K-Means Module Division Method of FDM3D Printer-Based Function–Behavior–Structure Mapping

Product performance, function, cost, and the level of module generalization are all significantly influenced by product modular design, but different goods require division indicators techniques. The purpose this study is to provide a set appropriate techniques for FDM 3D printers. This research offers an ecologically friendly index uses clustering as principle in accordance with current industrial development trend fundamental requirements printer consumers market. K-means algorithm used use Jaccard similarity coefficient metric DSM process realize after studying function–behavior–structure mapping model printer. Additionally, elbow method–cluster error variance average contour evaluation systems were built, respectively, order verify viability method obtain best results. By analyzing these two systems, it was discovered that when divided into three modules, in-cluster diagram obviously had inflection point, profile other approaches need be adjusted their respective can theoretical foundation point reference.