Assessing the potential for additive manufacturable spare parts in the railway industry by a data-driven framework

Abstract

Due to the long-life span of railway vehicles, spare part availability is a major challenge for railway companies. To avoid downtime, high procurement costs, and shortages, railway companies often stock large and costly inventories. However, additive manufacturing (AM) makes it possible to cost-effectively produce spare parts in small quantities to reduce inventory. To assess the suitability of a spare part and choose the optimal supply strategy, economic and technical data must be available in good quality. The lack of centralised information and poor data quality is one of the biggest challenges for AM potential assessment.

A workflow was designed, in which system data from various databases was mapped and enhanced with technical information. In addition, the potential for additive manufacturability was evaluated based on costs and savings affected by transportation, design, manufacturing, storage compared to other supply strategies. The resulting database was then used to classify the spare parts based on specific characteristics. The defined classes provide information about the timeline for implementing the part and the type of application. A similarity check based on identified AM spare parts is used to search the entire database to identify additional potential AM parts. The result is a data-driven framework to conduct a holistic potential assessment for additive manufacturable spare parts.