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- W4312752434 abstract "Laser welding (LW) thanks to its flexibility, limited energy consumption and simple realization has a prominent role in several industrial sectors. LW process requires careful parameters’ tuning to avoid generating internal defects in the microstructure or a poor weld depth, which reduce the joining mechanical strength and result in waste. This work exploits a supervised machine learning algorithm to optimize the process parameters to minimize the generated defects, while catering for design specifications and tolerances to predict defect generation probability. The work outputs a predictive quality control model to reduce non-destructive controls in the LW of aluminum for automotive applications." @default.
- W4312752434 created "2023-01-05" @default.
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- W4312752434 date "2022-01-01" @default.
- W4312752434 modified "2023-09-26" @default.
- W4312752434 title "Minimization of defects generation in laser welding process of steel alloy for automotive application" @default.
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- W4312752434 doi "https://doi.org/10.1016/j.procir.2022.10.048" @default.
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