem, a two-phase QFD model is proposed based on failure modes feedback and modified QFD (Fig. 2). The model comprises two stages. In the first stage, failure modes and engineering characteristics are collected through various sources such as literature review and case study. And the matrix FE will be constructed to deploy failure modes into the engineering characteristics. In the second stage, the relative important engineering characteristics are selected. And the correlation matrix EG is constructed to prioritize the DfRem guidelines. The results from the EG will be fed back to the designers to perform the DfRem of

product. 4.1. The first stage 4.1.1. Determining customer requirements based on failure modes Failure mode signals why a part can no longer fulfill its intended function (Sherwood and Shu, 2000a,b). The classical failure modes include wear, bent, crack, burn, fracture (e.g., ductile fracture, brittle fracture),corrosion, fastener failure, loosened, and so on. The failure parts are either remanufactured or discarded. Typically, a part that has failed and is not repairable will be discarded, whose reasons are called scrap modes. Classical scrap modes include cosmetic imperfections, oversize/undersize, material loss, mating part lost, no process, overstock, weaken part, etc. (Sherwood and Shu, 2000). One kind of failure mode may lead to a number of scrap

6

X. Zhang et al. / Journal of Cleaner Production 239 (2019) 117967

mapped into the fuzzy comprehensive evaluation model. And the major steps are described in details as follows: Step 1 Let the set of evaluation criteria U ¼ fu1; u2 ; :::; uj ; :::; um g, in which m is the number of evaluation criterion. In order to present the different importance of evaluation criterion, the weighting coefficient set is defined. That is, A ¼ fa1 ; a2 ; :::; am g. Step 2 Assuming the evaluation index set V ¼ fv1 ; v2 ; :::; vi ; :::; vn g has n fuzzy evaluation indexes, such as ‘high’, ‘low’, ‘medium’, and so on. Step 3 For every element fJ 2Ffm , the one-way factor evaluation is performed and the evaluation matrix Rj can be denoted as follows

3

2 J

Fig. 2. The two-phase QFD model for identification of design characteristics for DfRem.

modes, and vice versa. The less the failure, the better the product's remanufacturability. The customer requirements can be deduced according to the failure modes and scrap modes, which are shown in Table 3 in details. And for the special product, some of them may be ignored or some new failure modes may be added. And its failure modes should be collected by interviewing with the consumers, the remanufacturers, recyclers, designers, and the end-of-life (EOL) disposal engineers. Suppose a product has Q kinds of failure modes, and its corresponding customer requirements can be denoted as follows:



Ffm ¼ f1 ; f2 ; :::; f2Q



In practice, not all of the failure modes deserve remanufacturing. For instance, the cracked component should not be remanufactured and reused because of high cost and no process. Thus, the importance of customer requirements is described by fuzzy linguistic variables. In order to deal with the fuzziness, uncertainty and subjective in determining the importance of customer requirements, the fuzzy comprehensive evaluation method is applied. According to Yang and Mak (2017), the above problem can be

6 r 11 6 J 6 Rj ¼ 6 r 21 6 « 4 J r m1

r J12

…

r 22 « J r m2

… « …

J

r J1n 7 J 7 r 2n 7 7 « 7 5 j r mn

J

Where, r ij 2½0; 1 can be obtained by interviewing with experts. Step 4 The result of comprehensive evaluation can be calculated by the following formula

BJ ¼ A,Rj ¼ ½B1 ; B2 ; /; Bn  2 6 J 6r 6 11 6 J 6r ¼ ½a1 ; a2 ; /; am 6 6 21 6 « 6 6 4

3 r 12

J

/

r 1n

J

r J22

/

r J2n

«

«

«

r Jm1

r Jm2

/

7 7 7 7 7 r Jmn 7 7; 7 7 7 5

J ¼ 1; 2; /; 2Q Based on the principle of maximum membership, the weighting WJ of fJ can be obtained. Assuming By ¼ max fBj g, then WJ ¼ vy . 0jn

To facilitate subsequent operations, the WJ is converted into the linguistic variables set d ¼ fl0 ;l1 ;:::;lq ;:::;lp g. Furthermore, lq can be

Table 3 Classical failure modes and corresponding customer requirements. Failure modes

Scrap modes

Customer requirements

Wear

Makes oversize, Makes undersize, etc.

Bent/Distortion

Weaken part

Crack

Weaken part, No process

Burnt

Makes oversize, Makes undersize, Material loss,etc.

Fracture

No process, material loss, etc.

Corrosion

Makes oversize, Makes undersize, Material loss,etc.

Hole

Weaken part, material loss,etc.

Fastener failure

Overstock

Handling damage Dent

Cosmetic imperfections

…

…

No Wear Remanufacturability of wear No bent Remanufacturability of bent No Crack Remmanufacturability of crack No burnt Remanufacturability of burnt No fracture Remanufacturability of fracture No corrosion Remanufacturability of corrosion No hole Remanufacturability of hole No fastener failure Remanufacturability of fastener failure No handing damage No dent Remanufacturability of dent …

X. Zhang et al. / Journal of Cleaner Production 239 (2019) 117967

 mapped into triangular fuzzy number

 q1 q qþ1 ; ; . p p p

normalized by Eq. (11).

