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Sensor deployment for variation diagnosis considering heterogeneity in single-station multi-step assembly processes

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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.

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Acknowledgments

The authors would like to thank the National Natural Science Foundation of China (no. 51075070), the Natural Science Foundation of Anhui Province (no.1708085ME104), the Suzhou Univ. Professor (PhD) Scientific Research Foundation (no.2016JB09), and the Key Project of Natural Science Foundation of Universities of Anhui Province (no. KJ2017A439, KJ2016A803).

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Authors and Affiliations

  1. Mine Machinery and Electronic Engineering Research Center, Suzhou University, Suzhou, 234000, China

    Kang He

  2. School of Mechanical Engineering, Southeast University, Nanjing, 211189, China

    Kang He & Minping Jia

  3. Department of Mechanical and Aerospace Engineering, Monash University, Clayton, 3800, Australia

    Lin Zhu

  4. School of Mechanical and Automotive Engineering, Anhui Polytechnic University, Wuhu, 241000, China

    Zhuanzhe Zhao

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  1. Kang He
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  2. Minping Jia
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Correspondence to Kang He.

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He, K., Jia, M., Zhu, L. et al. Sensor deployment for variation diagnosis considering heterogeneity in single-station multi-step assembly processes. Int J Adv Manuf Technol 94, 3889–3901 (2018). https://doi.org/10.1007/s00170-017-1083-6

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  • DOI: https://doi.org/10.1007/s00170-017-1083-6

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