A Mass-Customization-Based Remanufacturing Scheme Design Method for Used Products

Abstract

Remanufacturing scheme design (RSD) is an essential step in the restoration and upgrading of used products. However, the quantity of remanufactured products is growing rapidly, and customers have personalized demands for remanufactured products that lead to shorter design cycles. In addition, the used products are scrapped due to their own defects, such as performance failure and functional degradation, which correspond to the inherent remanufacturing demand (IRD) of used products. Faced with large quantities of used products, how to quickly develop reasonable remanufacturing schemes for satisfying customers’ individual demands and the IRD is an urgent problem to be solved. To address these issues, a mass customization-based RSD method is proposed. First, remanufacturing demand comprising customer demand and the IRD is analyzed to determine the RSD targets and remanufacturing types. Then, the RSD methods are intelligently selected based on the remanufacturing types, which include restorative remanufacturing, upgrade remanufacturing and hybrid remanufacturing, while the hybrid contains restorative remanufacturing and upgrade remanufacturing. Moreover, the restorative remanufacturing scheme is generated to satisfy the restorative remanufacturing targets based on reverse engineering (RE) and the tool contact point path section line (TCPPSL) method. After used products are restored, case-based reasoning (CBR) is used to retrieve the case that best matches the upgrade remanufacturing targets, while the grey relational analysis (GRA) algorithm is applied to calculate the similarity between cases. Finally, the feasibility of this method is verified by considering the RSD of a used lathe. The results indicated that the proposed approach can rapidly help designers to obtain remanufacturing solutions for satisfying the customer demand and IRD.

1. Introduction

Remanufacturing is an effective way to promote the reuse of waste resources, energy conservation and emission reduction [1]. However, an unreasonable remanufacturing scheme will lead to increased consumption of energy and materials, which will lose the advantages of remanufacturing. The remanufacturing scheme mainly includes remanufacturing technology development, remanufacturing process planning, and remanufacturing parameters development, all of which affect the smooth implementation of used product remanufacturing, e.g., laser cladding repair for lathe guide, and laser power, spot diameter and scanning speed changes will affect the surface accuracy of the guide, and will also affect the repair energy consumption. To guarantee remanufacturing reliability and reduce remanufacturing costs, it is necessary to design a reasonable remanufacturing scheme based on customer demand (CD) and the quality of the used product [2].

However, the quantity of used products is gradually increasing, and the customers’ individual demands for remanufactured products are becoming diverse and personalized leading to shorter design cycles. Facing these problems, the traditional design methods can no longer meet the mass customization production. In addition, as a result of part failure, performance depreciation and functional decline in end-of-life products, these lead to the inherent remanufacturing demand (IRD) of the used products. For these reasons, remanufacturers need to be flexible and quick to develop appropriate remanufacturing schemes based on individual demand and the quality of the used product, which makes the RSD more complex. To address these issues, it is crucial to propose an advanced RSD method to solve these problems. Currently, there are many scholars who have conducted studies on RSD, Huang et al. (2020) proposed an RSD method based on the reconstruction of the incomplete information of used parts, which can solve the uncertain and highly personalized problems in remanufacturing [3]. Du et al. (2013) proposed a reuse-oriented redesign method based on axiomatic design theory and quality function deployment, which can standardize and optimize the redesign process [4]. Cao et al. (2014) developed a redesign method for used products, which applied the object meta-model dog to express the functional set of products for the retrieval of similar remanufacturing schemes [5]. The aforementioned research developed new RSD methods, which could contribute to the accurate generation of remanufacturing schemes and the effective implementation of RSD.

However, these methods only consider the development of remanufacturing schemes for an individual used product and are not applicable to mass customization-based RSD. Mass customization is to meet the premise of mass production to achieve customized individual new products [6,7,8]. Different from mass customization-based new product design, mass customization-based RSD should consider not only the CD but also the IRD of the used product, this makes it harder to rapidly develop a suitable remanufacturing scheme. CD mainly contains restorative demand and upgrade demand, the restorative demand is to restore the used product to its original performance, and the upgrade demand is to enhance the performance or function of the product. IRD mainly contains physical restoration, technology upgrades and economic improvement. Physical restoration means that the size or performance is restored to its original state, technology upgrades are the product performance and functionality enhancements, economic improvement refers to the reduction in costs and consumption during the service of the product. RSD needs to satisfy both CD and IRD, while the combination of the two different demands will generate different remanufacturing types. Furthermore, CD data are generally vague and diverse, being expressed with popular and emotional vocabulary, and it is difficult for designers to accurately capture the real intentions of customers, these make it impossible to accurately determine the remanufacturing type. To address this, researchers proposed Quality Function Deployment (QFD) to analyze the CD data [9,10]. Unfortunately, QFD is a manual analysis method that cannot handle large amounts of data. Other scholars have proposed to use the vector space model (VSM) for describing customer demand [11]. However, it cannot quickly extract the key information of customer demand. CD is the main target of RSD, and inaccurate analysis of customer demand will lead to the selection of the wrong remanufacturing type while resulting in unreasonable design solutions. Moreover, even if the CD is analyzed and the design targets are acquired, how to quickly determine the remanufacturing type is also a difficult problem to solve. The remanufacturing types are restorative remanufacturing, upgrade remanufacturing and hybrid remanufacturing, the hybrid remanufacturing contains restorative remanufacturing and upgrade remanufacturing. Different remanufacturing types require different RSD methods. As the failure features of the used products are diverse and uncertain, it is hard to develop a standardized restorative remanufacturing design method. To deal with it, it needs accurate extraction of failure feature parameters, while generating the remanufacturing process paths and parameters by using the CAD/CAM system. Different from restorative remanufacturing, upgrade demand generally includes performance upgrades and function upgrades. While used products (called cores) are used as blank materials in RSD, the structures, materials and performance parameters have mostly standard values. Hence, the designers can reuse existing technology solutions and processing parameters for upgrade remanufacturing by using intelligent methods. According to the above analysis of remanufacturing types, different remanufacturing types have different properties, so it is necessary to apply different design methods for RSD.

