## 4 Case Study  
To demonstrate the usefulness of the proposed design method, a case study with a coffee maker was performed. The specification of the coffee maker is described in Table 2. In the first step, the function flows of the coffee maker were described to understand functional relationship between parts for identifying modules as shown in Figure 2. Then, DSM was developed to reflect the relationships between parts in the product as shown in Table 3. In the second step, each part design and interface design between parts in the product were analyzed by using Equations (2) and (3), respectively. Based on the analyzed values, SCCI is calculated by using Equation (1) as shown in Table 4. Each value of elements in the adjacency matrix was calculated by the sum of the SCCI values of two parts based on Equations (4) and (5), so that the adjacency matrix in Table 5 is determined finally. For example, a value of the element between bottom cover (1) and bottom casing (17) was 0.020 and it was calculated by the sum of SCCI value of the bottom cover, 0.010, and SCCI value of the bottom casing, 0.010. In the third step, MCL was applied to determine modules for product recovery, which is a design boundary for part consolidation for AM as well, by using the adjacency matrix. Since MCL is an unsupervised learning algorithm, the number of modules is determined randomly. In this case study, the number of modules converges to 7 as shown in Table 6. \begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Part Name** & **Material Type** & **Coffee Maker** \\ \hline
1 & Bottom cover & PP & \\
2 & Silicon ring & Silicon & \\
3 & Hot plate & Al & \\
4 & Casing for heater & PP & \\
5 & Heater & Al & \\
6 & Power cord & Copper & \\
7 & Water tube set & PP & \\
8 & Silicon tube & Silicon & \\
9 & Water reservoir & PP & \\
10 & Steam sprout & PP & \\
11 & Filter basket & PP & \\
12 & Filter frame & PP & \\
13 & Filter net & PP & 4\(\sim\)6 cups/0.6 L \\
14 & Filter handle & PP & Brewing time \(<\)10 min \\
15 & Lid of coffee maker & PP & \\
16 & Decanter & Glass & \\
17 & Bottom casing & PP & \\ \hline \end{tabular}
\end{table} 
Table 2: Specification of the coffee maker. In order to improve design feasibility of modules when adopting AM, manufacturing constraints of AM should be considered. Accordingly, total size of the module should be less than build chamber size of selected AM process and material types of parts in the module are identical except for using multi-material AM process. Furthermore, design rules for AM should be considered to improve manufacturability of product design. The design rules are mostly related to minimum thickness and overhang features that require support structure [45], which are derived from a combination of material and AM processes [46]. Therefore, designers should understand these various design rules. In this study, we used the material type for assessing design feasibility of modules because the material type was critical when parts in a module were consolidated as a single part by sharing the same additive manufacturing processes. Accordingly, parts in modules 5 and 6 as shown in Figure 4 can be consolidated by using AM, which is 9' and 11' in Table 6. Accordingly, designers can consolidate parts in the modules 5 and 6 as a single part by using AM. In the fourth step, the product disassembly complexity was applied to understand difficulty of disassembly and compare the difficulty of disassembly between a product with conventional modules and a product with consolidated parts in the modules 5 and 6. As a result, the product with consolidated