State-Of-The-Art and Trends in CO2 Laser Cutting of Polymeric Materials—A Review

Lasers Manuf. Mater. Process.

et al. 2021

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 CO 2 laser cutting. A three-parameter three-level full factorial array has been used, and twentyseven (3 3 ) 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.

Section: Introductionmentioning

confidence: 99%

“…Kerf width is one of the most studied factors for the laser cutting quality performance [16][17][18] of thin thermoplastic materials and is affected by a number of process parameters such as the focusing distance, spot diameter, cutting speed, assistgas pressure and laser power (see Fig. 1; [2,7]).…”

Section: Introductionmentioning

confidence: 99%

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

Lasers Manuf. Mater. Process.

et al. 2021

“…Any material removal process utilising a laser beam as a cutting tool presents many bene ts such as fast cutting speed (and thus higher productivity rates), high accuracy, good control of the heat affected zone and minimal deformation, especially when low power CO 2 lasers are used [1,2]. Low power lasers are employed to process polymer based materials in a wide range of applications [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…Kerf width is one of the most studied factors for the laser cutting quality performance [14][15][16] of thin thermoplastic materials and is affected by a number of process parameters such as the focusing distance, spot diameter, cutting speed and laser power [2]. A wide experimental range of laser power and cutting speed values has been investigated in the literature [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

A generalised approach on kerf geometry prediction during CO2 laser cut of PMMA thin plates using neural networks

et al. 2021

Preprint

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 PMMA thin plates. Stand-off distance, cutting speed and beam power are the studied parameters for the case of low power CO2 laser cutting. A three-parameter three-level full factorial array has been used and twenty-seven (33) cuts were performed. Subsequently, the kerf width and angle were measured and analysed through ANOM, ANOVA and interaction plots. The statistical analysis highlighted that linear modeling is insufficient for the precise prediction of kerf characteristics. A FFBP-NN was developed, trained, validated and generalised for the accurate prediction of the kerf geometry. The FFBP-NN achieved an R-sq value of 0.98, in contrast to the ANOVA linear models which achieved a value of about 0.90.

“…Any material removal process utilising a laser beam as a cutting tool presents many bene ts such as fast cutting speed (and thus higher productivity rates), high accuracy, good control of the Heat Affected Zone (HAZ) and minimal deformation, especially when low power CO 2 lasers are used [1,2]. Low power lasers are employed to process polymer-based materials in a wide range of applications [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A generalised approach on kerf geometry prediction during CO2 laser cut of PMMA thin plates using neural networks

et al. 2021

Preprint

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.