For improving design efficiency, intelligent and advanced technologies need to be used to generate remanufacturing schemes. For restorative remanufacturing, the main reason for product scrapping is component failure, including wear, corrosion and fracture. This results in parts with missing dimensions, and it is very difficult to accurately repair the parts based on incomplete dimensional data. To address this, laser cladding [12], thermal spraying [13], and welding [14] are applied to restore the used parts. Yet, due to the uncertainty of the incomplete information, it is impossible to set the process parameters and process routes accurately, which makes it difficult to develop the restorative remanufacturing scheme. For reconstructing the incomplete information, reverse engineering has been used to measure the size of the used part [15], and the size of the detective part can be obtained by fitting it to the original part model. For the restoration of failure features, the processing path and processing parameter are important for the implementation of remanufacturing. CAD/CAM is an effective method that can generate tool paths for the virtual repair process [16], and it has been widely applied in the recovery of mechanical shafts [17] and damage blade repair [18]. As the upgrade remanufacturing scheme is more uniform for the same type of products, the historical remanufacturing knowledge can be reused to generate a new upgrade scheme. For intelligently extracting helpful knowledge, many scholars have applied the CBR technology to retrieve the most similar case to satisfy the design targets [11]. CBR is an effective method to improve the reuse of existing knowledge, which can be applied to the solution of the upgrade remanufacturing scheme. In conclusion, the mass customization-based RSD process is cumbersome and complex, which contains remanufacturing demand analysis, remanufacturing type determination and remanufacturing scheme generation. So, it is crucial to develop a valid method for mass customization-based RSD.

To rapidly and accurately generate the remanufacturing scheme, this paper proposes a mass customization-based RSD method for used products. First, the customer demand is analyzed based on Kansei engineering (KE) and K-means. Meanwhile, the remanufacturing type is determined based on CD and IRD, while rule-based reasoning (RBR) is used to retrieve the suitable remanufacturing type. Then, the RSD methods are selected based on the remanufacturing type. For restoring the used parts, the RE is used to reconstruct the model of the defective part, and the TCPPSL is applied to plan the restoration path. To upgrade the performance and function of the product, the CBR is used to retrieve the most similar upgrade case, which can rapidly generate the upgrade remanufacturing scheme. In summary, the main contribution of this study comprises four aspects: (1) a mass customization-based remanufacturing scheme design method for used products is developed for rapid and flexible generation of suitable remanufacturing schemes, which can adaptively switch remanufacturing schemes based on the CD and IRD; (2) development of a customer demand data analysis method for identifying the customers’ perceptual demand and quickly classifying the main demand features; (3) integrating customer demand and inherent remanufacturing demand to determine design targets and remanufacturing types, and applying rule-based reasoning to intelligently select RSD methods; (4) a hybrid remanufacturing scheme generation methods are proposed to satisfy different types of remanufacturing demand that helps precisely implement used product remanufacturing.

2. Literature Review

A typical RSD process contains at least two steps: (1) remanufacturing demand collection and analysis; (2) remanufacturing schemes generation. To date, a great deal of research has been devoted to these two steps.

2.1. Remanufacturing Demand Collection and Analysis

Remanufacturing demand contains CD and IRD, and CD is the main factor affecting remanufacturing targets and remanufacturing schemes. As for CD collection, CD data are generally vague and diverse, being expressed with popular and emotional vocabulary, and it is difficult for designers to accurately capture the real intentions of customers. To address this, it is necessary to apply a unified template for collecting CD data; hence, many scholars have focused on this. Kück and Freitag (2020) proposed local k-nearest neighbor models, which can predict CD in an industrial context [19]. Hartono (2020) proposed a modified Kansei engineering method for sustainable service design that can consider human-center demand and perceive the emotional satisfaction of the customer [20]. Dong et al. (2021) proposed a fuzzy mapping method for Kansei demand interpretation considering the individual Kansei variance, which enables the designer to more accurately understand the customer’s individual demand in the context of MC [21,22]. It can be seen from the literature mentioned above that Kansei engineering (KE) is an effective method for accurately describing CD, which has an emotional description, and intelligent methods are applied to analyze CD, which can deal with large amounts of CD data rapidly.

For remanufacturing demand analysis, the main purpose is to analyze the remanufacturing type and extract the design targets. The remanufacturing type is jointly decided by CD and IRD, and the design targets are mainly to meet the customer demand. Thus, it is essential to accurately analyze the CD data for extracting the design targets. QFD is a common method to translate customer requirements into engineering features [23,24]. The Kano model can describe the link between the attributes of the product and customer satisfaction [25]. There is no doubt that these methods can analyze the weighting of individual CD information and map it to product engineering features. Nevertheless, in the Internet era, CD data is exploding and it is difficult to rapidly obtain the key customer demand by using a traditional method. To address this, many scholars have adopted some intelligent methods based on data mining and data classification in recent years. Duan et al. (2019) proposed an improved K-means algorithm to assign failure modes to priority levels, which can determine the critical failure modes [26]. Jiang et al. (2019) applied grey correlation analysis to identify the key drivers of remanufacturing eco-performance that can improve the accuracy of remanufacturing eco-performance assessment [1]. Jiang et al. (2020) utilized the improved local mean decomposition (ILMD) method to decompose the data, which can obtain several data series to compensate for the low prediction accuracy caused by the small amount of data [27]. These studies provide precious opinions and contributions, which can rapidly handle large amounts of data. K-means is especially outstanding in classifying data and extracting key information. Thus, K-means can be used to cluster customer demand data and find out key customer demand data to help determine the design targets.

