INTRODUCTION
With the advent of intelligent manufacturing, the business model with “personalized customization” as the theme and the manufacturing model with “flexible manufacturing” as the core have entered the traditional manufacturing industry (Yang and Xiong 2022). The traditional manufacturing industry should adapt to this change with rapid transformation and upgrading to digital design and manufacturing (Wiedenbeck and Parsons 2010; Tang et al. 2018). Digital design includes modeling with full consideration of the entire product life cycle and the customer needs for customized products via the computer, network, computer-aided design (CAD), and computer-aided engineering (CAE) software, printer, plotter, and other tools (Liu et al. 2019). The process consists of relying on digital compression, coding, demodulation, modulation, and other processing technologies. Then, the product design is completed after certain calculations, the geometric model is constructed, and there is repeated data debugging (Yang et al. 2019). Research on the digital design of household products at home and abroad has focused on the standardized and serial design of products (Ren et al. 2022a,b). With the rapid development of customized homes and the application of various kinds of control software in household enterprises, digital design has developed in the direction of integrated technology of design, manufacturing, and management. However, there has been little systematic research on digital design technology systems for intelligent manufacturing.
Development History of Digital Design
In China, the development of household digital design and manufacturing technologies has experienced four major stages (Zhang et al. 2021; Xiong and Yue 2022; Wuet al. 2022).
The first stage
Based on the research and development of digital furniture design software, the theoretical research of software systems has achieved certain results. In this study, a computer-aided fashion design (CAFD) system for furniture parts and structures, a furniture computer aided design (FCAD) system for furniture structure design, and a CAD system for interior design and furniture modeling design were developed independently. However, due to the lack of software maintenance and technical up-grading, the scope of promotion and application remains limited.
The second stage
The introduction of design software and interface communication is the foundation for the digital transformation of China’s furniture industry. AutoCAD software has been recognized by furniture enterprises because it is easy to learn and use, and it has a secondary development interface. Some kinds of cabinet design software such as KCD Cabinet Designer, Cabinet Pro, and Cabinet Vision Solid have been promoted. Due to the incompatible format, no localization, and high price, they have been only applied by a few foreign-funded or OEM companies.
The third stage
The development of digital design mainly focuses on the custom development of digital furniture design software at home and abroad. Digital design and transformation of manufacturing has become the core competitiveness for enterprises to survive and develop. Domestic software options including SAP, Oracle, Epicor, WCC, 2020, IMOS, Top-solid, Dingjie, and Jindie have become the main representatives of digital design and control systems in the industry. Special software has been developed for furniture enterprises in consideration of domestic characteristics, such as Dongguan Shufu Software and Guangzhou Liansi Software.
The fourth stage
Online displays of digital furniture designs have attracted wide attention. Considering the functional characteristics during the process of customized furniture design, domestic software enterprises have developed functional software for household digital design concerning functions such as order disassembly and rendering. Enterprises represented by Guangzhou Rhinoceros R5, Yuanfang, three-dimensional (3D) home, and Kujiale have formed a situation of domestic digital design function software. Compared with internationalized mature software and operation standards (standardized import type), domestic software, which is more suitable for the actual needs of China’s furniture enterprises, has quickly opened the market of China’s digital furniture design.
