Iterative Durability Design of Products via Degradation-Informed Bayesian Optimization

Green manufacturing has become a pressing issue in recent years, driven by the acknowledgment of its long-term social and economic benefits, increasing demand for environmentally friendly products, and expanding regulations. One of the key approaches to attaining sustainability in green manufacturing is to design long-lasting products. Nevertheless, the traditional approaches have faced several unique challenges in designing a product with an extended lifetime, such as costly and time-consuming procedures, as well as the noisy, sparse, insufficient or incomplete data. This paper proposes a novel framework to tackle these issues from a data-driven perspective. Specifically, the proposed method employs Bayesian optimization with Monte-Carlo acquisition functions to take into account both product design factors and degradation signals and incorporate the inherent uncertainty in the modeling of underlying degradation processes and prediction of product lifetimes. A series of simulation studies are presented to assess the performance of the proposed method. A case study on the Lithium-ion battery dataset is further conducted, which demonstrates the advantages of the proposed method over existing benchmark approaches.
Note to Practitioners
—This research paper provides a novel data-driven approach to finding an optimal product design with a prolonged lifetime. The conventional methods for the product lifetime extension are often time-consuming and expensive, while the associated data is in general noisy or incomplete. The proposed framework addresses these challenges by encoding the general degradation path model into the Bayesian optimization. In particular, the proposed method involves an iterative process that consists of the following three main steps: 1) collecting degradation signals, product design factors, and lifetime (optional) of existing products; 2) constructing a general degradation model and incorporating these results into the Bayesian optimization; and 3) identifying the next design factors to evaluate. The proposed method is especially useful in cases where the exact lifetimes of many existing products are unknown, i.e., products are still operating or have been replaced prior to their failure, and we collect signals that (indirectly) reflect the degradation status of a product.