Experimental investigation to optimize laser cutting process parameters for difficult to cut die alloy steel using response surface methodology

Patel, A. R.; Bhavsar, Sanket N.

doi:10.1016/j.matpr.2020.11.201

Cited by 10 publications

(11 citation statements)

References 16 publications

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“…Roughness values obtained in the present work are similar to those reported by Jarosz et al for AISI-316 stainless steel [14] and by Rajaram et al [18] for 4130 steel. On the contrary, for EN-31 steel, lower roughness values around 1 µm were obtained [15]. As a general trend, the highest standard deviation was found in experiments 2, 4, 6, and 8, corresponding to high frequency, compared to the rest of the experiments performed with low frequency.…”

Section: Ra Dimensional Error and Burr Thicknessmentioning

confidence: 73%

Effect of Process Parameters on the Quality of Laser-Cut Stainless Steel Thin Plates

Buj-Corral

Costa-Herrero

Domínguez-Fernández

2021

Metals

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At present, laser cutting is currently employed to cut metallic plates, due to their good finish and dimensional quality, as well as because of the flexibility of the process to obtain different shapes. In the present paper, surface roughness, dimensional accuracy, and burr thickness of thin plates of 0.8 mm are studied as functions of different process parameters: pulse frequency, pulse width, and speed. Eight different experiments were performed according to a full 23 factorial design, with two replicates each. Square specimens of 10 mm × 10 mm were cut. Arithmetical mean roughness Ra was measured with a contact roughness meter, and the dimensions and burr thickness with a micrometer. Ra values ranged between 1.89 and 3.86 µm, dimensional error values between 0.22 and 0.93%, and burr thickness between 2 and 34 µm. Regression analysis was performed, and linear models were obtained for each response. Results showed that roughness depends mainly on frequency, on the interaction of frequency and pulse width and on pulse width. The dimensional error depends on pulse width, frequency, and the interaction between pulse width and speed. Burr thickness is influenced by frequency, pulse width, and the interaction between frequency and speed. Multi-objective optimization showed that, in order to simultaneously minimize the three responses, it is recommended to use high frequency (80 Hz), high pulse width (0.6 ms), and high speed (140 mm/min). The present study will help to select appropriate laser cutting conditions in thin plates, in order to favor good surface finish and dimensional accuracy, as well as low burr thickness.

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Section: Ra Dimensional Error and Burr Thicknessmentioning

confidence: 73%

Effect of Process Parameters on the Quality of Laser-Cut Stainless Steel Thin Plates

Buj-Corral

Costa-Herrero

Domínguez-Fernández

2021

Metals

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“…These parameters include Ra-Arithmetical mean of height and Rz-Maximum height of profile. Furthermore, one hybrid parameter, Rmr (c)-Load length ratio of profile curve elements to the evaluation length at cut level c (% or µm), was used [1,5].…”

Section: Surface Qualitymentioning

confidence: 99%

“…The output layer acts mainly as a network output interface. [5,6,13]. The transfer function can be divided into two categories.…”

Section: Mathematical Model Of the Neuronmentioning

confidence: 99%

“…Furthermore, the network may consist of several hidden layers designed to perform the desired operations as a whole. The output layer acts mainly as a network output interface [5,6,13].…”

Section: Mathematical Model Of the Neuronmentioning

confidence: 99%

“…Therefore, the article's aim, considering the previous introduction, is to independently evaluate the surfaces that have arisen as a result of applying a water jet and laser beam to the aforementioned surfaces [5]. Due to the energy loss during the passage, when the cutting is carried out, the quality of the cut surface changes, which can be described by the roughness or waviness parameters, both in 2D and 3D [2].…”

Section: Introductionmentioning

confidence: 99%

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Solving the Issue of Discriminant Roughness of Heterogeneous Surfaces Using Elements of Artificial Intelligence

et al. 2021

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This work deals with investigative methods used for evaluation of the surface quality of selected metallic materials’ cutting plane that was created by CO2 and fiber laser machining. The surface quality expressed by Rz and Ra roughness parameters is examined depending on the sample material and the machining technology. The next part deals with the use of neural networks in the evaluation of measured data. In the last part, the measured data were statistically evaluated. Based on the conclusions of this analysis, the possibilities of using neural networks to determine the material of a given sample while knowing the roughness parameters were evaluated. The main goal of the presented paper is to demonstrate a solution capable of finding characteristic roughness values for heterogeneous surfaces. These surfaces are common in scientific as well as technical practice, and measuring their quality is challenging. This difficulty lies mainly in the fact that it is not possible to express their quality by a single statistical parameter. Thus, this paper's main aim is to demonstrate solutions using the cluster analysis methods and the hidden layer, solving the problem of discriminant and dividing the heterogeneous surface into individual zones that have characteristic parameters.

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Modeling and process parameter optimization of laser cutting based on artificial neural network and intelligent optimization algorithm

Ren

Fan

Pan

et al. 2023

Int J Adv Manuf Technol

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Experimental investigation to optimize laser cutting process parameters for difficult to cut die alloy steel using response surface methodology

Cited by 10 publications

References 16 publications

Effect of Process Parameters on the Quality of Laser-Cut Stainless Steel Thin Plates

Effect of Process Parameters on the Quality of Laser-Cut Stainless Steel Thin Plates

Solving the Issue of Discriminant Roughness of Heterogeneous Surfaces Using Elements of Artificial Intelligence

Modeling and process parameter optimization of laser cutting based on artificial neural network and intelligent optimization algorithm

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