Gear Grinding Machine Data for Virtual Rolling Quality Control
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Solution Overview
Problem
The existing methods for quality control in gear grinding, particularly for components used in electromobility, require extensive testing efforts due to the need for 100% examination of interlinking components, which is time-consuming and computationally intensive.
Innovation Solution
A procedure that utilizes component-specific machine data recorded during grinding to evaluate the quality of interlinking components through a data model that correlates with results from both test-based rolling tests and virtual rolling tests, thereby reducing the need for extensive physical testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If 100% inspection of all gears manufactured in series production is carried out using rolling test procedures, then quality control and noise behavior assurance are improved, but testing effort and time consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by performing virtual rolling tests and calculating virtual twins of gear components during or immediately after the grinding process, before physical rolling tests are conducted. This allows early identification of potential quality issues and enables predictive quality assessment, reducing the need for extensive subsequent physical testing while maintaining high quality control standards
Solution Approach 2:
The patent creates virtual copies (virtual twins) of the actual gear components using recorded machine data from the grinding process. These virtual models replicate the geometric and physical properties of the real components, allowing virtual rolling tests to be performed on the copies instead of always testing the physical components, thereby significantly reducing testing effort while maintaining assessment accuracy
2Loss of time
If virtual twin calculation and analysis is used to identify noise anomalies and machine defects, then testing effort is reduced, but computational effort and processing time increase
Solution Approach 1:
The patent applies partial action by performing virtual rolling tests and virtual twin calculations only for critical quality parameters and selected gear components rather than analyzing every possible parameter for every component. This selective approach reduces computational effort while still identifying the most significant quality issues and noise anomalies that would require physical testing
Solution Approach 2:
The patent introduces an intermediary data model that maps relationships between grinding machine parameters and rolling test results. This data model acts as a mediator that translates recorded machine data into predictive quality assessments, reducing the need for complex full-scale virtual twin calculations while maintaining accurate quality prediction with lower computational requirements
3Measurement precision
If classic quality control loops based on measured test bench data are used, then measurement accuracy is improved, but testing time and productivity decrease
Solution Approach 1:
The patent implements feedback by using results from physical rolling tests to continuously update and refine the data model that maps grinding parameters to quality outcomes. This feedback loop allows the system to learn from actual test results and improve the accuracy of virtual predictions over time, enabling progressively reduced reliance on physical testing while maintaining or improving measurement precision
Solution Approach 2:
The patent replaces the mechanical testing system with a data-driven virtual testing system. Instead of relying solely on physical rolling tests to determine quality, the system uses recorded machine data from the grinding process and a data model to predict quality outcomes, substituting mechanical measurement with computational analysis that is faster and enables continuous production flow
Data Source
Figure 1
AI summary
Method comprising the process steps: grinding a gear (2) of a component (4) using a gear grinding machine (6), wherein during the grinding of the component (4) a plurality of component-specific machine data are recorded, such as machining parameters, spindle currents, control deviations or the like;Determining one or more results of a computer-implemented rolling test (cWP) of the gear teeth (2) of the component (4) by passing the component-specific machine data or parameters derived therefrom as input data to a data model, wherein the data model exhibits correlations between results of test bench-based rolling tests (pWP) and component-specific machine data associated with the results of test bench-based rolling tests (pWP), and wherein output data of the data model determined on the basis of the input data correspond to the result or results to be determined of the computer-implemented rolling test (cWP).