US 12,403,540 B2
Welding bead modeling method for wire-arc additive manufacturing, device therefor and system therefor
Hai'ou Zhang, Hubei (CN); Guilan Wang, Hubei (CN); Shangyong Tang, Hubei (CN); Siyu Zhou, Hubei (CN); and Runsheng Li, Hubei (CN)
Assigned to HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY, Hubei (CN)
Appl. No. 17/310,905
Filed by HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY, Hubei (CN)
PCT Filed Dec. 23, 2020, PCT No. PCT/CN2020/138656
§ 371(c)(1), (2) Date Aug. 30, 2021,
PCT Pub. No. WO2021/129671, PCT Pub. Date Jul. 1, 2021.
Claims priority of application No. 201911347776.X (CN), filed on Dec. 24, 2019.
Prior Publication US 2022/0161344 A1, May 26, 2022
Int. Cl. B23K 9/00 (2006.01); B23K 9/04 (2006.01); B23K 9/095 (2006.01); B33Y 30/00 (2015.01)
CPC B23K 9/04 (2013.01) [B23K 9/0953 (2013.01); B33Y 30/00 (2014.12)] 20 Claims
OG exemplary drawing
 
1. A welding bead modeling method for wire-arc additive manufacturing, wherein the method comprises an off-line training stage and an on-line modeling stage;
the off-line training stage comprises the steps of:
step 1:
using a dynamic parameter method, and using different welding process parameters in the same welding bead in the wire-arc additive manufacturing process to obtain a welding bead with synchronous and dynamic changes in profile along with the dynamic changes of the welding process parameters; wherein the welding process parameters comprise welding bead temperature, welding current, welding voltage, wire feeding speed and travelling speed; in the welding process, acquiring the temperature of the welding bead in real time by an infrared thermal imager;
using a line laser sensor for scanning to obtain the segmented profile of the welding bead obtained by the dynamic parameter method, and corresponding each welding bead profile to the welding process parameters one by one as training data; wherein the welding bead profile comprises a welding bead height and a welding bead width;
step 2: conducting model training
using welding process parameters as the input of neural network, using the welding bead profile corresponding to the input welding process parameters as the output of neural network, and training the neural network, so as to obtain a welding bead modeling model capable of obtaining the corresponding welding bead profile according to the input welding process parameters;
the on-line modeling stage comprises the steps of:
step 3, acquiring welding process parameters in real time in the wire-arc additive manufacturing process, and inputting the acquired welding process parameters into the welding bead modeling model obtained by off-line training in step 2 to obtain the welding bead profile.