A Neural Network Based Approach for Tolerance Analysis

公差分析係探討產品的零件規格變異上限，當組合成產品，零件規格變異的累積效應不致影響產品的品質。公差分析之前的研究關注在大量生產的環境下，產品的零組件尺寸分配是已知的，然而在彈性製造生產的環境下，先前的研究假設在大量生產情境下，零組件尺寸的統計分配用來進行公差分析是不適宜的。本研究探討在量少多樣式的模組化彈性組裝生產環境下，零組件一般都具有可互換性特性時的公差分析。本研究運用類神經網路預測組裝後產品的公差而不須任何事先假設零組件尺寸的統計分配。研究結果顯示當零件公差是均勻分配、韋柏分配或混合分配時，本研究提出運用類神經網路來預測產品公差分析的誤差優於傳統公差分析的誤差。

關鍵字

公差分析；類神經網路

並列摘要

Tolerance analysis consists of determining upper bounds on the variations in a product's part dimensions such that after the parts are assembled the accumulated effect of these variations will not affect the product quality. In the past, tolerance analysis focused on the design stage in a mass production environment where the distribution of parts and components was necessarily known in advance. Thus, the assumptions concerning statistical distributions found in previous research on tolerance analysis are not applicable in flexible manufacturing and assembly environments. This research focuses on tolerance analysis for low volume, large variety production, in which parts with certain characteristics in common are typically interchangeable, as in the modular flexible assembly environment. We apply neural network techniques to predict assembly tolerances without a priori assumptions concerning the statistical distributions of parts and components. Experiments have shown that the proposed neural network based approach outperforms the traditional tolerance analysis model when the distributions of part tolerances are uniform, Weibull or mixed.

並列關鍵字

tolerance analysis ； neural network

參考文獻

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