Machine Tool Load Analysis for Statistical Tool Fault Detection
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Solution Overview
Problem
Existing methods for detecting tool abnormalities in machine tools are inadequate as they cannot accurately determine tool damage when loads are below the maximum recorded load, leading to potential tool breakage, reduced machining precision, and increased maintenance costs.
Innovation Solution
An apparatus and method that repeatedly measure the load of a workpiece transferring or machining unit, calculate a normal range based on the standard deviation of load data, and automatically detect tool abnormalities by comparing real-time load data against this normal range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the existing method compares current load only against maximum recorded load, then the detection method is simple, but it cannot accurately detect tool abnormalities when loads are below maximum load
Solution Approach 1:
The patent transforms the detection approach from comparing load against a single maximum value to comparing load against a dynamically calculated normal range. The normal range is derived from statistical parameters (mean and standard deviation) of repeatedly measured load data, enabling accurate detection of abnormalities even when loads remain below the maximum recorded value. This parameter transformation resolves the contradiction by improving detection accuracy through statistical analysis without requiring fundamentally new detection hardware.
Solution Approach 2:
The system implements continuous feedback by repeatedly measuring load data during machining operations and using this data to calculate and update the normal range. The calculated normal range is then fed back into the detection process, creating a closed-loop system that adapts to actual machining conditions. This feedback mechanism enables accurate abnormality detection while maintaining a relatively simple detection framework that builds upon existing load measurement capabilities.
2Reliability
If the existing method stops machining only when current load exceeds maximum load, then the control logic is simple, but it leads to tool breakage and reduced machining precision
Solution Approach 1:
The patent applies preliminary action by detecting tool abnormalities before they lead to tool breakage or significant machining precision deterioration. By continuously comparing load data against the statistically calculated normal range and triggering warnings or stoppages at earlier stages of abnormality, the system prevents catastrophic failures while maintaining productivity. This early intervention approach resolves the contradiction by catching tool degradation trends before they result in wasted machining time and resources.
Solution Approach 2:
The patent replaces the simple mechanical threshold-based control (comparing against maximum load) with a statistical analysis system that processes repeatedly measured load data. By substituting the crude mechanical comparison with statistical evaluation of load patterns, the system achieves more reliable detection of tool abnormalities, enabling timely interventions that preserve machining precision without unnecessarily interrupting productive operations.
3Productivity
If no abnormality detection is performed, then the operation is continuous and productive, but tool damage occurs leading to increased maintenance costs
Solution Approach 1:
The system implements self-service by automatically performing abnormality detection using load data that is already being measured during normal machining operations. The control unit repeatedly measures the load, calculates the normal range from this data, and automatically compares new measurements against this range to detect abnormalities. This self-monitoring capability enables continuous machining with automated tool health monitoring, resolving the contradiction by maintaining productivity while ensuring tool integrity through automatic detection without requiring additional manual inspection time.
Data Source
AI summary
The present disclosure relates to an apparatus and a method for detecting an abnormality of a tool of a machine tool, and more particularly, to an apparatus and a method for correcting backlash of a machine tool, which are capable of repeatedly measuring a load of a workpiece transferring unit or a workpiece machining unit, and automatically detecting an abnormality of a tool based on a normal range of load data that are proportional to a standard deviation of the load data measured based on any one of a mode value, a mean value, or a median value of the repeatedly measured load data.