Application of Multi-attribute Clustering Algorithm
Multiple attribute decision making (MADM) is a method that selects the optimal scheme or ranking in consideration of multiple attributes based on the existing decision information (Zolfani et al. 2016). Clustering is a process of forming multiple categories from multiple physical or abstract sets according to the principle of similarity. There are various kinds of cluster analyses, mainly partitioning methods, hierarchical methods, density-based methods, grid-based methods, and model-based methods. The traditional clustering methods, which are classified by distance, could not achieve a good clustering effect for samples with multiple attributes (D’urso and Massari 2019). Therefore, the MADM model is often applied to be combined with the clustering algorithm. Multi-attribute overlapping clustering technology (MOCT) is a clustering method that includes multiple attributes of samples into the analysis. This clustering method could integrate and analyze cardinal category, ordinal category, as well as qualitative and other indexes, and interpret the clustering results and significance from numerous aspects (Liu and Shi 2017). Therefore, MOCT has been employed for analyses and evaluation, user preference, evaluation report, geographic cluster analysis and navigation, and medical analysis, among others
There are two major methods to deal with mixed data containing multiple attributes, i.e., data preprocessing, which presents all variables of the same type through the common methods of binary variables and virtual coding, and dissimilarity measure, which could handle the mixed data possibly by assigning a weighting system for addressing the relevance of each attribute type (D’urso and Massari 2019). Liu and Shi (2017) put forward MOCT based on the application of the clustering analysis and compared it with the traditional methods in terms of the evaluation and analysis of urban comprehensive competitiveness in China, and the results indicated its superiority.
Other studies have combined MADM with the K-mean algorithm. Ma (2017) determined the importance of ranking of attributes based on users’ preferences to select the cluster number K, which provides a new system framework for preference modeling. Rijati et al. (2018) applied the multi-attribute evaluation technology to quantify the factors that affect student entrepreneurship and developed weight standards, which reduces the sum of squared errors and the number of iterations com-pared with the conventional K-means algorithm. Liu et al. (2021a) considered the quality of service (QOS) objectives and customer preference attributes, quantified attribute preference through the collaborative filtering (CF) algorithm, optimized multi-attribute manufacturing service composition (MSC) via the third-generation non-dominated sorting genetic algorithm (PONSGA-IID), and finally recommended the most appropriate solution for customers through attribute preference sorting. Xiao et al. (2021) proposed a mechanical product module division method based on a spectral clustering algorithm. The correlation of parts was standardized with real numbers between zero and one and then weighted to optimize clustering. The results indicated that this method could effectively reduce the complexity of mechanical product design.
Pang et al. (2019) proposed a MapReduce-based subspace clustering algorithm (PUMA) to tackle the problem of high-latitude data processing. First, the co-occurrence probability of attributes in different latitudes is calculated to obtain the weight value. Next, the sub-clustering of each attribute subspace was calculated. Finally, the hierarchical clustering iteration is combined to merge the sub-clustering. This method could achieve better clustering accuracy in the expert system. Hao et al. (2010) proposed a multi-attribute fuzzy clustering blood vessel segmentation algorithm based on spatial continuity, which considered both intensity information and geometry information. The results indicated that this method had better quality compared with a single intensity information segmentation method.
D’urso and Massari (2019) proposed a fuzzy clustering model of mixed feature data, which fully considered different types of variables and attributes and combined different measures of each attribute through the weighting scheme to obtain the distance measurements of multiple attributes. This method has been applied to tackle the problem of the clustering of ski-lifts located in the Dolomi area and the European social survey, and the results indicated that it is very effective (D’urso and Massari 2019). Wei et al. (2020) developed an adaptive propagation traffic behavior recognition algorithm with multi-attribute trajectory characteristics, proposed multi-attribute similarity measurement of space, speed, and route, and combined it with a density-based clustering algorithm to provide better monitoring and navigation suggestions. Notably, the combination of MADM and clustering algorithm has been extensively applied.
The application of clustering algorithms to the furniture industry mainly focuses on the group processing of parts, and the selection of classification attributes is generally limited to craft. Previous studies respectively applied the ant colony clustering algorithm and fuzzy clustering algorithm to investigate the part classification of customized cabinets, which remarkably improved the processing efficiency of enterprises (Jia et al. 2016; Tao et al. 2020). Güven and Şimsir (2021) utilized fuzzy clustering and rank order clustering methods to create the part-machine matrix according to the production flow technique and grouped the parts and machines, which improved the productivity and reduced the cost. To optimize the production layout, Dianita et al.(2020) applied the rank order clustering (ROC) and Hollier method to classify the machinery and process in the production layout, successfully reducing the total travel time and distance. The classification of solid wood furniture parts is complex, and the factors that affect their production and processing include the craft, size, tongue and groove structure, and bending degree. Single attribute technology could not meet the requirements of group processing of parts design and production.
