W 形金属封严环成形型面几何质量在机检测研究

Haonan Pei, Sizhe Du, Ming Luo

科研成果: 期刊稿件文章同行评审

摘要

Metallic sealing ring is a static sealing structure part widely used in aero-engine, and its profile forming accuracy is the key to the sealing performance, which is of great significance in reducing engine internal flow losses and improving engine thrust-to-weight ratio. Currently, the formed quality of metallic sealing ring mainly uses final destructive sampling inspection, and the lack of geometric quality inspection method in the forming process prevents timdy finding its forming defects. To address the above problems, an on-machine inspection research of geometric quality for metallic W-ring formed profile is carried out. A multi-level registration and distance thresholds based irregular section profile measurement point cloud preprocessing method is proposed, to solve the problem of serious outliers, missing and noise in the measurement point cloud. In order to realize the accurate extraction of complex geometric feature parameters, a minimum area bounding rectangle based profile geometric feature parameters extraction method, a medial axis based wall thickness extraction method, and a curvature and pre-location based circular feature parameters extraction method are proposed. Based on the dual laser scanners, an on-machine measurement system is built, and integrate the above methods, the hardware and software module are developed. Finally, the experimental results demonstrate the advantage of the method. Compared with the traditional destructive inspection results, the average relative error of 1.350% for the profile geometric feature parameters, 3.219% for the wall thickness and 1.372% for the circular feature parameters.

投稿的翻译标题On-machine Inspection of Geometric Quality for Metallic W-ring Formed Profile
源语言繁体中文
页(从-至)35-50
页数16
期刊Jixie Gongcheng Xuebao/Journal of Mechanical Engineering
60
20
DOI
出版状态已出版 - 10月 2024

关键词

  • feature extraction
  • geometry quality
  • metallic sealing ring
  • on-machine inspection
  • point cloud preprocessing

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