~ J of the Jth customer requirement Thus, the fuzzy weighting w can be expressed as follows:

~J ¼ w



U wLJ ; wM J ; wJ



; J ¼ 1; 2; ::::; 2Q

(7)

Eq. (6) is used to remove the weighting's fuzzies. Then they are normalized by Eq. (8).

xJ ¼

7

  U W LJ þ 2W M J þ WJ

 j xEC

¼

jðLÞ

jðMÞ

jðUÞ

wEC þ 2wEC þ wEC



4

wjEC ¼ xjEC

n .X

xjEC

(11)

j¼1

The weighted importance and the relative weights wjEC illustrate the relative importance of each engineering characteristic of product in meeting the failure modes’ needs.

4

wJ ¼ xJ

2Q .X

4.2. The second stage

xJ

(8)

J¼1

4.1.2. Candidate engineering characteristics and the correlation matrix FE The product's engineering characteristics record the voice of engineer. The information of engineering characteristics is generated by interviewing with the design engineers of products or research articles. In this paper, the candidate engineering characteristics can be selected from Table 2. The correlation matrix FE is the most important room, which encompass the influence that an engineering requirement has on gaining the corresponding customer requirements deduced from failure modes. To develop the correlation matrix FE, an appropriate scale is set for assigning the relative degree as ‘Very Weak (VW)','Weak(W)','Medium(M)','Strong(S)’, and ‘Very Strong (VS)'. Table 4 illustrates the relative degrees between failure modes and engineering requirements, which is established by interviewing with experts, literature review, or deducing from the definition. For example, adhesive wear is a function of the contact probability between micro-bulge on material surface, the normal load, yield stress of worn material (Burwell and Strang, 1952; Archard, 1953; Wang et al., 2017a,b). While abrasive wear can be described by the total sliding distance between two metal surface, the angle between conical surface of grit and part's surface with low degree of hardness, the hardness of worn material (Wang et al., 2017a,b). According to the above definitions, the relative degree between adhesive wear and material (e4 ), wear resistance (e2 ) is ‘Very Strong (VS)', and that between abrasive wear and material (e4 ), wear resistance (e2 ), hardness (e14 ) is ‘Very Strong (VS)', and so on. According to Table 4, the correlation matrix FE can be developed. Through mapping these linguistic variables into the intervals (1,9), the fuzzy relationship matrix FE can be calculated by Eq. (9):

3

2P



FE ¼ cij 2Q n ¼ 4

f cl l¼1 ij 5

f

; i ¼ 1; 2; ::::; 2Q ; j ¼ 1; 2; :::; n

4.2.1. Alternative DfRem guidelines Design guidelines are an efficient and effective means for DfRem. However, it is difficult to consider all these guidelines simultaneously. In general, the design guidelines are developed by case study, literature review, and/or interviews with engineers. In this paper, the list of design guidelines for DfRem is collected from literature and research articles (Mabee et al., 1999; Ijomah et al., 2007; Yang et al., 2015), which are shown in Table 5. Denote the set of DfRem guidelines as G ¼ fg1 ; g2 ; :::; gk ; :::; gq g. 4.2.2. Construction of the correlation matrix EG The second matrix EG is developed to describe how the DfRem guidelines are determined by engineering characteristics. To fill up the matrix EG, an appropriate scale is set for assigning the relative degree as ‘Very Weak (VW)','Weak(W)','Medium(M)','Strong(S)','Very Strong (VS)'. The relational strength of engineering characteristics and alternative DfRem guidelines is indicated with numbers tjk determined by the e experts, defined as follows:

h

EG ¼ tjk

"Pe

i nq

¼

e

# ; j ¼ 1; 2; :::; n; k ¼ 1; 2; :::; q

(12)

nq

Where e is the number of experts invited, n is the number of engineering characteristics and q is the number of DfRem guidelines. The fuzzy weight wkEG for the kth DfRem guideline can be obtained, shown in Eq. (13). The wkEG is removed the weighting's fuzzies by Eq. (6) and are normalized by Eq. (14). Thus, the DfRem guidelines are ranked by the relative weights wkEG .

wkEG ¼

n X

  kðLÞ kðMÞ kðUÞ wjEC tjk ¼ wEG ; wEG ; wEG ; k ¼ 1; 2; :::; q

(13)

j¼1

 xkEG

¼

kðLÞ

kðMÞ

wEG þ 2wEG

kðUÞ

þ wEG



4

(9) wkEG ¼ xkEG

2Q n

s s¼1 t jk

q .X

xkEG

(14)

k¼1

Where cij denotes the relationship between the ith customer requirement and the jth engineering characteristic (EC); clij is the fuzzy degree between the ith index and the jth element given by the lth expert; And f is the number of experts invited. j According to Eq. (9), the weight wEC of the jth engineering characteristic can be obtained, shown in Eq. (10).

wjEC ¼

2Q X

  jðLÞ jðMÞ jðUÞ wi cij ¼ wEC ; wEC ; wEC ; j ¼ 1; 2; :::; n

(10)

i¼1 j

The wEC is removed the weighting's fuzzies by Eq. (6) and are

j

Where, wEC is the relative weight for the jth engineering characteristic, q is the number of DfRem guidelines, n is the number of product's engineering characteristics. 5. Case study and discussion 5.1. Identifying the design characteristics of engine crankshafts for remanufacturing The automotive sector was the first sector to gain success

8

X. Zhang et al. / Journal of Cleaner Production 239 (2019) 117967

damage

Table 4 Relative degrees between failure modes and engineering requirements.

remanufacturing because the automotive engines have got many complex and high valuable parts with an important potential for remanufacture. Furthermore, the remanufactured engines provided a valuable source of spare parts and under-warranty engines (Seitz, 2007). A crankshaft is the part of an engine which translates the piston reciprocation motion into rotary motion (Mateus et al., 2019). Take the engine crankshafts as a case (Shown in Fig. 3). Through literature review, case studies, and visit enterprise, it is found that the main typical failure modes of crankshaft include wear (e.g., crankpin, main journal, and thrust face), bent, burnt, and fatigue fracture.