Based on the investigation of the design process and enterprises of solid wood furniture, this study put forward the steps of building a systematic standardized design platform process. Aiming at the division of the part family, which is the key node in standardized design, a classification method based on MOCT was proposed. The performance of this method was verified by the product data of solid wood cabinet doors sold in Moganshan household enterprises.
Manufacturing Status
The combination of affordable and plate cabinets with rich and diverse solid wood cabinet doors accounts for a large proportion of the diversified product line layout of cabinet furniture. However, plate cabinets only take approximately 20 d from order to package, while solid wood cabinet doors take approximately 35 d (Fig. 1). With the enterprise resource planning (ERP) system as the information transmission medium, the store designers upload drawings or models that need to be manually checked and disassembled by factory designers. The order adopts the forward arrangement method to arrange production (Li et al. 2018; Ma et al. 2020; Amaral and Peças 2021; Zhu et al. 2022a,b). After the survey on peer enterprises (HOLIKE, SLEEMON, MENTIAN, and A-Zenith), it was found that the production cycle of solid wood products was generally 30 d to 45 d and the production efficiency was quite low.
This low efficiency has two explanations. First, the product system is complex. There are various types of cabinet doors due to different modeling and functional requirements, whose parts are different in size and structure and difficult to exchange. Moreover, there are barriers between design and production. The design section mainly applies 3D software, the manufacture section mainly deals with CAD drawings, and the store section has model files only for display. When delivered to the manufacturing end, the orders are manually separated for flow production, which leads to low production efficiency.
The Basic Process of Digital Design
Home digital design refers to the application of specific 3D software tools to realize a series of digital operations such as virtual creation, modification, improvement, analysis, and display of furniture models through design data acquisition, processing, and display. The basic process is presented in Fig. 2.
Key Technologies for Building the Digital Design Platform
As the display window of digital design, the platform has key technologies including cabinet door data extraction and integration, part family division, coding technology, model base construction, as well as information interaction and connection (Kulak et al. 2012).
Cabinet door data extraction and integration
The cabinet door product data shall be obtained from the enterprise’s CAD drawing date, design side software database (e.g. 2020-Design, Topsolid, and 3D +) (Hui et al. 2020), product display manual, product technical manual, and other paper and digital materials. The basic design data of cabinet door parts including shape, dimension parameters, tenon and groove parameters, and frame mode are collected and integrated.
Taking the 93 door types of Moganshan company as examples, the existing cabinet door data of the enterprise are cumbersome and complex. Under the same model, the sizes and structures of door and drawer panels, molding core board doors, and flat core board doors are different, which makes it difficult for the parts to be replaced and makes production more difficult. Therefore, the parts under the same model should be common to the largest extent. Under the principle of simplifying the same functional parts, the structure and size of parts are analyzed and classified. The structure directly affects the thickness of parts, so the integration order is structure and size.
Tongue and groove structure
The tongue and groove structure is reflected in the connection method between the frame board and the core board or the wood line. It is necessary to analyze and integrate the structure of the frame board, core board, and wood line. The frame board structure is related to the frame form and the wood line type. The frame form could be divided into core board embedded frame and whole panel, while the wood line type could be classified into pressing line and buckle line. Therefore, the tongue and groove style could be divided into core board embedded frame-no wood line (a), core board embedded frame-pressing line (b), and core board embedded frame-buckle line (c) (Fig. 3).
