Traditionally, Forming Limit Diagrams (FLDs) and Curves (FLCs) have been used to predict failure in metal sheets, but these methods are often unreliable due to their sensitivity to strain paths and forming conditions. This results in either false positives or false negatives in failure predictions.
The invention presents a method for identifying potential failure locations in sheet metal components. The method involves determining regions of failures by using the Strain Non-Uniformity Index (SNI) across multiple critical planes within the metal sheet.
- Identification of critical planes in the sheet metal component, comprising points of peak and minimum major strain
- Calculation of Strain Non-Uniformity Index (SNI) for major and thickness strains in each critical plane
- Comparison of SNI values against threshold values to identify regions of potential failure
- Capability to predict punch travel at which failure is imminent
- Eliminates the need for laboratory samples; actual components can be directly used for failure prediction
- Accounts for user-defined failure criteria and varying strain paths, unlike traditional FLDs
- Provides actionable data to adjust manufacturing processes to prevent failures
Experimental validation has been carried out using flat blanks marked with circles, which deform into ellipses during testing. The strains were measured, and the SNI was calculated to identify critical planes and potential failure points.
The method has been tested using Nakazima samples from various grades of steel and aluminum alloys, proving the relationship between major strain SNI and thickness strain SNI for failure prediction.
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This technology helps reduce material waste and production costs by improving the accuracy of failure predictions in metal forming processes. This efficiency can lead to more sustainable manufacturing practices and lower costs for consumers.
Automotive, Aerospace, Electronics manufacturing
- Automotive manufacturing
- Aerospace component production
- Electronic tool manufacturing
201821003820
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