Then through the following steps the more important engineering characteristics and DfRem guidelines could be obtained. Step 1 Determining weights of customer requirements. According to the above inspecting result, the set of customer requirements

Ffm ¼ ff1 ; f2 ; f3 ; f4 ; f5 ; f6 ; f7 ; f8 g n ¼ No wear; No bent; No crack; No burnt; Remanufacturability of wear;

X. Zhang et al. / Journal of Cleaner Production 239 (2019) 117967

9

Table 5 Guidelines of DfRem. Remanufacture requirements

Guidelines

ID.

Ease of disassembly and separation

Disassembly instructions provided on the core (Yang et al., 2015) Easy to loosen joints/fasteners (Ijomah et al., 2007) No integral construction of more than one material (Mabee et al., 1999) Reduce the variation of the tools used (Yang et al., 2015; Mabee et al., 1999) Only common tools used (Mabee et al., 1999) Removal/disassembly without damage (Amezquita et al., 1995; Mabee et al., 1999) Permanent joining methods are not used (Amezquita et al., 1995; Ijomah et al., 2007) Using one disassembly direction (Yang et al., 2015) Multi-disassembly should be possible with one operation (Mabee et al., 1999) Deposits, impurities are removable without part damage (Mabee et al., 1999) Easy access to the fastener/joints (Mabee et al., 1999) Easy identification of the fastener (Mabee et al., 1999) Easily access to subsystem (Mabee et al., 1999) Easily remove subsystem (Mabee et al., 1999) Simple inner and outside surfaces (Yang et al., 2015) Simple and fewer variation method for cleaning (Shu and Flowers, 1999; Yang et al., 2015) Surfaces to cleaned are smooth (Mabee et al., 1999; Ijomah et al., 2007) Labels and instructions withstand the cleaning process (Sundin and Bras, 2005) Markings withstand cleaning process (Mabee et al., 1999) Surfaces are wear resistant (Yang et al., 2017; Mabee et al., 1999) No secondary finishes (Mabee et al., 1999) Plastic surfaces are not coated (Mabee et al., 1999) Surface treatments last through refurbishment (Mabee et al., 1999) Texture areas are refurbishable (Mabee et al., 1999) Prevent the corrosion of parts (Ijomah et al., 2007; Charter and Gray 2008) Less hazardous materials (Yang et al., 2017) Bulky-overdesign (Shu and Flowers, 1999; Yang et al., 2015) Wear surfaces are overtoleranced for long life (Mabee et al., 1999) Accent lines/areas are easily separable (Mabee et al., 1999) Sufficient clearance and support at the base to avoid damages during transportation (Yang et al., 2015); (Shu and Flowers, 1999) Ease of classification of the components (Mabee et al., 1999) Parts are marked with material composition (Mabee et al., 1999) All similar parts are clearly identified or marked for easy sorting (Mabee et al., 1999) Ease of assessing the condition of the components (Yang et al., 2015) Request for more objective testing methods (Yang et al., 2015) Testing points are easy to access (Sundin and Bras, 2005) Test methods should be simple and quick (Amezquita et al., 1995) Mounting points are easily accessible (Mabee et al., 1999) Mounting points are easily identified (Mabee et al., 1999) …

g1 g2 g3 g4 g5 g6 g7 g8 g9 g10 g11 g12 g13 g14 g15 g16 g17 g18 g19 g20 g21 g22 g23 g24 g25 g26 g27 g28 g29 g30

Ease of access

Ease of cleaning

Ease of handling

Ease of sort and identification

Ease of inspection

Ease of reassembly …

g31 g32 g33 g34 g35 g36 g37 g38 g39 …

Fig. 3. Typical failure modes of engine crankshafts. (From: Fonte et al., 2019; www.360che.com/pic/31924_140250.html;dy.163. com/v2/article/detail/CO5782CN0527GQ35. html).

Remanufacturability of bent; Remanufacturability of crack; Let the evaluation criteria set feconomic feasibility; technical feasibilityg.

U¼

fu1 ;

u2 g ¼

The better the remanufacturability, the higher the economic and technical feasibility. According to the experts grading method, the weighting coefficient set A ¼ fa1 ; a2 g ¼ f0:45; 0:55g.

10

X. Zhang et al. / Journal of Cleaner Production 239 (2019) 117967

And the evaluation index set:

V ¼ fv1 ; v2 ; v3 ; v4 ; v5 g 0  0 0 0 0 0 0 ¼ Very High0 ; High0 ; Medium ; Low ; Very Low0 Based on the responses collected from experts invited, the oneway fuzzy judgment matrices can be obtained. The result of comprehensive evaluation can be calculated and shown as follows:

B1 ¼ ð0:1550; 0:3100; 0:3450; 0:1450; 0:0450Þ B2 ¼ ð0:1225; 0:2900; 0:2000; 0:2550; 0:1325Þ B3¼(0.0500,0.1450,0.2500,0.3100,0.2450)