The core board is divided into “shape” and “without shape” whose thicknesses are different, but both rely on the U-shaped edge of 5 mm to lap or insert into the frame board, so their structure could be unified (Figs. 3d, 3e, and 3f). The wood line is classified into buckle and pressing. The buckle line could unify its lap depth, i.e.the difference value from the front of the core board to that of the frame board, which is 12 mm after integration. The total depth of the core board and the pressing line is 15 mm and the thickness of the core board is 5 mm, so the lap depth of the pressing line shall not exceed 10 mm. The line structure is presented in Fig. 4.
Enlarge this image.Fig. 3. The a, b, c) frame board tongue and groove structure and d, e, f) frame board and core board assembly method
Enlarge this image.Fig. 4. Wood line structure of the a, b, c, d, e) buckle line and f, g, h) pressing line
Size integration
The cabinet door meets the changeable needs of customers by changing the length of four frame boards (upper, lower, left, and right), the length and width of the core board, and the length of the wood line and stuck line. Therefore, it is necessary to unify the width and thickness of four frame boards, the width and thickness of the middle frame board, and the thickness of the core board to improve standardization. There are three size optimization principles. First, the value with the largest proportion of part size is taken as the optimization object. Second, parts of the same structure and function should keep the same size to the largest extent. Third, the structure and size of the drawer panel and the door panel shall be consistent as much as possible, and the door panel shall be the main integration.
Thickness
The thickness of the four frame boards and the middle frame board shall be consistent to facilitate the structural insertion, of which 22 mm accounts for the most. The thickness of the raw material is distributed within the approximate range of 24 mm to 26 mm, which could maximize the use of raw materials, so the thickness of the four frame boards is combined into this value. The thickness of the core board is the same as the structure with a molding door panel of 15 mm and a flat door panel of 5 mm.
Width
Part of the door type has a bending shape, so the upper and lower frames are different from the left and right frames. Under the same type, the left and right frames of the drawer panel and door panel are the same. The top three width dimensions are 55, 75, and 90 mm. The left and right frames widths are 75 mm, and they account for the largest proportion, so the left and right frames widths on the door panel and drawer panel are merged with this value. The 55 mm dimension is mainly distributed on the upper and lower of the drawer panel. Because the drawer panel is small, a large size of the upper or lower frame will affect the shape of the core board, so the size of upper and lower frames on the drawer panel is maintained at 55 mm. There are bending-shaped doors on the upper frame board, and the width of upper and lower frames on the drawer panel is uniformly 75 mm, while that on the door panel is 90 mm. The middle board needs to have upper and lower tongues and grooves prepared. If the size is the same as the upper and lower frames, then the visible width after slotting will be less than that of the upper and lower frames, which will affect the beautiful proportion. So the largest proportion number (90 mm) is taken as the merged target value. The door type produced by other factories shall retain its decorative frame board size, and the width of the frame board shall be 26 mm.
Part family division
The upper limit of cabinet door derivative design depends on whether the division of product parts family is fine. Based on complete door data collection, the cabinet door is disassembled into single parts. The part family classification of solid wood could be affected by various indexes, four essential indexes are extracted, i.e. size, craft, tongue, and groove structure, and bending degree, which are divided by MOCT (Duet al. 2020; Tao et al. 2020; Xiong et al. 2021), as described in detail in next section.
Coding techniques
As the basic data of enterprise production and manufacturing, the code of cabinet door parts is also the key link to realizing the tracking and retrieval of cabinet door parts (Gong et al. 2019). Following the principles of uniqueness, practicability, expansibility, and standardization, the product information code and part processing information code of cabinet door are formulated in the coding mode of “identification code + classification feature code + size code.” Among them, there are more surface material features of the cabinet door, so its material feature code is formulated separately (Fig. 5).
Enlarge this image.Fig. 5. The a) cabinet door information code, b) cabinet door material feature code, and c) part processing information code
The cabinet door information code mainly highlights the overall attribute and size, including the main shape and size information of the cabinet door product. The cabinet door material feature code is applied to be combined with the cabinet door information code, including the material feature attribute, assembly way, and the total number of product parts. The part processing information code is mainly utilized for the production end, e.g. part identification, serial number, structural position, process, carving shape, bending shape, and size information. The cabinet door size and part size often change, so intuitive embodiment on the code could increase information processing efficiency.
