	US 12,032,878 B2
	Method for determining laser machining parameters and laser machining device using this method
David Bruneel, Sougne-Remouchamps (BE); Liliana Cangueiro, Leuven (BE); Paul-Etienne Martin, Bordeaux (FR); José-Antonio Ramos De Campos, Angleur (BE); and Axel Kupisiewicz, Neupré (BE)
Assigned to LASER ENGINEERING APPLICATIONS, Angleur (BE)
Appl. No. 16/964,147
Filed by LASER ENGINEERING APPLICATIONS, Angleur (BE)
PCT Filed Jan. 25, 2019, PCT No. PCT/EP2019/051914 § 371(c)(1), (2) Date Jul. 22, 2020, PCT Pub. No. WO2019/145513, PCT Pub. Date Aug. 1, 2019.
Claims priority of application No. 2018/5046 (BE), filed on Jan. 26, 2018.
Prior Publication US 2021/0031304 A1, Feb. 4, 2021
Int. Cl. G06F 30/20 (2020.01); B23K 26/06 (2014.01); B23K 26/0622 (2014.01); B23K 26/082 (2014.01); B23K 26/359 (2014.01); B23K 26/382 (2014.01); B23K 26/40 (2014.01); B23K 26/70 (2014.01); G05B 19/4155 (2006.01)

CPC G06F 30/20 (2020.01) [B23K 26/0624 (2015.10); B23K 26/0626 (2013.01); B23K 26/082 (2015.10); B23K 26/359 (2015.10); B23K 26/382 (2015.10); B23K 26/40 (2013.01); B23K 26/70 (2015.10); B23K 26/702 (2015.10); G05B 19/4155 (2013.01); G05B 2219/45165 (2013.01)]

16 Claims

1. A method for determining laser machining parameters for the machining of a material with a laser machining system and comprising the following steps:

a) providing a learning database comprising a plurality of pairs of machining data samples comprising machining results obtained as a function of the laser machining parameters used;

b) providing a central unit;

c) defining at said central unit a machining result sought of a material to be machined;

d) providing said central unit with a learning machining function capable of learning on the basis of said plurality of pairs of machining data samples, said learning machining function comprising an unsupervised learning algorithm, a supervised learning algorithm, a semi-supervised learning algorithm, or a reinforcement learning algorithm, wherein the learning machining function is capable of defining for said machining result sought and for said machining system the following laser machining parameters:

information regarding a polarization of said machining laser beam,

a pulse energy of said machining laser beam E_p,

a diameter of said machining laser beam at the focal point w,

an order of the Gaussian p of said machining laser beam between 1 and 20,

a pulse repetition rate PRR of pulses n of the machining laser beam,

a wavelength of the machining laser beam;

e) making said learning machining function with said central unit to learn on the basis of said plurality of pairs of machining data samples, so that said laser machining system can machine said material to be machined according to the machining result sought, by providing it with the machining parameters determined by said learning machining function when learning it,

wherein the method further comprises the following additional steps:

f) providing a laser machining system comprising:

said material to be machined;

said laser machining device comprising:

a laser source for emitting an ultra-short laser beam, less than 100 ps, onto said material to be machined;

a control unit for controlling the emission of said laser beam from said laser source;

a unit for analyzing the state of said material to be machined connected to said learning database;

said central unit for controlling said control unit;

said database;

g) machining said material with said laser source configured with said laser machining parameters determined by said learning machining function in step e);

h) acquiring machining results with said analysis unit after the step g) the machining result comprising the redeposition of material at the edges of the ablation and roughness;

i) transmitting said machining results and said machining parameters used in machining in the step g) to said central unit, said central unit being configured to communicate a machining data pair comprising machining results obtained according to the laser machining parameters used;

j) enriching said learning database with said machining data pair.