A method and system for metal tool wear prediction and maintenance recommendation generation

By constructing a multi-source physical feature perception system and a deep learning model, the problems of heterogeneity of multi-source signals and limited prediction accuracy were solved, enabling high-precision prediction of metal tool wear and intelligent maintenance suggestion generation, thereby improving the automation level and operational reliability of the production line.

CN122134319APending Publication Date: 2026-06-02HUNAN DESHAO HARDWARE PRODUCTS CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HUNAN DESHAO HARDWARE PRODUCTS CO LTD
Filing Date
2026-02-05
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

Existing technologies struggle to accurately extract metal tool wear characteristics from heterogeneous signals acquired from multi-source sensors, and prediction models have limited accuracy under complex machining conditions. This leads to a disconnect between maintenance decisions and production tasks, resulting in resource waste and production continuity issues.

Method used

A multi-source physical feature perception system is constructed. Through multi-dimensional temporal feature matrices, deep learning models, and multi-objective optimization algorithms, signal synchronization alignment, feature enhancement, and intelligent maintenance suggestion generation are achieved. Combined with deep residual networks, bidirectional long short-term memory networks, and attention mechanisms, wear status and remaining life are predicted, and intelligent maintenance suggestions are generated.

Benefits of technology

It significantly improves the prediction accuracy and generalization ability of the wear process, realizes deep integration with production tasks for decision-making, avoids unplanned downtime and resource waste, and improves the automation level and operational reliability of the production line.

✦ Generated by Eureka AI based on patent content.

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Abstract

This application belongs to the field of intelligent manufacturing technology, specifically relating to a method and system for predicting and generating maintenance suggestions for metal tool wear. It aims to solve the problems of low accuracy in tool wear prediction and delayed maintenance decisions. The method includes: constructing a multi-source physical feature sensing system to collect and align multi-dimensional signals during the machining process in real time; performing heterogeneous data preprocessing and feature enhancement, and filtering noise using adaptive wavelet packet decomposition; extracting multi-dimensional quantitative indicators and selecting a subset of core features; constructing a prediction model that integrates a deep residual network, a bidirectional long short-term memory network, and an attention mechanism to output the real-time wear level and remaining useful life; this application achieves accurate capture of wear status and optimized scheduling of maintenance resources through multi-source data fusion and deep learning evolutionary analysis, thereby improving machining quality and reducing unplanned downtime.
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