ultrafast laser
Recently Published Documents


TOTAL DOCUMENTS

2071
(FIVE YEARS 442)

H-INDEX

71
(FIVE YEARS 9)

2022 ◽  
Vol 34 (1) ◽  
pp. 012014
Author(s):  
Daniel Flamm ◽  
Myriam Kaiser ◽  
Marvin Feil ◽  
Max Kahmann ◽  
Michael Lang ◽  
...  
Keyword(s):  

2022 ◽  
pp. 2111920
Author(s):  
Weigao Sun ◽  
Lingfei Ji ◽  
Zhenyuan Lin ◽  
Jincan Zheng ◽  
Zhiyong Wang ◽  
...  

Author(s):  
Luca Baronti ◽  
Aleksandra Michalek ◽  
Marco Castellani ◽  
Pavel Penchev ◽  
Tian Long See ◽  
...  

AbstractArtificial 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.


2022 ◽  
Vol 120 (1) ◽  
pp. 012401
Author(s):  
I. A. Dolgikh ◽  
F. Formisano ◽  
K. H. Prabhakara ◽  
M. V. Logunov ◽  
A. K. Zvezdin ◽  
...  

2021 ◽  
Vol 130 (24) ◽  
pp. 243105
Author(s):  
Ruben Ricca ◽  
Victor Boureau ◽  
Yves Bellouard

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Lennart Gaus ◽  
Lothar Bischoff ◽  
Michael Bussmann ◽  
Eric Cunningham ◽  
Chandra B. Curry ◽  
...  

2021 ◽  
Author(s):  
Wei Hu ◽  
Muhammed Kilinc ◽  
Wendwesen Gebremichael ◽  
Christophe Dorrer ◽  
Jie Qiao
Keyword(s):  

Sign in / Sign up

Export Citation Format

Share Document