Model base construction
In the original database, the cabinet door takes the whole door as the smallest unit during the process of design and production, which only changes its length and width but could not achieve automatic disassembly and output BOM table with the disassembly section. Therefore, it is necessary to build a model base with parts as the minimum unit, whose construction includes two major aspects, such as the formulation of size constraint relation and the parametric module making through programming technology.
For the analysis and formulation of the size constraint relation, the essential parameter assignment of parts includes the length and width range of cabinet doors, the connection gap between door leaves, the width and thickness of parts, and the structure and size of tongue and groove. The size constraint relationship among parts is presented in Table 3.
Various standard component models could be established via parametric design modeling software (e.g.UG NX and SOLIDWORKS) (Zhou and Zang 2020). The steps include formulating the part structure size, applying the macro recording to save its code file, and utilizing programming software to set its variable size as modifiable and obtainable, thus realizing the position relation of the assembly structure of each part of the cabinet door through constraint block. The key lies in the disassembly and reorganization of various parts and fast positioning through connected points or surfaces (Fig. 6).
Information interaction and connection
Design information needs to establish information loops in both directions with ERP software, production data management (PDM), and manufacturing execution system (MES) to connect the whole product lifecycle management (PLM) process of the enterprise (Gong et al. 2019). Local area network (LAN) is applied to build software sharing ports for the design section, disassembly section, and manufacturing section (Guo et al. 2018; Xiong et al. 2020; Liu et al. 2021b). Each equipment shall possess the cloud storage and processing functions, reduce the phenomenon of two-dimensional (2D) drawings and other engineering data throughout the whole situation, ensure smooth data flow, and reduce the cost of information interaction. In reality, this method could reduce the error rate and labor cost. The ERP system should be applied to track and trace the global data information efficiently, realize the transparency of internal data, and enhance the interconnection of various departments. Applying internet of things technology, sensing technology, monitoring technology, and logistics control technology to develop mobile terminal software that realizes real-time tracking and control of products to serve the whole production and sales cycle of products (Fig. 7) (Chen 2020).
EXPERIMENTAL
The key step of digital design is the division of the part family. A part family division method based on MOCT is proposed to tackle the complex situation of group machining of solid wood parts. This method is verified by taking over 40 solid wood door panels of M company as the test object.
Method
This study applied MOCT. First, four attribute indexes of solid wood custom cabinet door part family division (size, craft, tongue, groove structure, and bending degree) were selected (Song et al. 2016; Hu and Guan 2020). Next, single index analysis and clustering were conducted. Finally, the part family was determined by overlapping synthesis according to the weight ordering of the four indexes. The research sequence of this test was weight sorted by four indexes, single index clustering, and part family division.
Index weight sorting
The attribute sorting was determined by the analytic hierarchy process (AHP) and scored by experts. On a one to five scale, one denotes the least importance and five represents the greatest importance. Ten experts were counted.
Single index clustering-size
The agglomerative hierarchical clustering method is applied to classify the parts (Lv et al. 2019). To do this, count the orders in 2020, take the size modes of all types of doors as the test data, and disassemble them into the size of each part (upper frame, lower frame, left frame, right frame, core board, wood lines, wood louvers, louver frame lines, and whole panel). The size data of parts is defined as a set of points in 3D space, and the similarity between parts is determined by calculating the distance between the points (Liu et al. 2020). The smaller the distance, the higher the similarity. The relatively close data points or categories are grouped. The length, width, and thickness of the disassembled part A as x, y, and z values in space are defined. The maximum normalization method is applied to standardize the data. The standardized x, y, and z are defined as xst, yst, and zst, as shown by Eq. 1,
(1)
The similarity between standardized part A (xA, yA, and zA) and part B (xB, yB, and zB) is measured by the square Euclidean distance. The distance between A and B is defined as dAB as shown by Eq. 2,
(2)
Data points or categories with similar distances are merged into groups. The average distance between categories is utilized to measure the distance between groups. Similar categories are merged, and the clustering results are determined in line with the needs.
