Most current methods for product verification and reliability assurance are based upon statistical sample sizes and the underlying probability distributions. But that approach typically results in industrially unmanageable sample sizes and high test resource requirements. The proposed approach in this thesis uses a generalized "Inverse Most-Probable-Limit State" concept, which takes a Monte-Carlo based most likely noise factor scenario in all load dimensions into account for each failure mode.
Frank Gerhorst
Fraunhofer IPK stochastic mechatronic reliability sample size reduction virtual product creation quality assurance Monte-Carlo method design for six sigma Maschinenbauingenieur Elektroingenieur Maschinenbauingenieure Elektroingenieure