A novel fuzzy algorithm for assembly precision management - Liu, Sheng; Yu, Haidong (Applied mathematical modelling, 2024)
•Proposed fuzzy small displacement torsor model for tolerance classification representation.•Established an assembly reliability index for evaluating assembly precision.•Developed constrained transformation method for efficient and high-accuracy calculation.•Proposed a novel fuzzy algorithm for assembly precision management. The assembly precision of a specified product is subject to epistemic uncertainty due to manufacturing and measurement errors. The efficient and accurate assembly precision management is essential for realizing smart production lines, which rely on the robust assembly precision analysis model and calculation method. Parametric models are extensively used for assembly precision analysis in industrial products, typically integrating worst-case and statistical methods for calculations. Nevertheless, the combined application of these methods presents inherent limitations. Therefore, a novel fuzzy algorithm for assembly precision management is proposed in this paper by using fuzzy sets to quantify epistemic uncertainty in assembly precision, which integrates the proposed fuzzy-based assembly precision analysis model and fuzzy-based calculation method. The proposed fuzzy small displacement torsor model is a fuzzy-based model for deviation representation and tolerance classification using decomposed fuzzy numbers, where an assembly reliability index is always established for the hierarchical evaluation and management of assembly precision. Subsequently, a comprehensive assembly precision analysis model is developed for precision prediction and contribution quantification by integrating the Jacobian model for deviation propagation. A new constrained transformation method is proposed as a fuzzy-based calculation method, offering efficient and highly accurate assembly precision computation. It accounts for torsor parameter constraints to ensure greater prediction accuracy than the worst-case method and improves computational efficiency compared to statistical methods. An assembly case of the centering pin mechanism is applied to verify the superiority of the proposed novel fuzzy algorithm compared with the Jacobian-Torsor model with worst-case or statistical methods.