A Recommendation System for Aircraft Parts Processing Methods Based on a Hybrid of Rules and Instances

Linhao Qin, Yiran Luo, Zhenjun Ming*, Hongpeng Zhang, Hu Lu, Wei Chen, Baoguo Chen, Yafeng Ding, Qin Wen, Ruiqiang Lyu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Addressing the business logic and relevant requirements of process personnel during the selection stage of processing methods for aerospace structural parts, this paper introduces a recommendation system for aircraft parts processing methods based on a hybrid of rules and instances. Firstly, the typical features and processing methods of aircraft parts are defined, vectorized, and their feature similarity is calculated using the Euclidean distance method. Based on industry standards, a rule library for selecting processing methods is established to filter recommendation results. For the characteristics of the parts, the recommendation system retrieves the most similar features and their processing methods from the feature instance library for recommendation. Validation tests demonstrate that this recommendation system can effectively recommend suitable processing methods based on the features of aircraft parts.

Original languageEnglish
Title of host publication2024 7th International Conference on Computer Information Science and Application Technology, CISAT 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages803-808
Number of pages6
ISBN (Electronic)9798350375107
DOIs
Publication statusPublished - 2024
Event7th International Conference on Computer Information Science and Application Technology, CISAT 2024 - Hangzhou, China
Duration: 12 Jul 202414 Jul 2024

Publication series

Name2024 7th International Conference on Computer Information Science and Application Technology, CISAT 2024

Conference

Conference7th International Conference on Computer Information Science and Application Technology, CISAT 2024
Country/TerritoryChina
CityHangzhou
Period12/07/2414/07/24

Keywords

  • feature similarity
  • processing methods
  • recommendation system

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