Selective Assembly in Manufacturing: Statistical Issues and Optimal Binning Strategies

Optimization of selective assembly and adaptive manufacturing by means of cyber-physical system based matching

“…of crankshafts, pistons or electric drive rotors) [2][3][4], the requirements to the product quality in more and more cases approach the technological limits of the potential manufacturing processes. In particular, for assemblies with very small tolerances, the manufacturing processes of the components are either not capable of guaranteeing the required precision or only at very high cost.…”

Applying the Concept of Selective Assembly to Modular Construction to Mitigate Impacts of Component Variability

“…Two common ways to partition the dimensional attributes of bins are (1) equal dimensional width or (2) equal probability. Equal width partitioning divides the total interface variability equally between bins, while equal probability partitioning ensures that each bin has equal populations of components [5]. Matching criteria defines how components are matched between bins.…”

“…The basic premise behind this technique is to measure and organize parts which are to be mated together into classes or bins of dimensional compatibility. Selective assembly is traditionally used to achieve tight clearance tolerances between mating parts that are manufactured using imprecise techniques; leading to high quality assembly using inexpensive techniques [5][6][7].…”

Comparison of model selection methods for the estimation of principal points for a multivariate binary distribution

“…Recently, there have been a number of theoretical and application studies dealing with "principal points" (Flury, 1990) that optimally represent a given distribution in terms of mean squared distance. In fact, principal points are applied to various problems, including product design and quality control problems, such as finding an optimal set of designs for masks (Flury, 1993), selective assembly for two parts in the manufacturing field (Mease et al, 2004;Mease and Nair, 2006;Matsuura, 2011), functional data analysis (Tarpey and Kinateder, 2003;Shimizu and Mizuta, 2007;Shimizu and Mizuta, 2008), and classification analysis of the placebo effect (Tarpey, et al, 2010). The properties of principal points have been widely studied in the literature (e.g., Tarpey and Flury, 1995;Zoppè, 1995;Yamamoto and Shinozaki, 2000;Gu and Mathew, 2001;Kurata, 2008;Bali and Boente, 2009;Matsuura and Kurata, 2011;Matsuura and Kurata, 2014), and the parametric estimation of principal points of some continuous distributions has also been discussed (e.g., Flury, 1993;Tarpey, 1997;Stampfer and Stadlober, 2002;Tarpey, 2007).…”