Experimental investigation of processing disturbances in laser surface patterning

García‐Girón, Antonio; Romano, Jean–Michel; Batal, Afif; Michalek, Aleksandra; Penchev, Pavel; Dimov, Stefan Simeonov

doi:10.1016/j.optlaseng.2019.105900

Cited by 8 publications

(3 citation statements)

References 33 publications

(42 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, any variations in processing conditions due to structuring disturbances during the LIPSS generation affect the surface functionality, too. Typically, the surface responses are analysed experimentally to confirm whether the functional performance is still within acceptable limits [21,22]. However, obtaining functional performance data from the laser treated surfaces is often time-consuming, limited to specific processing settings, and requires special instruments and measurement setups.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network tools for predicting the functional response of ultrafast laser textured/structured surfaces

Baronti

Michalek

Castellani

et al. 2022

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Artificial Neural Networks (ANNs) are well-established knowledge acquisition systems with proven capacity for learning and generalisation. Therefore, ANNs are widely applied to solve engineering problems and are often used in laser-based manufacturing applications. There are different pattern recognition and control problems where ANNs can be effectively applied, and one of them is laser structuring/texturing for surface functionalisation, e.g. in generating Laser-Induced Periodic Surface Structures (LIPSS). They are a particular type of sub-micron structures that are very sensitive to changes in laser processing conditions due to processing disturbances like varying Focal Offset Distance (FOD) and/or Beam Incident Angle (BIA) during the laser processing of 3D surfaces. As a result, the functional response of LIPSS-treated surfaces might be affected, too, and typically needs to be analysed with time-consuming experimental tests. Also, there is a lack of sufficient process monitoring and quality control tools available for LIPSS-treated surfaces that could identify processing patterns and interdependences. These tools are needed to determine whether the LIPSS generation process is in control and consequently whether the surface’s functional performance is still retained. In this research, an ANN-based approach is proposed for predicting the functional response of ultrafast laser structured/textured surfaces. It was demonstrated that the processing disturbances affecting the LIPSS treatments can be classified, and then, the surface response, namely wettability, of processed surfaces can be predicted with a very high accuracy using the developed ANN tools for pre- and post-processing of LIPSS topography data, i.e. their areal surface roughness parameters. A Generative Adversarial Network (GAN) was applied as a pre-processing tool to significantly reduce the number of required experimental data. The number of areal surface roughness parameters needed to fully characterise the functional response of a surface was minimised using a combination of feature selection methods. Based on statistical analysis and evolutionary optimisation, these methods narrowed down the initial set of 21 elements to a group of 10 and 6 elements, according to redundancy and relevance criteria, respectively. The validation of ANN tools, using the salient surface parameters, yielded accuracy close to 85% when applied for identification of processing disturbances, while the wettability was predicted within an r.m.s. error of 11 degrees, equivalent to the static water contact angle (CA) measurement uncertainty.

show abstract

Section: Introductionmentioning

confidence: 99%

Artificial neural network tools for predicting the functional response of ultrafast laser textured/structured surfaces

Baronti

Michalek

Castellani

et al. 2022

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

“…In particular, two types of LIPSS topographies were fabricated on steel masters using femtosecond laser processing. The first type was highly regular LIPSS with a periodicity of 800–900 nm , while the second was a MS topography, that is, LIPSS superimposed on micron-scale protrusions. Then, these textured steel masters were used to replicate such functional topographies on polydimethylsiloxane (PDMS) and PU elastomers to investigate their bacterial adhesion characteristics against Gram-negative Escherichia coli bacteria.…”

Section: Introductionmentioning

confidence: 99%

Anti-Biofouling Properties of Femtosecond Laser-Induced Submicron Topographies on Elastomeric Surfaces

et al. 2020

View full text Add to dashboard Cite

Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document.When citing, please reference the published version. Take down policyWhile the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.

show abstract

In situ three-dimensional laser machining system integrating in situ measurement, reconstruction, parameterization, and texture mapping

Xiao

Ren

Mei

et al. 2020

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Experimental investigation of processing disturbances in laser surface patterning

Cited by 8 publications

References 33 publications

Artificial neural network tools for predicting the functional response of ultrafast laser textured/structured surfaces

Artificial neural network tools for predicting the functional response of ultrafast laser textured/structured surfaces

Anti-Biofouling Properties of Femtosecond Laser-Induced Submicron Topographies on Elastomeric Surfaces

In situ three-dimensional laser machining system integrating in situ measurement, reconstruction, parameterization, and texture mapping

Contact Info

Product

Resources

About