Single index clustering-craft
Craft clustering mainly aims at the machining section to build the part-craft matrix (Jia et al. 2016; Wu 2019). Some parts have the same craft, and under the same craft, the part with the smallest serial number is taken as the sample for the subsequent test. The similarity coefficient matrix is constructed through the fuzzy clustering method, and the transfer closure is calculated. Combined with the actual production situation, the appropriate clustering results are selected. The first step is to build the part-craft matrix. The unclassified furniture parts are defined as An= {a1, a2…, an} and the production craft of cabinet doors is defined as Bn= {b1, b2…, bn}. The structure of the part-craft matrix can be calculated by Eq. 3,
The next step is to construct the part similarity coefficient matrix. The similarity coefficient between the two parts is determined by the maximum and minimum method based on the collected part-craft matrix. The similarity coefficient of part i and part jthat belong to two different parts in An is denoted by xij. Craft k belongs to production craft Bm, eik represents the value of part i on craft k, and ejk signifies the value of part j on craft k, as shown by Eq. 4,
(4)
The similarity coefficient could be comprehended as xij = number of crafts jointly owned by part i and part j (the number of crafts jointly owned by part i and part j + crafts with part i not part j + crafts with part j not the part i). Therefore, the greater the proportion of crafts owned by both parts, the greater the similarity coefficient. Given the above, the similarity matrix between parts is constructed by Eq. 5,
Next, the number of groups is determined by the threshold λ. The grouping conditions under different thresholds could be obtained by the interception of the threshold λ in the fuzzy equivalent matrix T, and then Tλ is the λ block matrix of T. After different clustering results are counted, its dynamic clustering diagram is obtained through Eq. 6, the mean intra-group similarity coefficientCp. The larger theCp value, the higher the mean intra-group similarity coefficient and the better the grouping effect. The formulas for Czand Cp are shown by Eqs. 7 and 8, respectively, (7) (8)
At a certain threshold λ, the sample is divided into g groups. There are g average similarity coefficients, i.e. Cz1, Cz2,…,Czg. The number of parts in each group after grouping is denoted by h, and H represents the part set in the group after grouping, i, j.
Single index clustering-tongue and groove structure
By analyzing the connection type of parts, the connection structure mainly includes four frame boards and core boards, four frame boards, and a wood line. Therefore, the tongue and groove structures of four frames, core board, and wood line need to be analyzed (Huang and Xu 2019; Fang et al. 2020), and the similar tongue and groove structures of parts should be integrated.
Single index clustering- Bending degree
Among the parts, only the upper frame has a bending shape. Therefore, this step mainly analyzes the parts with bending shape in the upper frame and divides them into categories according to the bending radius and the corner radian radius.
Part family division
The clustering results of each attribute index are sequenced according to the classified categorical character strings, and all indexes are included in multi-attribute overlapping clustering. The parts with the same character strings are classified into one family, and the total number of the different character strings is the number of families for part division.
Samples
Forty types of customized solid wood cabinet doors (26 types of log cabinet doors and 14 types of composite solid wood doors produced by the Shenghua Yunfeng Moganshan Whole Wood Factory in Huzhou, China), are shown in Fig. 9. Flat core board, composite core board, composite flat door, and log flat plate door are the samples. The door shape is also presented in Fig. 9. After disassembly, there were 458 parts in total. After plastic stuck lines and hardware decorative lines were removed, there were 314 solid wood parts including 205 solid wood parts with different shapes and structures, which were taken as the test samples.