B4 ¼ ð0:0365; 0:1635; 0:2000; 0:3450; 0:2550Þ B5 ¼ ð0:3000; 0:2000; 0:2550; 0:2000; 0:0450Þ B6 ¼ ð0:3275; 0:1725; 0:2225; 0:1775; 0:1000Þ B7 ¼ ð0:2450; 0:3450; 0:2000; 0:1550; 0:0550Þ B8 ¼ ð0:1450; 0:2500; 0:3550; 0:1500; 0:1000Þ ~1 ¼ Based on the principle of maximum membership, w 0 High0 , 0 Low0 , w ~2 ¼ ~3 ¼ ~4 ¼ w w 0 0 0 0 0 0 0 0 Low0 , w ~ 5 ¼ Very High ,w ~ 6 ¼ Very High , w ~ 7 ¼ High , w ~ 8 ¼ Medium0 :Let the corresponding linguistic variables set d ¼ fl0 ; l1 ; …; lq ; …; lp g ¼ fVery Low; Low; Medium; High; Very Highg.And the linguistic var~ ¼ iables can be translated into the triangular fuzzy numbersW 0 Medium0 ,

{(0,0,0.25), (0,0.25,0.5), (0.25,0.5,0.75), (0.5,0.75,1), (0.75,1,1)}.Thus, the above weightings can be expressed by the triangular fuzzy numbers. Furthermore, according to Eq. (6) and Eq. (8), the relative importance weighting can be obtained (shown in Table 6). This study selected all of the 27 engineering characteristics as given in Table 2. In addition, 10 experts were interviewed to complete the correlation matrix FE. The mapping between the relevance degree and the intervals (1,9) was shown in Table 7. Thus, the experts' fuzzy linguistic evaluation can be transformed into triangle fuzzy numbers according to Table 7 and Fig. 4. According to 10 experts' preference to the five intervals of (1,9), the correlation between product engineering characteristics and

customer requirements can be obtained by using Eq. (9), which is shown in Table 8. After arranging these results, the matrix FE was obtained and the fuzzy weightings of engineering characteristics were deduced by using Eq. (10), and then defuzzied and normalized by Eq. (6) and Eq. (11) respectively, as shown in Table 9. All of the 39 engineering characteristics as given in Table 5 were selected. Furthermore, 10 experts were interviewed to complete the correlation matrix EG. Similarly, the matrix EG can be obtained by using Eq. (12) according to 10 experts' preference to the five intervals of (1,9). And according to Table 7 the triangle fuzzy numbers matrix EG was deduced, which is shown in Table 10. The fuzzy weightings of DfRem guidelines were obtained by using Eq. (13), and then defuzzied and normalized by Eq. (6) and Eq. (14) respectively, as shown in Table 11. Therefore, according to the relative weights in Table 11, DfRem guideline g28 has the largest priority and g4 has the lowest priority. Moreover, the order of DfRem guidelines is g28 > g20 > g25 > g10 > g23 > g39 > g15 > g38 > g27 > g26 > g32 > g17 > g16 > g24 > g12 > g33 > g37 > g11 > g14 > g30 > g29 > g9 > g2 > g31 > g34 > g36 > g13 > g1 > g7 > g8 > g21 > g18 > g22 > g3 > g5 > g19 > g6 > g35 > g4 . The designers could select the proper guidelines for redesigning the new generation engine crankshafts according to the ranks of them. Consequently, five top DfRem guidelines in bold (Table 11) are identified, as shown in Table 12. Hence, these key DfRem guidelines should be focused on to perform the redesign of engine crankshaft. For example, the wear surfaces of the engine crankshaft can be improved by material selection, structure design, or heat treatment. If more detail is required, the next matrix between DfRem guidelines and improvement method of structure or material should be built. 5.2. Discussion The main studies on DfRem include identification of design factors and DfRem methodologies and tools. However, it is hard and costly to consider all of design factors in DfRem. The two-phase QFD model in this paper aims to fill the gap between the design factors and the DfRem methodologies and tools by finding the key design factors more effectively. The case study allows us to bring to light the main characteristics of the proposed method in this paper. It is a structured and general method, that is, can be applied to identify the key DfRem factors for any product. In addition, the key guidelines that are suitable for a certain failure mode are extracted in a targeted way,

Table 6 Weightings of failure modes. Step 2 Determining the correlation matrix FE. Failure mode Ffm

Wear Bent Crack Burnt Remanufacturability of wear Remanufacturability of bent Remanufacturability of crack Remanufacturability of burnt

Evaluation criteria U

Economic feasibility Technical feasibility Economic feasibility Technical feasibility Economic feasibility Technical feasibility Economic feasibility Technical feasibility Economic feasibility Technical feasibility Economic feasibility Technical feasibility Economic feasibility Technical feasibility Economic feasibility Technical feasibility

Evaluation index V Very high

High

Medium

Low

Very low

0.1 0.2 0.15 0.1 0.05 0.05 0.02 0.05 0.3 0.3 0.3 0.35 0.3 0.2 0.2 0.1

0.2 0.4 0.4 0.2 0.2 0.10 0.18 0.15 0.2 0.2 0.2 0.15 0.4 0.3 0.25 0.25

0.4 0.3 0.2 0.2 0.25 0.25 0.2 0.2 0.2 0.3 0.25 0.2 0.2 0.2 0.3 0.4

0.2 0.1 0.2 0.3 0.2 0.4 0.4 0.3 0.2 0.2 0.15 0.2 0.1 0.2 0.15 0.15

0.1 0 0.05 0.2 0.3 0.2 0.2 0.3 0.1 0 0.1 0.1 0 0.1 0.1 0.1

Fuzzy Weighting

Relative weighting

Medium (0.25,0.5,0.75)

0.1026

High (0.5,0.75,1)

0.1538

Low (0,0.25,0.5)

0.0513

Low (0,0.25,0.5)

0.0513

Very High (0.75,1,1)

0.1923

Very High (0.75,1,1)

0.1923

High (0.5,0.75,1)

0.1538

Medium (0.25,0.5,0.75)

0.1026

X. Zhang et al. / Journal of Cleaner Production 239 (2019) 117967

11

Table 7 Fuzzy numbers of the relevance degree. Relative degree

Very Weak (VW)

Weak(W)

Medium(M)

Strong(S)

Very Strong (VS)

Fuzzy numbers

(1,1,2)

(2,3,4)

(4,5,6)

(6,7,8)

(8,9,9)

Fig. 4. Triangular fuzzy linguistic terms applied in the two-phased QFD model.