After analyzing the CD, designers need to determine the remanufacturing types for selecting the suitable RSD methods. Remanufacturing types are determined by the CD and IRD. CD is the main target of RSD, which includes upgrade demand and restorative demand. IRD is the need to restore its own defects to achieve normal service, which involves physical restoration, technology upgrades and economic improvement. In addition, remanufacturing types include upgrade remanufacturing, restorative remanufacturing, and a hybrid of the two. Remanufacturing types are used for making decisions based on the combination of CD and IRD, and different combinations correspond to different remanufacturing types. Therefore, it is necessary to quickly formulate remanufacturing types by using an intelligent method. Rule-based reasoning (RBR) is an intelligent method for solving new problems by concluding rules that include the knowledge of problem-solving [28], and RBR has been widely applied in process planning [29], patient education [30], risk assessment [31], and environmental decisions [32] to retrieve the solution that best matches the target rule from the rule base. Hence, RBR is applied to resolve the suitable remanufacturing types from the rule base in this study, which is conducive to the rapid selection of suitable RSD methods.

2.2. Remanufacturing Schemes Generation

Once the remanufacturing type is determined, a reasonable remanufacturing scheme should be rapidly developed. Restorative remanufacturing requires the development of a corresponding remanufacturing scheme according to the failure features of the used products. The main reason for product scrapping is component failure, including wear, corrosion and fracture. In addition, the failure features of the used products are diverse and uncertain, which makes it difficult for a designer to accurately generate the remanufacturing scheme. Many scholars have researched in response to this problem. Du et al. (2013) proposed a redesign method based on axiomatic design theory and quality function deployment, which can standardize and optimize the redesign process [4]. Jiang et al. (2019) proposed a hybrid method incorporating a rough set and CBR for remanufacturing process planning that can rapidly retrieve the most suitable remanufacturing process scheme [33]. Chen et al. (2020) proposed a knowledge-based method for increasing the eco-efficiency of remanufacturing process planning, which can improve the efficiency of process planning and realize the legacy and development capabilities of process planning knowledge [34]. There is no doubt that these studies have greatly promoted the rapid generation of remanufacturing schemes. However, the failure features of the used products are random and difficult to quantify, and it is difficult to retrieve a fully consistent remanufacturing scheme, which will affect the precise implementation of remanufacturing. Reverse engineering (RE) is an effective method for identifying the area and amount of damage, which is conducive to the formulation of remanufacturing process parameters, and many scholars have applied this method to identify failure features. Li et al. (2017) proposed a method integrating the reverse-engineering-aided remanufacturing process, which can accurately determine the area and degree of damage of a used part and generate the remanufacturing process path rapidly [16]. Huang et al. (2020) proposed an RSD method based on the reconstruction of the incomplete information of used parts, which can solve the uncertain and highly personalized problems in remanufacturing [3]. Be and Ama (2019) proposed an integrated RE method for recovering the shaft of a rotary stretch-bending machine, which can repair any mechanical part without the support of the original manufacturer [17]. From the literature mentioned above, RE can be carried out to reconstruct a model of used products and identify the area and degree of damage by comparing the damaged model and the original model. Moreover, the remanufacturing process is a vital step of the remanufacturing scheme, and it is essential to resolve the remanufacturing process path and parameters for recovering damaged parts. To address these, simulated annealing (SA) particle swarm optimization (PSO) is applied to select the remanufacturing process route with the best eco-efficiency [35]. Furthermore, Genetic Algorithm (GA) and GAMS optimization software are used to solve the optimal closed-loop supply chain, which can help organizations maximize profits while minimizing their environmental impact by achieving sustainability [36,37]. Wang et al. (2017) applied the Genetic Algorithm (GA) and Artificial Neural Network (ANN) and they were integrated into the optimal remanufacturing process scheme [38]. These methods provide the ability to optimize the existing remanufacturing process schemes to obtain process parameters that better meet the optimization objectives. However, since the failure features are random, and the amount, the area and the shape of the defect are unknown, it is difficult to generate accurate remanufacturing process parameters and paths. Currently, the tool contact point path section line (TCPPSL) method is applied to generate a machining path which can rapidly generate the machining path based on the surface profile of the damaged area [9,18], and the CAM software can simulate the machining process to verify the feasibility of the machining path. Thus, the restorative remanufacturing scheme can be obtained rapidly and accurately by using RE and the TCPPSL.

Remanufacturing demand includes not only restorative demand, but also upgrade demand. Upgrade demand generally includes performance upgrades and function upgrades. Since the used products (called cores) are used as blank materials in RSD, the structures, materials, and performance parameters of the used products have mostly standard values. Hence, the technical parameters and process parameters of upgrade remanufacturing are repeatable and reusable. Case-based reasoning (CBR) is an intelligent way to solve new problems by reusing historical cases. It directly mimics human thinking and quickly retrieves the most similar cases from previous cases [39]. CBR has been widely applied in risk identification [40], product design [41], boiler combustion [42], cost estimation [43], etc. Yu et al. (2020) proposed a CBR method based on molding features, which can better portray the nuances between modeling features with higher retrieval accuracy [44]. Lee et al. (2019) proposed a novel knowledge-centric innovative service design (KISD) model, which can accelerate customized innovative service design, where CBR is used to identify critical problems by extracting previous experience [45]. Li et al. (2020) proposed a hybrid method that integrates remanufacturing process planning with blockchain (BC) and CBR, which can ensure the security of knowledge transmission and quickly retrieve useful knowledge [46]. Therefore, CBR is an effective way to rapidly retrieve the most similar upgrade remanufacturing scheme from the remanufacturing scheme case base.

