short-paper

Influence of Service Life on Stress Level of Portal Slewing Crane Based on Multi-detection Data Mining

Authors:

Wen Haobai,

Luo JianpingAuthors Info & Claims

AIAM2020: Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture

Pages 363 - 366

https://doi.org/10.1145/3421766.3421877

Published: 26 October 2020 Publication History

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Abstract

Portal slewing crane has long service time and bad working environment. The stress level of whole crane is directly related to the man-machine safety in the production. Aiming at portal slewing cranes with different service life, this paper selects high-stress measure points by means of counting multi-detection data, determines the relationship between overall stress level and service life, and further mines the distribution law and stress performance characteristics of high-stress measure points at various positions on cranes with different service life.

References

[1]

Sun Feng, Zhang Zhenxiong, Yu Heyuan. Design Rules For Port Cranes M. Beijing: China Communications Press. 2007:681.

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[2]

Liu Jin, Huang Guojian, Wang Xinhua. Research on fatigue crack growth life of gantry cranes J.Journal of Shanxi University of Science & Technology.2014, (32):142.

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[3]

Wang Xiaoping, Li Yidong, He Jiong. Investigation and Analysis on Corrosion of Crane Metal Structure in Zhoushan Area. J. Port Operation.2019, (6):35.

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[4]

China Waterborne Transport Research Institute. Technical specifications requirements for testing of jib cranes: JT/T 1262-2019.S. Beijing: China Communications Press, 2019.

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Index Terms

Influence of Service Life on Stress Level of Portal Slewing Crane Based on Multi-detection Data Mining
1. Applied computing
  1. Physical sciences and engineering
    1. Engineering
      1. Computer-aided design

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Published In

AIAM2020: Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture

October 2020

566 pages

ISBN:9781450375535

DOI:10.1145/3421766

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 October 2020

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Short-paper
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Refereed limited

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AIAM2020

AIAM2020: 2nd International Conference on Artificial Intelligence and Advanced Manufacture

October 15 - 17, 2020

Manchester, United Kingdom

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AIAM2020 Paper Acceptance Rate 100 of 285 submissions, 35%;

Overall Acceptance Rate 100 of 285 submissions, 35%

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