RESULTS AND DISCUSSION
Index Weight Sorting
The scores of 10 professional experts are counted and presented in Table 4. Firstly, the average value of each analysis item is calculated, and the judgment matrix is obtained by dividing the average value, which is constructed by SPSSAU. A higher average means a higher importance. The judgment matrix is shown in Table 5. According to the table, the average size is 5, the average value of mortise and groove structure is 4.2, the average value of process is 4.4, and the average value of curvature is 2.4.
Secondly, although the AHP method is suitable for expert scoring weight calculation, in order to avoid logical errors between attributes, a consistency check is required. The fourth-order judgment matrix is constructed, and the sum-product method is used to calculate and study, as shown in Table 6. The eigenvectors obtained from the analysis are 1.25, 1.05, 1.1, and 0.6. Combined with the eigenvectors, the maximum eigenvalue can be calculated as 4, and then the CI value is calculated by using the maximum eigenvalue. The CI value is used for the following consistency test.
The consistency test needs to use two index values, CI and RI. The CI value has been calculated to be 0.00, and the RI value can be found in Table 7 to be 0.89. CR=CI/RI. Generally, the smaller the CR value, the better the consistency of the judgment matrix. When the CR value is less than 0.1, the judgment matrix satisfies the consistency test. The CR value is 0, which means that the judgment matrix of this study satisfies the consistency test. Therefore, the experts scored the four attributes in order of weight: size, craft, tongue and groove structure, and bending degree.
Single Index Clustering-Size
The length, width, and thickness sizes of 205 parts are presented in Table 8. SPSS software (IBM, Armonk, NY, USA) was utilized for standardization and the distance between the parts was measured. After the hierarchical relationship was determined according to the similarity, the initial cluster number was eight. Combined with the actual functional types, the final cluster number was four and the shape of each classified part was described. The family tree diagram was too long, so it is omitted herein.
The sizes are divided into four categories, the distribution of parts in each of which is presented in Table 9. Category A includes all four frames, some wood lines, and a whole panel, which are all wide and long materials that are largely different in length, width, and thickness. Category B includes some wood lines, wood louvers, and louver frame lines, which are slender materials with similar widths and thicknesses and remarkably different lengths and widths. Category C includes all core boards, some whole panels, composite panel door, and log flat panel door, which are plate parts with similar widths and lengths and significantly different widths and thicknesses. Category D is the grid line.
Single Index Clustering-Craft
There are 33 statistical craft routes, as indicated in Table 10. C-language programming was utilized for the auxiliary calculation. The similarity coefficient matrix of 33 × 33 was constructed and the transitive closure matrix was calculated. The calculation of R32=R64 was conducted five times to obtain the fuzzy equivalent matrix. The matrix was intercepted using different thresholds of λ, and a total of 15 clustering results were obtained. The number of groups and mean intra-group similarity coefficient Cp under different thresholds are presented in Fig. 10.
Enlarge this image.Fig. 10. The a) tree spectrogram of grouping under threshold and the b) number of groups under different thresholds and the average similarity coefficient within the groups
As indicated in Fig. 10b, the number of groups and the mean intra-group similarity coefficient gradually increased as the threshold rose. Combined with the actual production of M company, there is only one cabinet door production line, in which there will be no more than two pieces of equipment with the same function on the production line. Hence, the scheme with a small number of groups should be selected on the node with the fastest growth rate of mean intra-group similarity coefficient in the group. Therefore, in the craft grouping, the data node should be selected when λ is equal to 0.71 and there are five groups. The grouping results are presented in Table 11.
The process is divided into five categories. Category A contains all four frames and follows the conventional craft route. Category B includes some wood lines and adopts the nail line route. Category C refers to some wood lines and grid line, fine cutting after the nail line route is adopted. Category D refers to the whole panel CYS051 and adopts the metal line route. Category E includes all core panels, louver strips, wood louvers, louver frame lines, and the flat whole panel and the milling route is adopted.
