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Where you are: TXGANews CenterNews Detail Writer:TXGA    Posted time 2018.09.18 16:30:32    浏览量:194次

How to detect the life of the connector

The service life of the connector is the primary indicator for measuring the reliability of the connector. As the demand for trouble-free performance of electronic products continues to increase, the increased service life in the connector design becomes a design orientation. In addition, increased market competition also requires designers to find suitable materials in non-expensive alloys to reduce connector costs. In many cases, the combined result of these trends has brought the copper alloy's operating characteristics closer to its performance limits.

Initial contact force is an important factor in connector design and material properties. Since the elastic deformation is converted into plastic deformation in the contact member, the stress release causes a decrease in the contact force. If the contact force is below a certain critical level, the contact will fail. Therefore, predicting stress relief as a function of time and temperature correlation is naturally a key factor in ensuring connector reliability. The following TXGA details the stress release test to predict the life of the connector.

Stress release data is an effective tool for designers to predict the useful life of electronic connectors and to make decisions about the choice of contact materials based on available data. These data are now widely used in the computer, communications and automotive electronics industries. At present, data on the life cycle of products is very scarce, especially in the computer field. Not only that, it is a more useful piece of data that shortens the product development cycle and expiration date.

Most connector designers use stress-releasing data primarily to narrow the choice of contact material to the application. However, many designers are also looking for appropriate test methods to more accurately predict the characteristics of the connector's useful life. This greatly reduces the number of samples required for testing and the associated costs associated with testing many samples.