The literature reviews show that there is considerable research devoted to customer demand analysis and remanufacturing scheme generation. However, these studies only focus on customer demand and remanufacturing schemes for individual products, mass customization-based RSD requires shorter design cycles and higher design efficiency, and remanufacturing demand needs to take into account both CD and IRD, these make the MC-based RSD difficult to be implemented. Hence, there is a need to integrate intelligent and advanced customer demand analysis and remanufacturing scheme generation methods to form a new MC-based RSD method.

3. The RSD Framework Based on MC

The purpose of MC-based RSD is to adaptively generate remanufacturing schemes that satisfy CD and IRD. Therefore, RSD based on MC mainly involves remanufacturing demand analysis and remanufacturing scheme generation, the details are shown in Figure 1. The major steps involve customer demand analysis, remanufacturing type determination, RSD method selection, restorative remanufacturing and upgrade remanufacturing scheme generation. The detailed process is described as follows.

3.1. Remanufacturing Demand Analysis

Remanufacturing demand is the goal of RSD. CD is the major factor of used product RSD and determines the remanufacturing types of used products. In the Internet era, CD information has seen explosive growth, is non-professional and cannot clearly describe the engineering features of remanufactured products, which makes it difficult to extract accurate design targets. Therefore, it is necessary to propose an intelligent CD analysis method that involves CD data collection, processing and classification to address these problems. CD involves two main categories: restorative demand and upgrade demand. The details are shown in Figure 2.

3.1.1. CD Analysis

(1)

CD data collection

Different customers have different demands and expressions for used products. To normatively describe CD, it is necessary to establish a CD data collection template to standardize the expression of different CD information, facilitate the processing of CD data, and accurately extract key demand features. The CD data collection table is shown in

Table 1. The CD data collection table.

Customer Number	Demand Type	Demand Description	Demand Level
			0	1	2	3	4
1	Surface repair	Guiderail wear					√
2	Performance repair	Surface hardness					√
3	Function upgrade	CNC device				√
4	Appearance upgrade	Lightweight			√
…	…	…	…	…	…	…	…

.

CD for used products mainly includes the restorative and upgrade types, such as surface restoration and CNC upgrades. Demand description is the specific description of the demand type, which can more accurately express the content of CD. The demand level expresses the urgency of CD, which includes a total of five levels. Level 0 represents that there is no related demand, level 1 represents that the related demand is slight, level 2 indicates that the demand intensity is general, level 3 represents that the related demand is strong, and level 4 represents that the related demand is very strong. Customers can select the relevant demand intensity level in the corresponding cell. Through the standard expression of CD, designers can more accurately extract the key demand information.

(2)

CD data processing

CD data are colloquial and inaccurate, which is not conducive to accurately describing remanufacturing targets. To accurately describe the remanufacturing targets, KE is applied to the perceptual vocabulary to evaluate the content of CD, and semantic difference (SD) is used to obtain perceptual demand value for remanufactured products. Moreover, the Likert scale is used to establish the relationship between customers, perceptual evaluation and demand intensity, the details of which are shown in

Table 2. The five-point Likert scale used for CD.

Number	Object	Perceptual Evaluation	Demand Intensity
			1	2	3	4	5
1	Guiderail surface	Smooth					√
2	Surface hardness	Hard				√
3	CNC device	Convenient				√
4	Appearance	Light				√
…	…	…	…	…	…	…	…

.

Here, object represents the features of the product, perceptual evaluation means the customer’s perceptual description of product features, demand intensity represents the recognition of the perceptual evaluation, level 1 means strongly opposed, level 2 means disagree, level 3 means neutral, level 4 means agree, and level 5 means strongly agree.

To make CD data suitable for computer recognition, it is necessary to convert the data into binary code so that the computer can process them. In the binary coded data, a value of 1 means agreement with the demand intensity, and a value of 0 means disagreement. The conversion process is shown in

Table 3. CD data coding.

Number	Guiderail Surface	Surface Hardness	CNC Device	Appearance	…
	Smooth	Hard	Convenient	Light	…
1	1	1	1	1	…
2	0	1	1	0	…
3	1	0	0	1	…
4	1	1	0	0	…
…	…	…	…	…	…

.

(3)

CD data classification

Due to the very large amount of demand data, it is necessary to classify the data to identify the main demand types for accurately extracting the remanufacturing targets. As CD data has been converted to binary data, and CD can be categorized by searching for similar binary data. This paper uses the K-means algorithm to classify data; the specific steps are as follows.

Step 1: K demand data are randomly selected from the demand data set as the initial clustering center.

Step 2: The Euclidean distance algorithm is used to calculate the distance between each demand data point and the cluster center, the calculation process is as follows.

$$dist(x_i,x_j)=\sqrt{{\displaystyle \sum _{i,j=1}^n{\left(x_i-x_j\right)}^2}}$$

(1)

where dist represents the distance between two demand data points, $x_i$, $x_j$ represent the i-th and j-th demand data point, respectively, and n means that there are n sets of demand data.

Step 3: The centroids of the new clusters are recalculated. The calculation process is as follows.

$$Cente{r}_k=\frac{1}{\left|C_k\right|}{\displaystyle \sum _{x_i\in C_k}{x}_i}$$

(2)

where Center_k represents the k-th centroid and $\left|C_k\right|$ means the number of k-th demand data clusters.