Table 8 The fuzzy evaluation of relationship between failure modes and engineering characteristics. Linguistic evaluation variables

modes, which is cost effective and easier for performing DfRem. 6. Conclusions

Triangle fuzzy numbers

VW

W

M

S

VS

0 0 10 0

2 0 0 0

2 0 0 0

6 3 0 2

0 7 0 8

2 5

5 5

3 0

0 0

0 0

5 4

5 2

0 4

0 0

0 0

3 3

2 2

3 2

2 3

0 0

1 0 0 0

3 3 0 0

4 5 3 3

2 2 5 4

0 0 2 3

(4.8,5.8,6.8) (7.4,8.4,8.7) (1,1,2) (7.6,8.6,8.8) … (2.4,3.2,4.2) (1.5,2,3) … (1.5,2,3) (2.4,3,4) … (3.1,3.8,4.8) (3.3,4,5) … (3.5,4.4,5.4) (3.8,4.8,5.8) (5.8,6.8,7.6) (6,7,7.7)

…

…

…

…

which avoids the blindness of DfRem. Meanwhile, the key factors are convenient to be integrated into the product's DfRem process purposefully. To underline its pros and cons, Table 13 shows the difference between the proposed method and existing related studies. Table 13 shows that existing studies focus on developing DfRem factors from all aspects. Based on these factors, this paper aims to identify the key factors according to the used products’ failure

Nowadays remanufacture has risen to an important way to address the environmental problems. DfRem is one of the key strategies to enhance the remanufacturability of products. However, one challenge of DfRem is the identification of design factors (e.g., product characteristics) which can be compiled to steer a design towards better remanufacturability. These factors are hard to be collected at the early design stage. Though hundreds of factors were developed by remanufacturing steps analysis, case study or interview with experts. It is hard and costly to consider all of these design factors in DfRem. A proper method to find the key design factors more effectively is another challenge. To deal with this issue, this paper presents a two-phase QFD model based on an improved quality function deployment (QFD) and failure modes’ feedback from end-of-life (EOL) product. The model comprises two stages. At the first stage, the comprehensive list of design factors of products is developed by literature review. The customer requirements related to remanufacturability closely are deduced according to the failure modes and scrap modes. And the fuzzy comprehensive evaluation method is used to determine the weighting of customer requirements. And then the failure modes are translated into engineering characteristics. At the second stage, the relative important engineering characteristics are selected. And the key DfRem guidelines are identified according to the engineering characteristics. The results will be fed back to the designers to perform the DfRem of product. Finally, a case study of the automotive engine crankshaft validates the feasible of method proposed.

Table 9 The correlation matrix FE. Step 3 Determining the correlation matrix EG. Customer requirements

wJ

e1

e2

e3

…

e27

f1 f2 f3 f4 … f8 Fuzzy weighting Relative weight

0.1026 0.1538 0.0513 0.0513 … 0.1026

(4.8, 5,8, 6.8) (1.5, 2, 3) (2.4, 3, 4) (3.3, 4, 5) … (1.2, 1.4, 2.4) (2.048, 2.608, 3.608) 0.0247

(7.4, 8.4, 8.7) (1.2, 1.4, 2.4) (3.4, 4.4, 5.4) (2.3, 3, 4) … (1.8, 2.4, 3.4) (3.4264.041, 4.777) 0.0371

(1,1,2) (1.1, 1.2, 2.2) (1.4, 1.8, 2.8) (1.7, 2.2, 3.2) … (2.3, 3, 4) (1.300, 1.590, 2.590) 0.0161

… … … … … … … …

(2.4, 3.2, 4.2) (1.5, 2, 3) (3.1, 3.8, 4.8) (2.3, 3, 4) … (6, 7, 7.7) (4.938, 5.810, 6.526) 0.0525

