ACCOUNTING FOR MODEL UNCERTAINTY IN MACHINE LEARNING ASSISTED MECHANICAL DESIGN

Machine learning is becoming increasingly prevalent in mechanical design as it allows for surrogate models to replace expensive computational models. However, the accuracy of these models is of particular concern when safety-critical products are involved. In order to address this issue, we examine how to estimate model error by taking into account epistemic uncertainty in surrogate models when the design is also subject to randomness (aleatory uncertainty) in data. The paper clarifies important questions about modeling coupled epistemic and aleatory uncertainty when using surrogate models built from noise-free training points without aleatory uncertainty. Specifically, the study focuses on quantifying the effects of uncertainty in mechanical design by developing a most-probable-point based method. This method can be especially applicable for mechanical component design, where failure prevention is a critical concern, and the probability of failure is low. The proposed method is demonstrated using a shaft design as an example. The results show that the method can effectively estimate the model error and quantify the uncertainty in the design process. This approach can help designers to make more informed decisions by providing them with a better understanding of the limitations of surrogate models. By doing so, designers can ensure that their designs are safe and meet the required specifications.