Development of an Automated and Continuous Data Extraction and Input Pipeline in AM for Simulation Processes using Ontology-Based Knowledge

Additive manufacturing (AM) known as rapid prototyping or 3D (three dimensional) printing, has been widely applied to multiple industrial domains to address supply chain (SC) disruption issues caused by crises, such as COVID-19. The significant benefits of AM have been proven in a substantial number of current research studies. However, the adoption of AM in SC, which would also enhance and maintain SC performance, still lacks focus. Meanwhile simulation has been identified as a powerful tool for mimicking and predicting processes by executing numerous scenario setups. However, the challenge remains in the manual operation of the simulation process, whereby operators normally input hundreds, if not thousands, of scenario datasets each time for execution. This paper aims to propose an automation process for simulation using the ontology-based database from the AM and SC ontology combination. The network optimisation model for AM SC is modelled using two simulators (Anylogistix and Tecnomatix Plant Simulation), thus formulating an automated data pipeline between simulation and knowledge base.