Step 4: Steps 2 and 3 are repeated until the centroids of the demand data cluster do not change, at which point the iteration ends.

3.1.2. IRD Analysis

MC-based RSD is different from the new product design, as the products are scrapped due to part failure, functional degradation and high maintenance costs. For used products to work properly, it is necessary to restore the product functions and performance through remanufacturing; we call this the IRD of the used products, which involves wear restoration, performance restoration, and function upgrade, e.g., MC-based RSD not only satisfies the individual CD but also considers the IRD of used products. Hence, it is necessary to conduct a coupling analysis of the demand and determine the appropriate remanufacturing types.

From the perspective of product life, product scrapping occurs when the life of a product is lower than the threshold of normal use. The life of the product mainly includes its physical life, technical life and economic life. The cause of each end-of-life is a problem that needs to be addressed in remanufacturing; it is also the IRD of the used products when they are remanufactured. The IRD of used products mainly includes physical restoration, technology upgrades and maintenance cost reduction, and the IRD of used products needs to be determined based on the historical service data of each product, the maintenance cost data, and the detection data collected during recycling. The specific content is shown in Figure 3.

3.1.3. Remanufacturing Type Determination

The CD and IRD jointly determine the remanufacturing types of used products. Remanufacturing types for used products are generally divided into three categories: restorative remanufacturing, upgrade remanufacturing, and a hybrid of the two. Furthermore, if the CD is restorative demand and the used products are decommissioned because of backward technology, the used products can be reused directly, because the performance of the used products is not degraded and can satisfy the demand of customers without restoration. The remanufacturing type is determined by CD and IRD, and different demand combinations have different remanufacturing types. The specific remanufacturing type formulation rules are shown in

Table 4. Remanufacturing type formulation rules.

ID	IRD	CD	Remanufacturing Type
1	Physical restoration	Restorative demand	Restorative remanufacturing
2	Improve economy	Restorative demand	Restorative remanufacturing
3	Physical restoration	Upgrade demand	Restorative and upgrade remanufacturing
4	Improve economy	Upgrade demand	Restorative and upgrade remanufacturing
5	Technology upgrade	Upgrade demand	Upgrade remanufacturing

.

To automatically push remanufacturing types, RBR is proposed to intelligently formulate the remanufacturing types; the specific process is shown in Figure 4.

To accurately extract remanufacturing types, first, it is necessary to analyze the CD and IRD of the used products. Then, the remanufacturing types that meet the conditions in the rule base are retrieved. If the IRD and CD match the rule base, the remanufacturing types are output. Otherwise, the search fails, requiring the search condition to be modified and the remanufacturing types to be retrieved again. Finally, according to the remanufacturing types, the appropriate design methods for the remanufacturing scheme are selected.

Since the number of remanufacturing type formulation rules is small, the production representation method is used to express the rules, and the judgement rule method is used to formulate the remanufacturing type. That is, if <condition> then <action>; when the conditions match, the corresponding judgement result is output. The specific rules are expressed as follows:

If the IRD is physical restoration or improvement of the economy and the CD is the restorative demand, then the remanufacturing type is restorative remanufacturing;

If the IRD is physical restoration or improvement of the economy and the CD is the upgrade demand, then the remanufacturing type is restorative and upgrade remanufacturing;

If the IRD is a technology upgrade and the CD is the upgrade demand, then the remanufacturing type is upgrade remanufacturing.

3.2. Remanufacturing Scheme Generation

After analyzing the remanufacturing demand and formulating the remanufacturing types, the design method can be intelligently switched to generate a suitable remanufacturing scheme. Used products are usually unable to operate normally due to part and component failure. Regarding normal product use, customers hope that the original product performance can be restored as much as possible or even exceeded through remanufacturing. However, due to long-term service, wear and the loss of technical documents, it is difficult to obtain complete information on used products and develop an accurate restorative remanufacturing scheme based on the failure features. In this study, RE is used to obtain defect data of the used parts, and the TCPPSL is applied to generate the remanufacturing process path for restoring the damaged parts. Different from restorative remanufacturing, upgrade remanufacturing generally involves technology upgrades or performance upgrades, and the upgrade mode is relatively unified. Previous remanufacturing knowledge can be reused to formulate remanufacturing schemes. Hence, it is necessary to select a suitable RSD method based on the remanufacturing type and remanufacturing demand.

3.2.1. Restorative Remanufacturing Scheme Generation

Since the parts of the product have become worn, broken, and corroded after long-term service and the technical documents have also been lost, it is difficult to recover the performance of the used parts based on incomplete information. Hence, it is necessary to use RE technology to reconstruct the incomplete data of used parts and develop a suitable remanufacturing process to restore the used parts. First, according to the CD and the damage information of the used products, 3D scanning equipment is used to extract point clouds of the damaged parts. Then, the integral iteration method is used to match the damage model with the original model to reconstruct the missing point cloud model. Finally, the TCPPSL method is applied to generate the remanufacturing process path. The details are as follows.

(1)

Geometric model constructions of the used product

Based on RE, the data of failed parts (point cloud) are collected, the damaged area can be determined by matching with the original model of the product, and the amount of damage can be determined according to the node coordinates of the defect area. The damage model reconstruction process is shown in Figure 5.

Step 1: Point cloud data collection and processing. A Handy Scan 300 precision scanner is used to extract the point cloud of the defect component, and the Geomatics Studio software is used to remove the noise points in the point cloud. Moreover, the original models of the used components are meshed.

Step 2: Determination of the area and amount of damage. First, it is necessary to create a CAD model of the used parts; then, the damaged areas are determined by matching the used parts model with the original model. Finally, the amount of damage is determined based on node coordinates of the defect area. The specific process is as follows.

