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Research on gear shaving error analysis based on knowledge graph.

Longlong Men1, Anjiang Cai2

  • 1School of Mechanical and Electrical Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055, People's Republic of China.

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|November 1, 2025
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Summary
This summary is machine-generated.

This study introduces a knowledge graph to connect gear shaving error factors, improving understanding and reducing tooth profile errors. Optimized radial feed settings are identified for specific overlap and axis intersection angle conditions.

Keywords:
Gear shaving tooth profile cutting depth errorKnowledge graphShaving-induced mid-profile concavity errorSiloization issueTooth profile error

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Area of Science:

  • Mechanical Engineering
  • Manufacturing Processes
  • Data Science

Background:

  • Gear shaving errors, specifically mid-profile concavity and tooth profile errors, stem from numerous complex factors lacking systematic interconnection.
  • Existing knowledge on these factors is siloized in dispersed text, hindering integrated utilization and understanding of influence mechanisms.

Purpose of the Study:

  • To address the siloization of knowledge regarding factors influencing gear shaving errors.
  • To develop computational methods for predicting and analyzing gear shaving errors based on cutting parameters.
  • To establish a knowledge graph for unified management and correlation analysis of error factors.

Main Methods:

  • Derivation of computational formulas for gear shaving tooth profile cutting depth error and shaving allowance.
  • Analysis of the influence of radial feed on these errors.
  • Construction of a knowledge graph using Neo4j to model interconnections between error factors.
  • Experimental validation of optimized radial feed parameters.

Main Results:

  • Identified an optimal radial feed range (44–50 μm) that simultaneously reduces mid-profile concavity and tooth profile errors under specific conditions (overlap 1.3–1.9, axis intersection angle error 0.2°–0.5°).
  • Successfully constructed a knowledge graph enabling unified management, correlation expression, and reasoning of shaving error factors.
  • Demonstrated the effectiveness of the knowledge graph in resolving missing intrinsic connections among factors and facilitating error analysis.

Conclusions:

  • The developed knowledge graph provides a systematic framework for understanding and managing gear shaving error factors.
  • Optimizing radial feed based on identified correlations can significantly reduce tooth profile errors.
  • This approach enhances the ability to reveal underlying mechanisms and solve tooth profile errors in gear shaving processes.