Accelerated Testing by (CSCPV) Combined Systematic Calculated Pre-Validation Method 2017-26-0319
A full-bodied validation of automotive system emphasis on a comprehensive coverage of failure modes of component on one hand and evaluation with full system for the intended function of single component on the other has for long been cumbersome to most commercial vehicle manufacturers.
This paper focuses on optimizing the test method in rig testing to relieve the complexity in the structural validation as whole system level. The methodology proposed by authors focuses on accelerating the vibration testing of component by compressing the validation timelines by using CSCPV (Combined Systematic Calculated and Pre Validation) method.
This method selects the components of the system for validation by VFTM (Vital Few and Trivial Many) approach from existing testing database failure data and selects the worst predominant failure cases.
This CSCPV method uses systematically calculated representing mass from analysis to validate the intended component alone instead of entire system. The stress and fatigue life of component are estimated by this method for the same excitation used in system level and are comparable to conventional system level testing. This ensures that the suspected components of a system get validated completely through capturing of all its possible failure modes based on past data.
Apart from the above mentioned strong-hold of the methodology, some of its salient features that are to be accorded prominence are
Cost of unwanted (testing not required) components saved
Assembling time for all components of the system avoided
Fixture development time for validating the entire system is avoided
Modeling software / analytical method is sufficient for Systematic Calculation for representing mass
Being a small component alone considered for validation, smaller capacity (Test Rig) actuator is sufficient which considerably saves the cost of testing
These forms of output will help to improve our validation processes by improving life data analysis
