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- W2903891330 abstract "To reduce the energy consumption of metal additive manufacturing, a thorough understanding of energy consumption in each process states is important. This paper introduces an algorithm to automatically identify the process states of a SLM machine based on power data. The proposed algorithm outlines a three-step procedure: data preprocessing, state identifier, results storage and assessment. Savizky-Golay (SG) filter and Extremum Slope of Regression Lines (ESRL) methods were used in the algorithm. Two samples of power profile were used to validate the algorithm, and the total error rates are both within 2%, average identification time for each data is 0.0016 seconds. In addition, some issues to be addressed are given, including potential for process state monitoring, state identification for scanning with different parameters, development of intelligent algorithm, and higher sampling rate." @default.
- W2903891330 created "2018-12-22" @default.
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- W2903891330 date "2018-08-01" @default.
- W2903891330 modified "2023-09-26" @default.
- W2903891330 title "Automated Process State Identification for Metal Additive Manufacturing based on Power Data" @default.
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- W2903891330 doi "https://doi.org/10.1109/coase.2018.8560571" @default.
- W2903891330 hasPublicationYear "2018" @default.
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