Abstract
Sensor deployment to robustly monitor operation parameters is the cornerstone for timely diagnosing variations that lead to product quality defects. A sensor system with optimal placement can help manufacturers improve product quality and reduce process downtime. However, current literature lacks investigation in methodologies that consider the mechanism of variation propagation, heterogeneity among sensor and variation properties, and multi-objective optimization involved in the sensor layout for variation diagnosis in an assembly process. In our approach, sensing information from fixture error and part machining error is integrated into a step-indexed state space model based on an understanding of variation propagation mechanism for the stream of assembly set variation described in a single-station multi-step assembly process. The effectiveness of such sensor system is quantified by some indexes, which are used to quantitatively characterize the diagnosability. Based on digraph theory, a quantitative fuzzy binary graph-based approach is developed to model the cause–effect relationship between part variations and sensor measurements considering the heterogeneous properties of sensor and variation, thus an improved shuffled frog-leaping algorithm is proposed to determine the sensor types, numbers, and locations to achieve full diagnosability. An example for the differential assembly illustrates the methodology.
Published Version
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