(a)

Creation of the mesh model of the original component

The ANSYS software is used to mesh the original parts model. First, it is necessary to define the grid size and type and then cut the surface of the parts to form the part model with known area information. Then, the three-dimensional model of the used part is meshed, and the corresponding relationship between the three-dimensional coordinate axis and the model is established. Finally, the coordinates of the broken grid node are derived.

(b)

Damaged area determination

The original model and the used part model are imported into 3D software. Moreover, three reference points of different planes are set up on the two models, and the coordinate systems of the two models are established based on the three reference points. Then, the two models are fitted by matching the two coordinate systems. Finally, the unit integral iteration method is used to compare the two model coordinates to identify the damaged area.

(c)

Determination of the amount of damage

The amount of damage can be calculated according to the node coordinates of the used product, and the area of damage can be divided into two parts based on the Z-X plane and Y-X plane. Then, the amount of damage is calculated through the integral iteration method, as follows.

(i)

Determine the damaged area on plane Z − X

$$A_i={\displaystyle {\int }_{z_{\min }}^{z_{\max }}(Z-Z^{\prime })}dx$$

(3)

where $A_i$ represents the damaged area value between the i-th and (i + 1)-th points on plane Z − X and $dx$ is the derivative along the X-axis.

(ii)

Determine the damage volume

$$V_i={\displaystyle {\int }_{z_{\min }}^{z_{\max }}{A}_i}dy$$

(4)

where $V_i$ represents the damage volume between the i-th and (i + 1)-th points and $d\mathrm{y}$ is the derivative along the Y-axis.

The first integration along the Z-X direction determines the damaged area, and the second integration along the Y-X direction determines the damage volume. The damaged area and volume can be determined by registering the original model and damage model of the used product. After reconstructing the incomplete model of the used product, the machined surface can be extracted from the incomplete model, which provides a basis for the subsequent formulation of the remanufacturing process scheme.

(2)

Remanufacturing process scheme generation

After determining the amount and area of damage, it is necessary to select a suitable restoration method and plan the restoration path. The main failure features of the used products are cracks, wear and corrosion. Therefore, the main method for remanufacturing the used products is additive manufacturing. In this study, laser cladding technology is used to repair the used products, and the TCPPSL method is used to formulate the remanufacturing process path. The specific process is as follows.

(a)

The constraint surfaces should be determined, which is already carried out in step 2. The outer surface of the broken part is the constraint surface, which is the path range.

(b)

The distance between the constraint surfaces needs to be determined between the tool paths [3]. The intersection of the constraint surface and the damaged surface is the tool path, which is shown in Figure 6.

(c)

Assuming that during laser cladding, the size of the spot remains the same, the damaged area can be processed according to the tool paths. The flatness of the cladding layer is mainly affected by the overlap, and the core impact parameter is the overlap distance of the cladding layer [3]. The theoretical overlap distance is shown in Figure 7.

Here, d is the theoretical overlap distance, h is the height of the cladding layer, h_s is the height difference between the two cladding layers, w is the width of the cladding layer, and O₁ and O₂ are the centers of the two cladding layers.

3.2.2. Upgrade Remanufacturing Scheme Generation

Restoring the failed features of used products is a prerequisite for the normal operation of those products. However, with the continuous increase in market demand, customers have put forward higher demands on the performance and functions of remanufactured products. Therefore, it is necessary to develop a suitable upgrade remanufacturing scheme for performance and function upgrade. This study applies the CBR technology to generate the remanufacturing scheme, which can yield the most suitable case for meeting the upgrade demand. This method contains three main parts, namely, case representation, case retrieval and case evaluation, and the specific steps are as follows.

(1)

Case representation

The case contains the description of the upgrade demand information, the description of the used product information, and the remanufacturing scheme information. The Case can be represented by three triples, as follows.

$$Case=\{N,C,S\}$$

(5)

where N represents the case number, C represents the upgrade demand features, and S represents the upgrade remanufacturing scheme information. In addition, $C=\{C_1,C_2,\dots ,C_i,\dots ,C_n\}$ is the upgrade demand features, and $C_i=\{n_i,w_i,v_i\}$ is the upgrade demand feature description, where $n_i$ is the i-th feature name, $w_i$ is the weight of the i-th feature, and $v_i$ is the actual value of the i-th feature. A detailed description of the case is given in

Table 5. Upgrade remanufacturing case description.

Case Number (N(x))
Upgrade demand feature (C)
Used product information:	Product type, structure size, performance, etc.
Demand type:	Rigidity, strength, hardness, CNC, etc.
Demand parameters:	50 N/m, 100 N/mm2, 50HRC, FAUNC system, etc.
Upgrade remanufacturing scheme (S)
Upgrade technology:	Gas metal arc welding, shot peening, PLC, etc.
Technical Parameters:	Particle size, welding speed, welding voltage, etc.

.

(2)

Case retrieval

After completing the construction of the case, the GRA algorithm is used to calculate the similarity between the target case and the historical case, and the similarity is calculated according to Equation (6).

$${\zeta }_j(k)=\frac{\underset{j}{\min } \underset{k}{\min }\left|v_{0K}-v_{jK}\right|+\rho \underset{j}{\max } \underset{k}{\max }\left|v_{0K}-v_{jK}\right|}{\left|v_{0K}-v_{jK}\right|+\rho \underset{j}{\max } \underset{k}{\max }\left|v_{0K}-v_{jK}\right|}$$

(6)

where ${\zeta }_j(k)$ is the similarity between the remanufacturing target case and the historical case, $v_{0K}$ is the value of the k-th feature of the target case, $v_{jK}$ is the value of the k-th feature of the historical case, and $\rho$ is the resolution coefficient, which is generally within (0, 1).

