| Title: | Optimizing laser cutting of stainless steel using latin hypercube sampling and neural networks |
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| Authors: | ID Šket, Kristijan (Author)
ID Potočnik, David (Author)
ID Berus, Lucijano (Author)
ID Hernavs, Jernej (Author)
ID Ficko, Mirko (Author) |
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| Files: |  1-s2.0-S0030399224016785-main.pdf (3,38 MB)
MD5: 35F8348F05CFAF548C77214BAB0D8A38
 https://www.sciencedirect.com/science/article/pii/S0030399224016785?via%3Dihub
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FS - Faculty of Mechanical Engineering
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| Abstract: | Optimizing cutting parameters in fiber laser cutting of austenitic stainless steel is challenging due to the complex interplay of multiple variables and quality metrics. To solve this problem, Latin hypercube sampling was used to ensure a comprehensive and efficient exploration of the parameter space with a smaller number of trials (185), coupled with feedforward neural networks for predictive modeling. The networks were trained with a leave-oneout cross-validation strategy to mitigate overfitting. Different configurations of hidden layers, neurons, and training functions were used. The approach was focused on minimizing dross and roughness on both the top and bottom areas of the cut surfaces. During the testing phase, an average MSE of 0.063 and an average MAPE of 4.68% were achieved by the models. Additionally, an experimental test was performed on the best parameter settings predicted by the models. Initial modelling was conducted for each quality metric individually, resulting in an average percentage difference of 1.37% between predicted and actual results. Grid search was also per formed to determine an optimal input parameter set for all outputs, with predictions achieving an average ac curacy of 98.34%. Experimental validation confirmed the accuracy and robustness of the model predictions, demonstrating the effectiveness of the methodology in optimizing multiple parameters of complex laser cutting processes. |
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| Keywords: | laser cutting optimization, cut surface quality, dross formation, Latin hypercube sampling, feedforward neural network |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 20.08.2024 |
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| Article acceptance date: | 24.11.2024 |
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| Publication date: | 30.11.2024 |
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| Publisher: | Elsevier |
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| Year of publishing: | 2025 |
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| Number of pages: | str. 1-10 |
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| Numbering: | Vol. 182, pt. B, [article no.] 112220 |
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| PID: | 20.500.12556/DKUM-91520  |
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| UDC: | 621.9:004.8 |
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| ISSN on article: | 0030-3992 |
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| COBISS.SI-ID: | 220295427 |
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| DOI: | 10.1016/j.optlastec.2024.112220 |
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| Copyright: | © 2024 The Author(s) |
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| Publication date in DKUM: | 10.01.2025 |
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| Views: | 171 |
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| Downloads: | 53 |
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| Metadata: |    |
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| Categories: | Misc.
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