12

X. Zhang et al. / Journal of Cleaner Production 239 (2019) 117967

Table 10 The correlation matrix EG. …

Engineering Characteristics

wEC

g1

g2

g3

e1 e2 e3 e4 … e27 Raw score Relative weight

0.0247 0.0371 0.0161 0.0655 … 0.0525

(1.1,1.2,2.2) (1,1,2) (1.5,2,3) (1,1,2) … (3,4,5) (2.43,2.74,3.67) 0.023

(1,1,2) (1,1,2) (4.2,5.2,6.2) (1,1,2) … (2.8,3.6,4.6) (3.52,2.978,3.9) 0.025

(1,1,2) (1,1,2) (1,1,2) (3.8,4.8,5.8) … (2,3,4) (2,2.4,3.37) 0.020

j

…

g39 (1,1,2) (1,1,2) (1.3,1.6,2.6) (1,1,2) … (5,6,6.8) (3.191,3.765,4.681) 0.031

Table 11 The weights list of DfRem guidelines. DfRem guidelines

The fuzzy weight wkEG

The relative weight wkEG

DfRem guidelines

The fuzzy weight wkEG

The relative weight wkEG

g1 g2 g3 g4 g5 g6 g7 g8 g9 g10 g11 g12 g13 g14 g15 g16 g17 g18 g19 g20

(2.4297,2.7384,3.6744) (2.5530,2.9775,3.9034) (1.9873,2.3958,3.3722) (1.8234,2.0764,3.0621) (2.0941,2.3038,3.2225) (1.9128,2.1518,3.1176) (2.3054,2.7026,3.6737) (2.3186,2.6839,3.6510) (2.5406,2.9631,3.9233) (3.3039,4.0353,4.9964) (2.5671,3.0449,3.9790) (2.6116,3.0819,4.0242) (2.3294,2.7658,3.7412) (2.5446,3.0064,3.9519) (3.1218,3.5391,4.4467) (2.6394,3.1303,4.1070) (2.6696,3.1394,4.0783) (2.0289,2.4872,3.4744) (1.8600,2.2464,3.2244) (4.0301,4.6809,5.5422)

0.0232 0.0249 0.0204 0.0181 0.0199 0.0187 0.0228 0.0227 0.0249 0.0328 0.0253 0.0257 0.0233 0.0251 0.0294 0.0261 0.0261 0.0210 0.0192 0.0380

g21 g22 g23 g24 g25 g26 g27 g28 g29 g30 g31 g32 g33 g34 g35 g36 g37 g38 g39

(2.0980,2.5977,3.5871) (2.0170,2.4257,3.4069) (3.2080,3.8262,4.7512) (2.6147,3.1045,4.0303) (3.9828,4.6622,5.4840) (2.9436,3.4163,4.2897) (2.9638,3.5153,4.4453) (4.0829,4.8247,5.7067) (2.5208,3.0045,3.9249) (2.5306,3.0032,3.9568) (2.4886,2.9750,3.9437) (2.7458,3.2630,4.1853) (2.5827,3.0983,4.0495) (2.3739,2.8339,3.7874) (1.8010,2.1429,3.1298) (2.3510,2.7880,3.7395) (2.6277,3.0769,4.0078) (3.0053,3.5555,4.5044) (3.1912,3.7650,4.6813)

0.0218 0.0206 0.0313 0.0258 0.0377 0.0282 0.02896 0.0390 0.0250 0.0251 0.0248 0.0270 0.0257 0.0237 0.0185 0.0234 0.0257 0.0293 0.0309

Table 12 Design characteristics of the engine crankshafts for remanufacturing. No. 1 2 3 4 5

DfRem guidelines Ease of handling

Ease of disassembly and separation Ease of handling

g28 g20 g25 g10 g23

Wear surfaces are overtoleranced for long life Surfaces are wear resistant Prevent the corrosion of parts Deposits, impurities are removable without part damage Surface treatments last through refurbishment

Table 13 The comparison between the proposed method and existing studies. Literatures

Methods

Pros and cons

This paper

Identified based on failure modes feedback and QFD.

1. 2. 3. 4. 1. 2. 1. 2. 1. 2.

It is a structured and general method. Key factors can be identified in a targeted way. It is subjective. It is convenient for practical application. Sundin, 2004; Sundin and Bras, 2005 Remanufacturing process analysis It is subjective. It is structured and general method. Mabee et al., 1999 Literature review and consulting manufacturing It is time-consuming and subjective. and remanufacturing representatives. It is a structured and general method. It is time-consuming. Amezquita et al.,1995; Sherwood and Shu,2000; Case study and interviewing remanufacturers It is not general and only can be applied for Williams and Shu,2000; Hatcher et al. (2013); and product designers. specific products. Hatcher et al., 2014; Saavedra et al., 2013 3. It is objective. Ijomah et al., 2007; Hammond et al., 1998 The design factors are developed by workshop methodology. 1. It is costly and time-consuming. 2. More comprehensive design factors are developed. Yang et al. (2016) Select subjectively It is simple and subjectively.

X. Zhang et al. / Journal of Cleaner Production 239 (2019) 117967

The highlights of this paper are listed as follows: (1) A novel two-phase QFD model is constructed to identify the engineering characteristics and DfRem guidelines based on the failure modes of product, which is still a mostly untapped method. It is a structured and systematized method for identification of key design factors. (2) The guidelines that are suitable for a certain failure mode are extracted, which is convenient to integrate the key factors into the product's design process for performing the DfRem purposefully. (3) The relationships between key factors and failure modes are hard to measure with crisp numbers but always in a form of imprecise and linguistic labels. In order to overcome this difficulty, the fuzzy comprehensive evaluation method is used to determine the weight of failure modes, and the triangle fuzzy numbers are integrated. The main contribution of this paper is the way it determines the product DfRem characteristics from the failure modes information. This helps the firms to perform the DfRem in a targeted way. However, there are still some limitations for this method. The two-stage QFD model is subjective. In further research, an expertknowledge database should be developed to help the construction of the model. Furthermore, the other phases of the QFD model can also be established according to these results to perform the DfRem of product. This method is time consuming, so a prototype software system is needed to help the designers. Acknowledgments This research is supported by the National Natural Science Foundation of China (No.51565044,51965049), the Inner Mongolia Natural Science Foundation of China (No.2017MS (LH) 0510) and the Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region(NJYT-17-B08). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.jclepro.2019.117967. References Amezquita, T., Hammond, R., Salazar, M., Bras, B., 1995. Characterizing the remanufacturability of engineering systems. In: Proceedings of ASME Advances in Design Automation.Boston, pp. 271e278. Akao, Y., 1990. Quality Function Deployment: Integrating Customer Requirements into Product Design. Productivity Press, Cambridge, MA. Archard, J.F., 1953. Contact and rubbing of flat surfaces. J. Appl. Phys. 24 (8), 981e988. € Bilis¸ik, O.N., S¸eke, S¸., Aydın, N., et al., 2019. Passenger satisfaction evaluation of public transportation in Istanbul by using fuzzy quality function deployment methodology. Arabian J. Sci. Eng. 44 (3), 2811e2824. Bras, B., McIntosh, M.W., 1999. Product, process, and organization design for remanufacture-an overview of research. Robot. Comput. Integr. Manuf. 15, 167e178. Burwell, J.T., Strang, C.D., 1952. On the empirical law of adhesive wear. J. Appl. Phys. 23 (1), 18e28. Carnevalli, J.A., Cauchick, M.P.A., 2008. Review, analysis and classification of the literature on QFD-Types of research, difficulties and benefits. Int. J. Prod. Econ. 114 (2), 737e754. Chakraborty, K., Mondal, S., Mukherjee, K., 2017. Analysis of product characteristics for remanufacturing using Fuzzy AHP and Axiomatic Design. J. Eng. Des. 28 (5), 338e368. Charter, M. , Gray, C. , 2008. Remanufacturing and product design. Int J Prod Dev. 6 (3-4), 375e392. Chen, W., Hoyle, C., Wassenaar, H.J., 2013. Decision-based Design: Integrating Consumer Preferences into Engineering Design. Springer, London, UK. Fang, H.C., Ong, S.K., Nee, A.Y.C., 2016. An integrated approach for product remanufacturing assessment and planning. Procedia CIRP 40, 262e267.

