Fast Laser Cutting Optimization Algorithm

Adelmann, Benedikt; Hellmann, Ralf

doi:10.1016/j.phpro.2011.03.075

Cited by 26 publications

(10 citation statements)

References 11 publications

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“…Adelmann and Hellmann [24] described a fast algorithm to optimize the laser parameters for laser fusion cutting process. The objective is to obtain a burr free laser cut.…”

Section: Introductionmentioning

confidence: 99%

Modeling and optimization of laser cutting operations

Gadallah

Abdu

2015

Manufacturing Rev.

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-Laser beam cutting is one important nontraditional machining process. This paper optimizes the parameters of laser beam cutting parameters of stainless steel (316L) considering the effect of input parameters such as power, oxygen pressure, frequency and cutting speed. Statistical design of experiments is carried in three different levels and process responses such as average kerf taper (T a ), surface roughness (R a ) and heat affected zones are measured accordingly. A response surface model is developed as a function of the process parameters. Responses predicted by the models (as per Taguchi's L 27 OA) are employed to search for an optimal combination to achieve desired process yield. Response Surface Models (RSMs) are developed for mean responses, S/N ratio, and standard deviation of responses. Optimization models are formulated as single objective optimization problem subject to process constraints. Models are formulated based on Analysis of Variance (ANOVA) and optimized using Matlab developed environment. Optimum solutions are compared with Taguchi Methodology results. As such, practicing engineers have means to model, analyze and optimize nontraditional machining processes. Validation experiments are carried to verify the developed models with success.

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“…Adelmann and Hellmann [24] described a fast algorithm to optimize the laser parameters for laser fusion cutting process. The objective is to obtain a burr free laser cut.…”

Section: Introductionmentioning

confidence: 99%

Modeling and optimization of laser cutting operations

Gadallah

Abdu

2015

Manufacturing Rev.

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“…Figure 4 shows a generic relationship between ablated depth and normalized fluence following Equation (13). The vertical dotted line shows the normalized threshold fluence Θth at which ablation begins to occur using the specified laser system and target material.…”

Section: (10) (11) (12)mentioning

confidence: 99%

“…The same has been done for laser fusion processes [13], where the author designs a 'fast laser cutting optimization algorithm' based on limiting the design parameters in order to limit the solution space. Another laser cutting optimisation study [14] for Nd:YAG lasers cutting thin superalloy sheet focused its attention on minimising the kerf width, taper and deviation using a hybrid approach of the Taguchi methodology and the grey relational analysis.…”

Section: Introductionmentioning

confidence: 99%

On-The-Fly Laser Machining: A Case Study for In Situ Balancing of Rotative Parts

Stoesslein

Axinte

Gilbert

2016

Journal of Manufacturing Science and Engineering

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“…Adelmann and Hellman [1] explored current approaches, such as caustic laser beam and surrogate criterion, while operating the TRUMPF machine. After analysing every step of the process, they found that the focal position is an influential factor that ensures stability in production.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modelling Study of Hardox400 Steel Parts’ Roughness and Hardness, Cut with CO2 Laser

Mileșan

Girdu

Cîrțînă

et al. 2020

SV-JME

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This study supports the research in the field of laser cutting of steel sheets HARDOX400 (H400), a less well-known and analysed material. An H400 sheet with a thickness of 10 mm was used; a cutting plan was prepared according to Taguchi's method; the input parameters (laser power, assistant gas pressure and cutting speed) were varied according to a specific rule, producing nine pieces. The initial factorial experiment was replicated four times under the same conditions as the initial one. The values for the cutting width between the piece and the plate, the hardness, and the roughness of the 45 resulting pieces were measured. The results were interpreted for each measured parameter, and by using design of experiments (DOE) from the Statistics 7.0 software, we obtained the description of roughness (R a ) in terms of power and speed using Response Surface Method (RSM) for prediction and correlation formula. Highlights• Indication of the mathematical expression of the roughness R a from algebraic and differential points of view. • Description of the cutting parameters of the CO 2 laser installation for cutting a Hardox400 plate according to a cutting plan, consisting of 45 pieces. • The use of the response surface and the three dimensional (3D) graph to determine the roughness R a depending on the input parameters, laser power, and cutting speed. • Establishing the formula for correlating the roughness R a according to the laser power and the cutting speed within the linear and quadric model, respectively the statistical calculation within the linear and quadratic interaction.

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Fast Laser Cutting Optimization Algorithm

Cited by 26 publications

References 11 publications

Modeling and optimization of laser cutting operations

Modeling and optimization of laser cutting operations

On-The-Fly Laser Machining: A Case Study for In Situ Balancing of Rotative Parts

Mathematical Modelling Study of Hardox400 Steel Parts’ Roughness and Hardness, Cut with CO2 Laser

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