The similarity between each feature of the cases has been calculated. Now, the overall similarity between the cases needs to be calculated according to Equation (7).

$$s_j={\displaystyle \sum _{k=1}^n{w}_k{\zeta }_j(k)}$$

(7)

where $s_j$ represents the overall similarity between the target case and the historical case and $w_k$ is the weight of the k-th feature, which can be calculated by the analytic hierarchy process (AHP).

(3)

Case evaluation

After retrieving the best matching case, it is necessary to evaluate the case because it may have undergone deviation. The designer can use simulation software to simulate and verify the upgrade remanufacturing scheme of the case and use their own design experience to evaluate the simulation results and improve the upgrade remanufacturing scheme.

4. Case Study

To verify the design method, the RSD for a used lathe is used as a case study. First, 100 pieces of CD data were collected through online reviews, questionnaires and customer return visits. In addition, a five-point Likert scale was used to normalize the expression of customer requirement data, the details of which are shown in

Table 6. Standard expression of CD.

Number	Object	Perceptual Evaluation	Demand Intensity
			1	2	3	4	5
1	Gear surface	Smooth					√
2	Surface hardness	Hard				√
3	CNC device	Convenient				√
4	Guiderail surface	Smooth				√
5	Tool holder	Automatic			√
6	Chuck	Automatic blessing		√

.

To process the demand data more easily, the CD data are encoded, as shown in

Table 7. CD coding.

CD Number	Gear Surface	Surface Hardness	CNC Device	Guiderail Surface	Tool Holder	Chuck
	Smooth	Hard	Convenient	Smooth	Automatic	Automatic Blessing
1	1	1	1	1	0	0
2	0	1	1	0	1	0
3	1	0	0	1	0	1
4	1	1	0	0	1	1
…	…	…	…	…	…	…
99	0	1	0	1	1	0
100	1	1	1	0	0	1

.

To accurately identify the remanufacturing targets, K-means is used to classify the CD data, the number of clusters is set to 4, and the Euclidean distance algorithm is used to calculate the similarity of the demand data. The clustering results are shown in Figure 8.

By decoding the demand data, the CD is mainly concentrated into four aspects: gear surface, guiderail hardness, CNC device, and guiderail surface. The number and weights of each demand data point are shown in

Table 8. The number and weights of each demand data point.

CD	Gear Surface	Gear Hardness	CNC Device	Guiderail Surface
Number	22	26	25	22
Weight	0.22	0.26	0.25	0.22

. Then, the CD is mapped to the engineering features, and the engineering targets are gear surface restoration, gear hardness improvement, high degree of automation and guiderail surface roughness improvement. In addition, the main reason for lathe scrapping is part failure, among which gear wear and cracks are the most serious; the details are shown in Figure 9.

In Table 8, the number of the four demand types are listed, and other small quantities of demand data are not listed. In addition, the weights of each demand are calculated by comparison with the total demand quantity.

According to the demand data and the failure data of a used product, the remanufacturing type can be formulated according to the remanufacturing type rules, i.e., restoration and upgrade remanufacturing. Therefore, the remanufacturing scheme of the used lathe includes restorative remanufacturing and upgrading remanufacturing.

To restore the performance of the used gear, RE is used to obtain the corresponding point cloud model, which is shown in Figure 10.

After obtaining the point cloud of the used gear, the original model and point cloud model of the used gear are matched to extract the broken part model, as shown in Figure 11.

Based on the calculation of the distance between the surfaces of the two models, the maximum deviation is 0.1 mm, which is within the allowable deviation. Hence, the two models are matched, and the broken part model is accurate. Then, the broken part model is imported to ANSYS software for meshing, and the coordinates of the nodes can be exported, as shown in

Table 9. Coordinate values of the nodes.

Node Number	X	Y	Z
1	−0.138004846516	0.118687400175	0.303884620107
2	−0.137482163981	0.119124448023	0.303418403807
3	−0.137991743816	0.118701314839	0.303869988304
4	−0.137975248441	0.118718799739	0.303851602804
5	−0.137954434329	0.118740750066	0.303828523745
6	−0.137928066586	0.118768259770	0.303799604410
7	−0.137894457095	0.118802641640	0.303763472469
8	−0.137851425037	0.118845514577	0.303718439994
9	−0.137796119581	0.118898841796	0.303662444427
10	−0.137723207419	0.118963923667	0.303593657164
…	…	…	…

.

According to the coordinate values of the nodes, the total height h of the laser cladding moulding is the difference between the maximum and minimum values of the Z-axis coordinate, which is 0.2 mm, and the tool width w is 0.5 mm, where is the width of the cladding layer. The damage amount can be calculated according to Equations (3) and (4), which is 9.38 mm³. To generate the laser cladding processing path, the model of the used gear is input into the UG software (Siemens PLM Software company, Plano, TX, USA), and the processing path can be simulated in the UG software based on the coordinates of the nodes and the volume of the broken part. The tool path is shown in Figure 12.

After restoring the gear, the lathe can run normally. Otherwise, the lathe needs to be upgraded based on the upgrade demand, which mainly requires a smooth gear surface, improvement in the gear hardness, a high degree of automation and guiderail surface roughness improvement. In this case, the used lathe is CA6136, the hardness of the gear is 55 HRC, and the guiderail surface roughness is Ra 1.6 μm. The customer hopes to improve on the original performance of the lathe, and the process designers develop suitable performance parameters based on the CD, namely, the hardness of the gear is improved to 60HRC, the guiderail surface roughness is Ra 0.8 μm, and the FANUC system selected is the oi-TF plus. Subsequently, the similarity is calculated by the GRA algorithm between the target case (N(x)) and the existing cases (N(1), N(2), N(3), N(4), N(5)); the details are shown in

Table 10. Details of the new case and existing cases.