13

Fargnoli, M., Pighini, U., 2002. Quality improvement in the design of mechanical systems. In: Proceedings of Design 2002, the 7th International Design Conference. Dubrovnik, Croatia. DS, vol. 30, pp. 1755e1760. Fargnoli, M., Sakao, T., 2017. Uncovering differences and similarities among quality function deployment-based methods in Design for X: benchmarking in different domains. Qual. Eng. 29 (4), 690e712. Favi, C., Marconi, M., Germani, M., Mandolini, M., 2019. A design for disassembly tool oriented to mechatronic product de-manufacturing and recycling. Adv. Eng. Inf. 39, 62e79. Favi, C., Germani, M., Mandolini, M., Marconi, M., 2016. Includes knowledge of dismantling centers in the early design phase: a knowledge-based design for disassembly approach. Procedia CIRP 48, 401e406. Fonte, M., Freitas, M., Reis, L., 2019. Failure analysis of a damaged diesel motor crankshaft. Eng. Fail. Anal. 102, 1e6. Graham-Jones, P.J., Mellor, B.G., 1995. Expert and knowledge-based systems in failure analysis. Eng. Fail. Anal. 2 (2), 137e149. Hammond, R., Amezuita, T., Bras, B., 1998. Issues in the automotive parts remanufacturing industry: a discussion of results from surveys performed among remanufacturers. Int. J. Eng. Des. Autom. Spec. Issue Environ. Conscious Des. Manuf. 4 (1), 27e46. Hatcher, G.D., Ijomah, W.L., Windmill, J.F.C., 2011. Design for remanufacture: a literature review and future research needs. J. Clean. Prod. 19, 2004e2014. Hatcher, G.D., Ijomah, W.L., Windmill, J.F.C., 2013. Integrating design for remanufacturing into the design process: the operational factors. J. Clean. Prod. 39, 200e208. Hatcher, G.D., Ijomah, W.L., Windmill, J.F.C., 2014. A network model to assist ‘design for remanufacture’ integration into the design process. J. Clean. Prod. 64, 244e253. Holt, R., Barnes, C., 2010. Towards an integrated approach to "Design for X": an agenda for decision-based DFX research. Res. Eng. Des. 21, 123e136. Ijomah, W.L., McMahon, C.A., Hammond, G.P., Newman, S.T., 2007. Development of design for remanufacturing guidelines to support sustainable manufacturing. Robot. Comput. Integr. Manuf. 23, 712e719. Ikeda, A., 2017. Remanufacturing of automotive parts in Japanese market. Procedia CIRP 61, 800e803. Jeandin, T., Mascle, C., 2016. A new model to select fasteners in design for disassembly. Procedia CIRP 40, 425e430. Kalayci, C.B., Gupta, S.M., 2013. Artificial bee colony algorithm for solving sequencedependent disassembly line balancing problem. Expert Syst. Appl. 40, 7231e7241. Kaufmann, A., Gupta, M.M., 1991. Introduction to Fuzzy Arithmetic:Theory and Application. Van Nostrand Reinhold, New York. King, A.M., Burgess, S.C., 2006. Reducing waste:repair recondition,remanufacture or recycle. Sustain. Dev. 14, 257e267. Lee, H.B., Cho, N.W., Hong, Y.S., 2010. A hierarchical end-of-life decision model for determining the economic levels of remanufacturing and disassembly under environmental regulations. J. Clean. Prod. 18 (13), 1276e1283. Mabee, D.G., Bommer, M., Keat, W.D., 1999. Design charts for remanufacturing assessment. J. Manuf. Syst. 18 (5), 358e366. ~o, I., Reis, L., 2019. Failure mode analysis of a 1.9 turbo Mateus, J., Anes, V., Galva diesel engine crankshaft. Eng. Fail. Anal. 101, 394e406. Pramod, V.R., Devadasan, S.R., Muthu, S., et al., 2006. Integrating TPM and QFD for improving quality in maintenance engineering. J. Qual. Maint. Eng. 12 (2), 150e171. Rao, S.X., Wang, Y., Wan, Z., 1999. Corrosion failure mode in the diagnosis of mechanical equipment based on the failure rule. Chin. J. Mech. Eng. 6 (6), 387e406. Saavedra, Y.M.B., Barquet, A.P.B., Rozenfeld, H., et al., 2013. Remanufacturing in Brazil: case studies on the automotive sector. J. Clean. Prod. 53, 267e276. Seitz, M.A., 2007. A critical assessment of motives for product recovery: the case of engine remanufacturing. J. Clean. Prod. (11e12), 1147e1157. Sherwood, M., Shu, L.H., 2000a. Supporting design for remanufacture through waste-stream analysis of automotive remanufacturers. Ann. CIRP 49 (1), 87e90. Sherwood, M., Shu, L.H., 2000b. Modified FMEA using analysis of automotive remanufacturer waste streams to support design for remanufacture. In: Proceeding of 2000 ASME DETC and CIE Design Theory and Methodology Conference, Baltimore, MD,USA, September 10-14,2000. DETC2000/DTM-14567. Shu, L.H., Flowers, W.C., 1999. Application of a design-for-remanufacture framework to the selection of product life-cycle fastening and joining methods. Robot. Comput. Integr. Manuf. 15, 179e190. Shu, L.H., 2004. Biomimetic design for remanufacture in the context of design for assembly. Proc.Instn Mech. Engrs Part B: J. Eng. Manuf. 218, 349e352. Song, S., Liu, M., Liu, G., et al., 2016. Theories and design methods for proactive remanufacturing of modern products. J. Mech. Eng. 52 (7), 133e141. Soh, S.L., Ong, S.K., Nee, A.Y.C., 2014. Design for disassembly for remanufacturing: methodology and technology. Procedia CIRP 15, 407e412. Subramoniam, R., Huisingh, D., Chinnam, R.B., 2009. Remanufacturing for the automotive aftermarket-strategic factors: literature review and future research needs. J. Clean. Prod. 17 (13), 1163e1174. Sundin, E., 2004. Product and Process Design for Successful Remanufacturing. PhD diss. Linkopings University. Sundin, E., Bras, B., 2005. Making functional sales environmentally and economically beneficial through product remanufacturing. J. Clean. Prod. 13, 913e925. Tchertchian, N., Millet, D., Pialot, O., 2013. Modifying module boundaries to design remanufacturable products: the modular grouping explorer tool. J. Eng. Des. 24 (8), 546e574.

