In remanufacturing, Reverse Engineering (RE) tools and techniques could become essential in the rapid creation of digital 3D models of existing components, especially when drawings are no longer available. These models can be used to investigate potential remanufacturable parts, identify damages or defects and assist in the decision-making process for as-is rebuilding or redesigning of the part to improve performance. In this context, Additive Manufacturing (AM) technology is particularly well suited due to its capability to rapidly produce parts with minimal geometry constraints and at relatively low cost. This study proposes a methodological procedure based on the combined use of RE-AM to facilitate the remanufacturing decision-making process for better part performance and strategy. Moreover, through the use of Machine Learning (ML) algorithms, damages can be automatically and objectively identified and quantified. An application case is used to demonstrate the effectiveness of the proposed approach.

RE-AM Combined Use to Facilitate Decision-Making in Remanufacturing

Greco, Alessandro;Califano, America;
2024

Abstract

In remanufacturing, Reverse Engineering (RE) tools and techniques could become essential in the rapid creation of digital 3D models of existing components, especially when drawings are no longer available. These models can be used to investigate potential remanufacturable parts, identify damages or defects and assist in the decision-making process for as-is rebuilding or redesigning of the part to improve performance. In this context, Additive Manufacturing (AM) technology is particularly well suited due to its capability to rapidly produce parts with minimal geometry constraints and at relatively low cost. This study proposes a methodological procedure based on the combined use of RE-AM to facilitate the remanufacturing decision-making process for better part performance and strategy. Moreover, through the use of Machine Learning (ML) algorithms, damages can be automatically and objectively identified and quantified. An application case is used to demonstrate the effectiveness of the proposed approach.
2024
9783031526480
9783031526497
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4912440
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