Generative Adversarial Networks for Resilient Design of Manufacturing Systems

Manufacturing systems often face unexpected disruptions such as machine failures or material shortages, which can severely impact the production performance. Traditional methods for addressing these disruptions tend to be time-consuming and resource-intensive, which cannot effectively maintain the resilience of manufacturing systems. Despite recent advances in digital twins (DT) and artificial intelligence (AI), very little has been done to mitigate high computational demands and generate resilient designs of manufacturing system under uncertainty. Therefore, this paper presents a new Generative Adversarial Network (GAN) approach for the on-the-fly design of manufacturing systems in response to production disruptions. First, we propose a novel Generative Adversarial Network (D-GAN) to generate diverse, adaptive system designs that align production performance with target key performance indicators (KPIs). Second, DT models are coupled with statistical metamodeling to sequentially generate large amounts of training data samples under different scenarios. Experimental results show the high potential of the proposed D-GAN approaches to generate cost-effective system designs and enhance manufacturing resilience.