A case study of efficient tolerance synthesis in product assemblies under loading

Modelling the effects of loads on mechanical assemblies in statistical tolerance analysis typically requires the use of computationally demanding numerical simulations. The associated Uncertainty Quantification (UQ) methods used for estimating yield are typically based on robust, yet computationally inefficient Monte Carlo (MC) simulation. Identifying optimum tolerances with tolerance synthesis requires multiple iterations of tolerance analysis. When combined with expensive numerical simulation of loading effects and a large number of model evaluation required by MC simulation, the computational cost can increase beyond practical limits. However, Polynomial Chaos Expansion (PCE) UQ methods have been under recent development which offers higher efficiency than MC sampling. PCE has been recently implemented in a Process Integration and Design Optimization (PIDO) tolerance synthesis approach for assemblies subject to loading. This work reports on an industry-based tolerance synthesis case study which demonstrates the high computational cost reductions achievable with the developed PCE based PIDO approach. The demonstrated approach can be adopted to effectively increase product robustness and manufacturing efficiency.