A method for analyzing the traceability of defects in a clamp production

By constructing a differentiable symbolic causal graph and a counterfactual gradient masking mechanism, the problem of insufficient physical mechanism explanation in the attribution of fixture production defects is solved, achieving highly accurate and reliable root cause identification and providing a direct basis for process adjustment.

CN122222191APending Publication Date: 2026-06-16DONGGUAN ZHUOBO METAL TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
DONGGUAN ZHUOBO METAL TECH CO LTD
Filing Date
2026-03-12
Publication Date
2026-06-16

AI Technical Summary

Technical Problem

Existing methods for attributing defects in fixture manufacturing lack explanations of physical mechanisms, resulting in attribution results that are not verifiable in engineering, thus affecting the accuracy and reliability of production optimization decisions.

Method used

A physical mechanism-based differentiable symbolic causal graph (SCG) is constructed. The gradient is automatically backpropagated along the physical path through a differentiable symbolic actuator module. Combined with a counterfactual gradient masking mechanism and process-sensitive weights, a structured attribution report is generated.

Benefits of technology

It significantly improves the physical consistency and mathematical differentiability of attribution analysis, enhances the accuracy and robustness of root cause identification, provides technical credibility and operational guidance, and supports migration to similar fixture process scenarios without retraining the model.

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Abstract

The present application provides a kind of fixture production defect bad traceability analysis method, comprising: real-time acquisition and standardization multi-source process parameters, realize noise suppression and integrity check based on process standard;With fixture process knowledge base, process mechanism model is formalized into differentiable causal diagram, and the defect prediction after physical variable disturbance and counterfactual effect quantification are realized using differentiable symbol execution module;Effective physical path is screened through gradient mask mechanism, and credible attribution score is calculated in combination with process sensitivity weight;Finally, core root cause variable is extracted, structured attribution path chain is constructed, and interpretable attribution report with counterfactual suggestion and tolerance analysis is formed, the present application improves the accuracy and verifiability of defect root cause positioning in fixture production process, which is beneficial to process parameter optimization and defect improvement.
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