Case Number	Gear Surface	Hardness	Surface Roughness	CNC Device
N(x)	Better	60HRC	Ra0.8	Better
N(1)	General	55HRC	Ra1.2	General
N(2)	Better	58HRC	Ra0.8	Better
N(3)	Better	60HRC	Ra1.6	General
N(4)	General	56HRC	Ra1.2	Better
N(5)	General	61HRC	Ra0.8	General

.

MATLAB is used to calculate the overall similarity between the existing case and the target case, and the calculation results are shown in

Table 11. The similarity calculation results.

Case Number	N(1)	N(2)	N(3)	N(4)	N(5)
Overall similarity	0.8437	0.9605	0.9780	0.8317	0.8272

. This table shows that the similarity of case N(3) exceeds 0.9, which indicates that it is most similar to the target case. The detailed information of case N(3) is shown in

Table 12. The detailed information of case N(3).

Serial Number	Design Targets	Remanufacturing Technology	Device Type	Technical Parameters
1	Gear surface smooth	Sandblasting	Sand blasting machine	Particle size: 1 mm, Injection pressure: 0.7 MP, Injection distance: 200 mm
2	Gear hardness improvement	Frequency hardening	GP0001	Oscillation frequency: 200 kHz, Feed speed: 50 mm/s, Output power: 80 Kw
3	Guiderail surface roughness	Grinding	MX0001	Spindle speed: 100 r/min, Shift motion: 50 mm, Travel speed: 0.1 m/min
4	CNC device	CNC upgrade	FANUC-oi-TF plus	Tool holder: CK110-4M, Maximum torque of motor: 12.7 N⋅m, Driver: Siemens SINAMIC S120, Frequency converter: WJ200-075HF

.

According to case N(3), the upgrade demand for the lathe can be satisfied, and the upgraded lathe is shown in Figure 13.

5. Discussion

This study proposes an MC-based RSD method for used products that can satisfy customers’ individual demands and the IRD. As shown, the method can analyze the remanufacturing demand accurately and rapidly generate a suitable remanufacturing scheme. The detailed remanufacturing scheme is as follows.

RE is applied to obtain the three-dimensional solid model of a used gear, and laser cladding is used to restore the broken part. The appearance of the gear surface is improved by sandblasting, which can remove surface burrs and make the surface smoother. To improve the wear resistance of the gear, high-frequency hardening is used to raise the hardness, which can extend the service life of the gear. The improved gear is shown in Figure 13. The surface roughness of the guiderail affects the machining accuracy of the parts. To improve the machining accuracy, grinding technology is applied to repair the surface, which can reduce the surface roughness value of the guiderail. Because the CA6136 machine tool is an ordinary lathe, the processing efficiency is low, and it is not suitable for mass production. To the lathe are added a CNC system module, a frequency converter, drivers, a CNC tool post, and a servo motor on the screw rod to realize automatic feed of the lathe. The upgraded lathe is shown in Figure 13. Through the CNC upgrade of the lathe, automatic processing is realized, and the machining accuracy and efficiency are improved.

It can be seen from the remanufactured lathe in Figure 13 that the remanufactured lathe is able to satisfy the CD and IRD. Compared with the literature in [3], this design method can meet not only the repair remanufacturing but also the upgrade remanufacturing, which has wider applicability and can better meet the customer demand. Compared with the literature in [4], this method can more intelligently generate design solutions that meet the requirements quickly. Overall, the RSD method can intelligently analyze the remanufacturing demand and rapidly obtain the remanufacturing scheme based on the IRD and upgrade demand, which contributes to the smooth implementation of large-scale remanufacturing of used products.

6. Conclusions and Future Work

This study proposes an MC-based RSD method for used products that include “remanufacturing demand analysis” and “remanufacturing scheme generation”. This design method is verified by the RSD of a used lathe. In this case, KE and the SD are used to collect customers’ remanufacturing demands for used lathes, K-means is applied to analyze the remanufacturing demand data and determine the main remanufacturing targets of the used lathe, and RBR is used to formulate the remanufacturing type for selecting a suitable RSD method. Moreover, RE and the TCPPSL are used to restore the failure features of the guiderail, and CBR is used to retrieve the cases most similar to the lathe upgrade remanufacturing targets.

To conclude, the present work contributes to the rapidly remanufacturing scheme generation for satisfying the mass customization products remanufacturing, intelligently analyzing the CD and IRD and classifying the remanufacturing types for accurately selecting RSD methods. Moreover, RBR is used to select the remanufacturing scheme for matching different remanufacturing types, which helps the accurate remanufacturing of the used products. The feasibility of the proposed approach has been verified in a used lathe that contains a significant surplus value. Compared with the previous RSD method, this study is to determine the remanufacturing scheme from the perspective of mass customization of used products, taking into account the customer demand and the state of the used products, so that the RSD can be more comprehensive and accurate, meanwhile, three remanufacturing scheme generation methods are proposed for designers to quickly develop a remanufacturing scheme that meets remanufacturing demand. Overall, this research provides a new theoretical basis for solving mass customized-based RSD for used products and promotes the smooth implementation of remanufacturing production.

In future studies, we will focus on the following directions: (1) As the diversity of customer demand and used product status makes it difficult to determine the type of remanufacturing, we will apply ontology technology to establish the remanufacturing type rule base, which can be used to select the RSD method more accurately. (2) Since the traditional design process cannot be displayed in real time, an RSD system interface will be developed to visualize the design process.