Single Index Clustering- Tongue and Groove Structure
After the structure of the part is integrated with the same function of 40 door types, it is found that the tongue and groove structure mainly refers to the connection mode between the four frames and the core board, stuck line, and wood line. The four frames are the key modeling structure material in the cabinet door whose tongue and groove structure is determined by whether there are wood lines and line styles. Wood lines are divided into two types, namely the F buckle line and the G pressing line. Therefore, the four frames tenon structure connected with it could be divided into three types, i.e. a wireless line structure (A), a buckle line structure (B), and a pressing line structure (C). In addition, there is the whole panel structure (D) and the core board structure (E), as shown in Fig. 11. Although there are two thicknesses, i.e. the flat core board of 5 mm and the molding core board of 15 mm, the joint thickness connected with the frames is 5 mm, so its tongue and groove structure could be unified into one. The structures of special parts of grille door type and shutter door type are inconsistent, which are classified as category H. The clustering results are presented in Table 12.
Single Index Clustering- Bending Degree
There is a bending shape in the upper frame of some door types, which only has one side of the upper frame and needs special processing by the tenoning machine. After the four frames of some door types are assembled, there is still a corner radian shape. Therefore, the bending radius and corner radian radius of the bending upper frame are counted. When the shape remains roughly the same, the similar sizes are combined. Because there is only one corner radian radius in this paper, it is retained. The relative details are shown in Table 13.
According to the bending radius, the parts with bending shape are classified into three categories, i.e. A, B, and C, and the straight materials are uniformly classified into category D, as presented in Table 14.
Part Family Division
By overlapping the synthesis sorting of category codes in Tables 7, 9, 10, and 12 and removing empty sets, 17-part families were obtained in this test, as presented in Table 15. The clustering results show that MOCT is suitable for the division of solid wood door parts family because it can achieve clear clustering results and interpret the meaning of part family from various aspects, e.g. AAAA (wide and long material, taking conventional four frames craft route, frame structure unconnected with the wood line, bending radius of 570 mm). The clustering results are sorted according to the importance of attributes and any attribute or multi-attribute overlapping clustering could be selected to meet the needs of different production conditions such as slotting (four side molder). The clustering results of the tongue and groove structure attributes and the corresponding tongue and groove cutter could be selected. If the bending degree only affects the processing mode of the tenoning machine, such an index could be removed and the number of family changes from 17 to 13 when the parts are processed by this process.
CONCLUSIONS
1. To improve the difficult situation of solid wood cabinet door design and disassembly, a digital design platform is proposed that includes cabinet door data extraction and integration, part family division, coding technology, model base construction, and information interaction and connection. Taking M Company’s 93 cabinet doors as examples, this paper expounded on the key technical principles and practices to lay a foundation for the establishment of a digital design platform for intelligent manufacturing.
2. Part family division method is designed based on MOCT. Select four main attributes that affect the production division, i.e. size, craft, tongue, and groove structure, and bending degree to carry on the multiple attribute clustering. Over 40 type cabinet doors of M Company were selected, and 314 solid wood parts were divided into 17-part families. Given the clustering complexity of solid wood parts, this method achieved a good clustering effect and adjustment that could be extensively applied to the group processing of solid wood parts.
3. The number of selected attribute indexes and that of single attribute categories directly affect the division of part families. The more attributes there are, the more detailed the clustering will be. Therefore, it is necessary to control the number of part families reasonably when organizing into group processing. The clustering attributes of solid wood furniture are over the four indexes described in the present study. For different solid wood furniture products, they should be carefully selected according to the needs of their manufacturing sections. This will be of reference significance to the multi-attribute clustering of solid wood furniture.
ACKNOWLEDGMENTS
The authors are grateful for the support of the Technology Innovation Alliance of Wood/Bamboo Industry (Grant No. TIAWBI202010), the Post graduate Education Reform Project of Jiangsu Province (Grant No. SJCX20_0267), and the National Key R&D Program of China (Grant No. 2018YFD0600304).
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