Simulation, statistical modeling, and optimization of CO2 laser cutting process of polycarbonate sheets

Moradi, Mahmoud; Moghadam, Mojtaba Karami; Shamsborhan, Mahmoud; Beiranvand, Zeinab Malekshahi; Rasouli, Alireza; Vahdati, Mahdi; Bakhtiari, Arash; Bodaghi, Mahdi

doi:10.1016/j.ijleo.2020.164932

Cited by 47 publications

(20 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moradi et al [25] developed a finite element model to simulate the CO laser cutting of (3.2 mm) PMMA plates. They concluded that increasing the laser cutting speed from 4 to 20 mm/s and decreasing the laser power from 50 to 20 W results in reducing the heat-affected zone.…”

Section: Introductionmentioning

confidence: 99%

“…Concluding, all the above investigations studied theoretically or experimentally the kerf characteristics of the thermoplastic PMMA thin plates (3-12 mm) within the following laser power and velocity ranges: (i) miniaturised structures: power bellow 2.5 W and velocity up to 64 mm/s [20], or power between 13 and 23 W and velocities between 202 and 586 mm/s [18]; (ii) Thin plate cutting with power higher than 100 W and velocities up to 150 mm/s [1,17,19,21,23,24,26], or power between 20 and 40 W and velocities between 2 and 18 mm/s [10,25]. The kerf characteristics that measured were: (i) average surface roughness between 1 and 15 μm, (ii) kerf widths (top and bottom) between 0.1 and 1.5 mm, (iii) kerf angle between 0 and 2 degrees, and (iv) HAZ depth between 0.1 and 0.33 mm.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Kechagias

Ninikas

Stavropoulos

et al. 2021

Lasers Manuf. Mater. Process.

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Kechagias

Ninikas

Stavropoulos

et al. 2021

Lasers Manuf. Mater. Process.

View full text Add to dashboard Cite

show abstract

“…Oktem et al 8 designed L 27 and L 9 Taguchi orthogonal arrays to find the optimal parameters that reduce product defects. Moradi et al 9 conducted experiments to investigate the effects of process parameters through variance analysis. Non‐iterative optimization methods are easy to implement and have been widely used in industry.…”

Section: Introductionmentioning

confidence: 99%

Intelligent injection molding: Parameters self‐learning optimization using iterative gradient‐approximation adaptive method

Dong

Zhao

Zheng³

et al. 2021

J of Applied Polymer Sci

View full text Add to dashboard Cite

Intelligent injection molding consists of three aspects: intelligent parameter optimization, process monitoring and control. The optimal process parameters are critical to guarantee product quality. Injection molding is a typical batch process and has the property that previous runs can provide feedback to optimize subsequent runs. This study proposes a self-learning parameter optimization method named iterative gradient-approximation adaptive optimization (IGAO) method, which adopts the batch-to-batch information to remove the need to establish optimization model with large numbers of experiments. The analysis and the optimization of the parameters can be performed simultaneously. The IGAO method approximates the gradient iteratively and assigns an adaptive step size to each parameter according to gradient accumulation.The experiments conducted in both simulation software and injection molding machine prove that the method has fast convergence speed. Standard product weight can be obtained within 11 runs from three different starting process parameters. Experiment results show that 25% less steps are needed compared with the traditional gradient descent method. The method also has good stability to resist disturbances during the optimization procedure. In general, the proposed IGAO method is fast, stable and robust, and it has good prospects for parameter optimization in batch processes.

show abstract

“…Concluding, all the above investigations studied theoretically or experimentally the kerf characteristics of the thermoplastic PMMA thin plates (3-12mm) within the following laser power and velocity ranges: (i) miniaturised structures: power bellow 2.5W and velocity up to 64mm/s [18], or power between 13-23W and velocities between 202-586mm/s [16]; (ii) Thin plate cutting with power higher than 100W and velocities up to 150mm/s [19,1,17,21,22,15,24], or power between 20-40W and velocities between 2-18mm/s [8,23]. The kerf characteristics that measured were: (i) average surface roughness between 1-15µm, (ii) kerf widths (top and bottom) between 0.1 to 1.5mm, (iii) kerf angle between 0-2( o ), and (iv) HAZ depth between 0.1-0.33mm.…”

Section: Introductionmentioning

confidence: 99%

“…Morady et al [23] developed a nite element model to simulate the CO 2 laser cutting of (3.2mm) PMMA plates. They concluded that increasing the laser cutting speed from 4 to 20 mm/s and decreasing the laser power from 50 to 20 W, results in the reduction of the heat-affected zone.…”

Section: Introductionmentioning

confidence: 99%

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

Kechagias

Ninikas

Stavropoulos

et al. 2021

Preprint

View full text Add to dashboard Cite

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.

show abstract

Simulation, statistical modeling, and optimization of CO2 laser cutting process of polycarbonate sheets

Cited by 47 publications

References 35 publications

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

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

Intelligent injection molding: Parameters self‐learning optimization using iterative gradient‐approximation adaptive method

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

Contact Info

Product

Resources

About