German rail network operator Deutsche Bahn has implemented 3D Spark, a software solution that automates the selection, costing, and pricing of Additive Manufacturing technologies based on Deutsche Bahn’s manufacturing requirements. 3D Spark utilises artificial intelligence to analyse 3D CAD files, 2D drawings (PDFs), and metadata to generate precise calculations for manufacturability, cost, lead time, and CO2 emissions.
To respond to incoming Requests for Quotation (RFQ), the Additive Manufacturing team at Deutsche Bahn used to perform several manual tasks, which included performing feasibility checks to ensure that AM is the most appropriate manufacturing approach for a given part or assembly. The system is also used for estimating the cost of AM parts using a variety of simplifications and assumptions regarding material cost, machine time, labour costs and any pre/post processes. It compares different AM technologies to select the most cost-effective and sustainable option.
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These tasks are repetitive, time-consuming, and prone to errors. In many cases, spreadsheet calculations, which are susceptible to errors, have been used to perform these tasks.
Implementing 3D Spark aims to significantly reduce the time required for cost calculations, while maintaining an accuracy of ±5%. Specifically, the implementation is expected to reduce cost calculation time by two-thirds, allowing the AM team to allocate more time to addressing customer challenges and generating revenue.
Using the 3D Spark platform, Deutsche Bahn can now track the amount of CO2 emitted while manufacturing a part. The organisation can also compare this data with other manufacturing technologies to identify the best manufacturing approach.
Deutsche Bahn has been able to make the quotation process more efficient by using 3D Spark’s built-in market price insights tool, which allows it to compare its own prices with average market prices.
The use of 3D Spark has reportedly helped Deutsche Bahn improve the accuracy and efficiency of its AM costing processes. This technology provides instant cost estimation, improves workflow efficiency, and reduces the time and resources required for manual tasks. Additionally, Deutsche Bahn can now effectively track the cost of AM parts over time, allowing the organisation to identify cost trends, explore additional opportunities, and optimise their AM processes.