Decision support workflow for repair with additive manufacturing : a case study in DED process

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Πορευόπουλος, Νικόλαος

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Advanced manufacturing processes are transforming both the design and the production of parts. Metal Additive Manufacturing (AM) is one of the revolutionary processes, providing unprecedented design freedom coupled with the capability for reasonable priced low volume productions of metal components. AMs versatility has attracted various industrial sectors are, with one of them being the repair and manufacturing industry. AM when coupled with conventional subtractive manufacturing processes, can enable the repair of a damaged part by filling the damaged area using additive processes and then finishing it by subtractive methods. However, there are few guidelines and metrics that quantitatively dictate if the best approach is to repair or remanufacture a part. To address this, this thesis delves into a decision support workflow aiming to provide a systematic approach for the feasibility study of a repair operation on a damaged part using Additive Manufacturing with the help of conventional process, based on a detailed analysis of metrics like design constraints, sustainability considerations, and techno-economic metrics. The implementation is done through a software in order to assist with the calculation of the techno-economical model but mainly with gathering all of the required information in a single point. The decision support workflow is verified through a case study involving the repair of shafts used in marine vessels. These shafts are made from stainless steel AISI 316L, but additional analysis was done with titanium Ti-6Al-4V considered as an alternative material. The repair process is conducted using a wire laser-based DED system (DED-LB/M) on a Hybrid Manufacturing robotic cell. The study identifies process and machine-related limitations and demonstrates the impact of part size and material selection on key performance indicators (KPIs). This case study results is creating a robust decision support tool that enhances the efficiency and sustainability of repair processes in additive manufacturing, providing valuable insights for both industry practitioners and researchers.



Additive Manufacturing, Hybrid manufacturing, Repair, Decision support