14

X. Zhang et al. / Journal of Cleaner Production 239 (2019) 117967

Tseng, H.E., Chang, C.C., Lee, S.C., Huang, Y.M., 2018. A Block-based genetic algorithm for disassembly sequence planning. Expert Syst. Appl. 96, 492e505. Wang, H., Jiang, Z., Zhang, X., et al., 2017b. A fault feature characterization based method for remanufacturing process planning optimization. J. Clean. Prod. 161, 708e719. Wang, J., Zhang, Y., Zhao, Q., Zhang, H., 2017a. Study of fatigue defect critical threshold of remanufacturing cores and judgment of re-manufacturability based on fatigue defect. J. Mech. Eng. 53 (5), 41e49. Williams, J., Shu, L., 2000. Analysis of toner-cartridge remanufacturer waste stream. In: Proceedings of the 2000 IEEE International Symposium on Electronics and the Environment, May 8e10, San Francisco, California, USA, pp. 260e265. Wu, Y.-H., Ho, C.C., 2015. Integration of green quality function deployment and fuzzy theory: a case study on green mobile phone design. J. Clean. Prod. 108, 271e280. Yager, R.R., 1981. A procedure for ordering fuzzy subsets of the unit interval. Inf. Sci. 24 (2), 143e161. Yang, S.S., Ong, S.K., Nee, A.Y.C., 2013. Design for remanufacturing-a fuzzy-QFD approach. In: Proceedings of 20th CIRP International Conference on Life Cycle Engineering, Singapore, pp. 633e638. Yang, S.S., Ong, S.K., Nee, A.Y.C., 2015. Handbook of Manufacturing Engineering and

Technology. Springer-Verlag London, pp. 3195e3215. Yang, S.S., Ong, S.K., Nee, A.Y.C., 2016. A Decision support tool for product design for remanufacturing. Procedia CIRP 40, 144e149. Yang, D., Mak, C.M., 2017. An assessment model of classroom acoustical environment based on fuzzy comprehensive evaluation method. Appl. Acoust. 127, 292e296. Yang, S.S., Nasr, N., Ong, S.K., Nee, A.Y.C., 2017. Designing automotive products for remanufacturing from material selection perspective. J. Clean. Prod. 153, 570-57. Yüksel, H., 2010. Design of automobile engines for remanufacture with quality function deployment. Int. J. Sustain. Eng. 3 (3), 170e180. Zaitsev, N., Dror, S., 2013. Water quality function deployment. Qual. Eng. 25 (4), 356e369. Zhang, X., Yu, G., Hu, Z., et al., 2014. Parallel disassembly sequence planning for complex products based on fuzzy-rough sets. Int. J. Adv. Manuf. Technol. 72 (1e4), 231e239. Zhang, X., 2019. User selection for collaboration in product development based on QFD and DEA approach. J. Intell. Manuf. 30 (5), 2231e2243. Zwolinski, P., Lopez-Ontiveros, M.A., Brissaud, D., 2006. Integrated design of remanufacturable products based on product profiles. J. Clean. Prod. 14, 1333e1345.