On Sharing Part Dimensions Information and Its Impact on Design Tolerances In Fixed‐Bin Selective Assembly

Abstract

F ixed-bin selective assembly (FBSA) is a method for producing high-tolerance specification assembly from lower precision components. This study investigates the design tolerance implications of an external supplier sharing dimensional information about shipped parts to be used for FBSA. An approach for reducing surplus components in FBSA is to predictively adjust the assembler's manufacturing process so that components produced internally better match those of incoming parts. However, it is unclear how the assembler's use of timely dimensions information-that is fully shared or is shared for a limited period, about the mean, variance, or both-of an externally sourced mating part would influence procedures for setting tolerances in an FBSA context. We develop and evaluate a Bayesian prediction-based model with estimated parameters from a US assembler of bearings. Our results indicate that adjustments made using predictions from solely historical data produced comparable assembly efficiency to those made with shared information about only the dimensional variance of incoming parts. Prediction-based adjustments, when only information about the dimensional mean was shared, yielded comparable matchable degrees to that when the mean and variance were both known. Furthermore, contrary to convention, looser tolerances were required to increase selective assembly efficiency. The shared information had a larger effect on the matchable degree than the modification of design tolerances in the absence of such information sharing. The insights have implications for coordinated component design and quality control.

A novel hybrid assembly method to reduce operational costs of selective assembly

“…Another approach to the problem consists of adapting the machine parameters in order to maximize the matching grade. This has been studied either in a binning context [28,30,[64][65][66][67][68][69][70][71][72], in a direct selective assembly context [73,74] Static Binning Determination of bin boundaries Equal-width [5,6,10] Equal-area [710] Statistical considerations [1117] Algorithms Deterministic algorithms [1822] Meta-heuristics [23] Combination of bins [2445] Machine parameters adaptation [28,30,6469,7173,76,77] Cost model development [82,83] Direct Meta-heuristics [4859] Graph problem [6063] Enumeration [46] Dynamic programming [47] Dynamic Binning [49,78,79] Machine parameters adaptation [70,75] Direct [60,80,81] Machine parameters adaptation [7375] Figure 1: Literature overview or in a combination of both [75]. Other authors have been investigating the correlation between having knowledge of production parameters and optimal binning strategy when one of the components is externally supplied…”