Tire Uniformity Machine Spindle Force Characterization
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Solution Overview
Problem
Current tire uniformity machines provide inconsistent test results due to inadequate characterization of spindle forces, which are influenced by misalignment and unique force characteristics of machine components, leading to inaccurate filtering and potential misclassification of tire nonuniformity.
Innovation Solution
A method for characterizing lateral and radial spindle forces of a tire uniformity machine, involving detailed analysis of spindle and chuck assembly misalignments, nose cone interactions, and load wheel forces to generate accurate characterization waveforms that can be used to filter out machine-induced errors from tire test data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional tire uniformity testing is performed without component characterization, then the testing procedure is simple, but the test results are inconsistent and unreliable
Solution Approach 1:
The patent applies preliminary action by performing component characterization tests before normal tire uniformity testing. The spindle and load wheel are characterized using calibration tires to generate correction waveforms that are stored and applied during subsequent production testing, eliminating the need to repeat characterization and ensuring consistent results.
Solution Approach 2:
The patent uses calibration tires with known, controlled nonuniformity characteristics to create representative models of tire behavior. These calibration tires serve as copies that allow characterization of machine components without requiring actual production tires, enabling the generation of correction waveforms that can be applied to all subsequent tests.
2Measurement precision
If filtering is applied to tire test results to account for machine characteristics, then measurement accuracy improves, but testing time increases due to additional processing
Solution Approach 1:
The filtering operation is performed in advance during the characterization phase rather than during production testing. Correction waveforms are generated beforehand and stored in memory, allowing rapid application during normal testing without adding significant time to the production test procedure.
Solution Approach 2:
The patent replaces complex mechanical filtering operations with computational signal processing. Instead of physical filtering mechanisms, the system uses digital subtraction of characterization waveforms from test waveforms, achieving accurate filtering through mathematical operations that are computationally efficient and rapid.
3Measurement precision
If spindle and load wheel characterization is performed, then machine-induced errors are reduced, but the characterization process itself introduces additional sources of potential error
Solution Approach 1:
The system performs self-characterization by using its own measurement capabilities to evaluate its components. The uniformity machine characterizes its own spindle and load wheel using calibration tires, allowing the system to identify and correct its own errors without requiring external calibration equipment or intervention.
Solution Approach 2:
The characterization process generates feedback information in the form of correction waveforms that are stored and applied during production testing. This feedback loop allows the system to continuously compensate for machine-induced errors, with the correction data derived from systematic characterization of each component's force contributions.
Data Source
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AI summary
A method for characterizing spindle forces of a tire uniformity machine (10) includes receiving a tire in an apparatus having an upper spindle (50) and rim (48) and a lower spindle and rim (30), wherein the rims (30, 48) capture the tire therebetween. A measurement data waveform is collected and an angular offset between the rims (30, 48) to define an engagement position is determined. The measurement data waveform is designated as a tire result waveform, and the measurement data waveform is appended to a collection of measurement data waveforms for each engagement position. Once the predetermined number of waveforms for the engagement position has been obtained, an average waveform for each engagement position is computed. A characterization waveform for each average waveform is then generated.