# Density-Based Prioritization Algorithm for Minimizing Surplus Parts in Selective Assembly
###### Abstract  
Selective assembly is a manufacturing method that matches and assembles pairs of parts in a manner that offsets the machining errors of these parts. In the production of products requiring high precision and efficient mass production, flow production and search-based selective assembly must be combined for market competitiveness; however, this method increases computational costs and generates many surplus parts. Therefore, research should aim to minimize surplus parts in search-based selective assembly at a low computational cost to suit flow production systems. In this paper, we propose the density-based prioritization (DBP) algorithm, which minimizes surplus parts in the search-based selective assembly of flow production systems. In addition, a method of varying the assembly tolerance is developed and incorporated into DBP to increase its process capability. The proposed algorithm requires an assembly facility to prepare parts with as many different sizes as possible. This paper confirms that DBP reduces computational costs and surplus parts while enhancing process capability.  
prioritization; selective assembly; surplus part; flow production; ball bearing +
Footnote †: journal: Applied Sciences  
of surplus parts in multi-matching selective assembly under a non-normal dimensional distribution of parts. [PERSON] and [PERSON] [9; 10] proposed a selective assembly model that used a genetic algorithm to minimize surplus parts within a strict assembly tolerance.  
These selective assembly approaches can be divided into group- and search-based methods. Group-based methods classify parts to be assembled with each other into groups according to their dimensions and organize groups so that machining errors are offset, whereas search-based methods identify and assemble the best combinations of parts through calculation. Group-based methods have simple structures and fast processing speeds, enabling efficient mass production, but when the required precision is high, the facility should be larger so that groups can be further subdivided. Search-based methods enable more precise assembly than group-based methods. Search-based methods require calculation every time a new component is introduced, so studies on these approaches focus on batch production systems rather than flow production systems. However, as the precision parts in automobiles and industrial machineries require very high precision and efficient mass production, flow production systems and search-based selective assembly must be combined for market competitiveness. Therefore, research should focus on minimizing surplus parts in search-based selective assembly at a low computational cost to suit flow production systems.  
In search-based selective assembly, a dimensional concentration phenomenon occurs in which the supply and assembly frequencies of parts according to size become unbalanced. As this phenomenon intensifies, the diversity of the parts decreases, thus reducing the probability that the combinations satisfy the assembly tolerance. In this paper, we propose the density-based prioritization (DBP) algorithm to minimize surplus parts in the search-based selective assembly of flow production systems. DBP regards the similarity of a part with the other parts as density on the dimensional coordinate and gives high selection priority to parts with high density to balance the supply and assembly frequencies. We examine the selective assembly procedure for producing precision ball bearings and analyze the factors that cause surplus parts to occur. We then evaluate selective assembly with DBP and compare it with traditional selective assembly.