Effect of Stress Interaction on Multi-Stress Accelerated Life Test Plan: Assessment Based on Particle Swarm Optimization

Sustainability of products that seek to maintain ecosystem balance, such as electric vehicles or solar system inverters, often require extensive testing during their developmental stages in a manner that minimizes wastage and drives creativity. Multi-stress accelerated test planning is often used for these products, their subsystems and components if their in-service failures are driven by multiple stress factors. Multi-stress accelerated life testing (ALT) often expedites time to failure for highly reliable products. Many studies assume model parameters that may not be appropriate for the considered stress factors. Most importantly, the effect stress interaction has on the ALT plan is often ignored, especially for cases where historical data are lacking. To address this gap, in this work, a technique based on a combination of rapid experimental data collection and heuristic-based optimization is proposed for ALT planning. In addition, the effect of stress interaction on the ALT plan was also evaluated. Specifically, the Arrhenius model was used to develop a maximum likelihood mathematical expression for multi-stress factor scenarios with and without interaction. Subsequently, two optimization stages based on particle swarm optimization (PSO) were carried out using time varying inertia weight constants to drive early and late global and local searches, respectively. In the first stage, model parameters were estimated, while, in the second stage, an ALT optimal plan was generated based on a D-optimality criterion. Verification of stress factor interactions was carried out using graphical response analysis. An experiment, designed to investigate electromigration in solder joints under three stress factors (temperature, current density and mechanical load), was used to validate the study. The variation in the choice of Latin hypercube design (LHD) results in disparity in the levels of stress within each stress combination as well as sample allocation. Our results clearly show the need to investigate stress interactions prior to multi-stress acceleration planning.