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A Generalised Approach on Kerf Geometry Prediction during CO2 Laser cut of PMMA Thin Plates using Neural Networks

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Abstract

This study presents an application of feedforward and backpropagation neural network (FFBP-NN) for predicting the kerf characteristics, i.e. the kerf width in three different distances from the surface (upper, middle and down) and kerf angle during laser cutting of 4 mm PMMA (polymethyl methacrylate) thin plates. Stand-off distance (SoD: 7, 8 and 9 mm), cutting speed (CS: 8, 13 and 18 mm/sec) and laser power (LP: 82.5, 90 and 97.5 W) are the studied parameters for low power CO2 laser cutting. A three-parameter three-level full factorial array has been used, and twenty-seven (33) cuts are performed. Subsequently, the upper, middle and down kerf widths (Wu, Wm and Wd) and the kerf angle (KA) were measured and analysed through ANOM (analysis of means), ANOVA (analysis of variances) and interaction plots. The statistical analysis highlighted that linear modelling is insufficient for the precise prediction of kerf characteristics. An FFBP-NN was developed, trained, validated and generalised for the accurate prediction of the kerf geometry. The FFBP-NN achieved an R-all value of 0.98, in contrast to the ANOVA linear models, which achieved Rsq values of about 0.86. According to the ANOM plots, the parameter values which optimize the KA resulting in positive values close to zero degrees were the 7 mm SoD, 8 mm/s CS and 97.5 W LP.

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Data Availability

The datasets generated during and/or analysed during the current study are available in the [Research Square] repository, [https://www.researchsquare.com/article/rs-268745/v1].

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Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Design and Manufacturing Laboratory, School of Technology, University of Thessaly, 43100, Karditsa, Greece

    John D. Kechagias & Konstantinos Ninikas

  2. Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, Patras, Greece

    Panagiotis Stavropoulos

  3. Sustainable Manufacturing Systems Centre, Manufacturing Department, Cranfield University, Cranfield, UK

    Konstantinos Salonitis

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  1. John D. Kechagias
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Contributions

Conceptualization, J.K. and K.N.; methodology, J.K. and K.N; software, J.K., P.S and K.S; validation, J.K. and P.S.; formal analysis K.N.; investigation, K.N.; resources, J.K.; writing—original draft preparation, J.K., K.N. P.S. and K.S.; review and editing, J.K., K.N, P.S. and K.S.; supperervision, J.K.; project administration, J.K.; All authors have read and agreed to the published version of the manuscript.

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Correspondence to John D. Kechagias.

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Article Highlights

• Application of FFBP-NN to predict kerf geometry during laser cutting of PMMA thin plates

• Robust design using full orthogonal experimental arrays

• Accurate predictions of kerf widths and kerf angle

• Map min-max method is adopted for data normalization

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Kechagias, J.D., Ninikas, K., Stavropoulos, P. et al. A Generalised Approach on Kerf Geometry Prediction during CO2 Laser cut of PMMA Thin Plates using Neural Networks. Lasers Manuf. Mater. Process. 8, 372–393 (2021). https://doi.org/10.1007/s40516-021-00152-4

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