Klongboonjit, Sakon

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Machine Component Clustering with Connection Correlation Method
(2021-06-30)
Kongsin, Tanongsak
;
Klongboonjit, Sakon
This study aims to introduce a new method for modular design, which is called Connection Correlation Method (CCM), to clustering machine parts with inter-part connection conditions to classify independent modules for the Industrial Printer Powertrain set with 105 components. With the CCM technique, module members would be appropriately assigned for each module so that machine modules are independent of their function. The CCM technique should first calculate the distance coefficient matrix with surface contact conditions of bonded, rough, separation, and frictionless for machine components. Finally, this distance coefficient matrix is used to generate the machine dendrogram with the dependency coefficient between 2.4945 and 3.5593. At the dependency coefficient of 2.7913, the 105 components of the Industrial Printer Powertrain set are clustered into 7 modules: Module 1 with 75 interconnection surfaces and 8 parts, Module 2 with 136 interconnection surfaces and 14 parts, Module 3 with 176 interconnection surfaces and 17 parts, Module 4 with 61 interconnection surfaces and 8 parts, Module 5 with 200 interconnection surfaces and 21 parts, Module 6 with 209 interconnection surfaces and 19 parts, and Module 7 with 18 interconnection surfaces and 18 parts. The results show that the CCM technique can apply to design a modular machine like DSM technique, and multitudinous connectivity factors can also be analysed together with general factors.
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Machine components clustering with DSM and repeating method: Case study of a soil mixing machine
(2018-08-14)
Kongsin, Tanongsak
;
Klongboonjit, Sakon
In this study, components of the machine are analyzed to group all components into modular groups with a case study of a soil mixing machine. The study begins by creating a design structure matrix of all components. Next, the design structure matrix is transferred into a distance matrix of all components with Jaccard method. After that, the equation of complete linkage must be applied to change the distance matrix to a tree dendrogram for showing the relationship of machine components and dependent coefficient. With this tree dendrogram, six clusters are arranged:- the 1st cluster has 8 modules at the lowest dependent coefficient, the 2nd cluster has 7 modules, the 3rd cluster has 6 modules, the 4th cluster has 5 modules, the 5th cluster has 4 modules, and the 6th cluster has 2 modules at the highest dependent coefficient. Finally, the 1st cluster with 8 modules is considered to be the most proper cluster for this soil mixing machine by applying the repeating method to analyze all six clusters.
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Machine Component Clustering with Mixing Technique of DSM, Jaccard Distance Coefficient and k-Means Algorithm
(2020-04-01)
Kongsin, Tanongsak
;
Klongboonjit, Sakon
This study aims to introduce a design method of Jaccard Distance Coefficient with k-Means algorithm for machine component clustering into independent modules so that a machine can be easily modified to achieve its requirement functions. In this study, Jaccard Distance Coefficient is firstly applied to generate relation matrices. After that, six results of distance coefficients and their clusters (2.02 with 2 clusters, 1.95 with 3 clusters, 1.89 with 4 clusters, 1.83 with 5 clusters, 1.75 with 6 clusters and 1.66 with 9 clusters) are calculated corresponding to relation matrices from the first step. Nextly, k-Means algorithm is used to take care of these six results for analyzing the most proper level coefficient. The result shows that the modules of 3 clusters with distance coefficient of 1.95 is the best outcome at the 0.7074 natural value.

Klongboonjit, Sakon

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