Laser welding process parameter optimization method based on Bagging integrated prediction model and particle swarm optimization algorithm

A technology of process parameter optimization and particle swarm optimization, applied in laser welding equipment, welding equipment, manufacturing tools, etc.

Active Publication Date: 2019-11-05
SHANDONG UNIV
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Problems solved by technology

However, because different machine learning models perform differently in different situations, for a specific data set, the existing welding process parameter prediction method using a single prediction model may not be able to achieve the optimal prediction effect

Method used

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  • Laser welding process parameter optimization method based on Bagging integrated prediction model and particle swarm optimization algorithm
  • Laser welding process parameter optimization method based on Bagging integrated prediction model and particle swarm optimization algorithm
  • Laser welding process parameter optimization method based on Bagging integrated prediction model and particle swarm optimization algorithm

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Embodiment 1

[0051] A Bagging algorithm to integrate multiple base learners into a new prediction model, including the following steps:

[0052] Determine the optimization target of laser welding process parameters. Laser welding process parameters include laser power (LaserPower, LP), welding speed (Welding Speed, WP), defocusing amount (Defocusing Amount, DA), laser pulse width (Laser Pulse Width, LPW) ; Welding quality evaluation parameters include weld depth-width ratio, weld tensile strength, weld reinforcement; where, weld depth-width ratio is DW=DP / BW, DP is weld pool depth, BW is weld fusion Pool width, weld tensile strength is TS=F max / S,F max is the maximum tensile stress of the weld, S is the effective cross-sectional area of ​​the weld, and H is the weld reinforcement. The optimization goal is to select reasonable laser welding process parameters (laser power, welding speed, defocus amount, and laser pulse width) to perform the welding task to obtain the largest weld depth-w...

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Abstract

The invention relates to a laser welding process parameter optimization method based on a Bagging integrated prediction model and a particle swarm optimization algorithm. The optimization consists ofa laser welding process parameter prediction model and a multi-target particle swarm optimization method, the prediction model is obtained by fusing a plurality of basic learners through a Bagging model fusion algorithm, and the prediction model establishes a nonlinear mapping relationship between the laser welding process parameters and weld quality evaluation parameters. The optimized laser welding process parameters are finally obtained through the multi-target particle swarm optimization algorithm, the prediction accuracy is higher, guidance can be better provided for the formulation of the laser welding process parameters, and the formulation efficiency of the process is improved.

Description

technical field [0001] The invention relates to a method for integrating a plurality of base learners into a new prediction model based on a Bagging algorithm, and optimizing laser welding process parameters by combining the new prediction model with a multi-objective particle swarm optimization algorithm. The invention is applicable to the optimization of robot laser welding process parameters and belongs to the technical field of robot welding. Background technique [0002] The existing laser welding process parameter optimization method includes two parts: laser welding process parameter prediction and parameter optimization. The laser welding parameter prediction model is a method to establish the nonlinear mapping relationship between the laser welding process parameters and the welding quality evaluation parameters, and its prediction accuracy directly affects the optimization effect of the laser welding process parameters. Commonly used welding parameter prediction m...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): B23K26/21B23K26/70
CPCB23K26/21B23K26/702
Inventor 胡天亮李政誉张承瑞沈卫东伍杰
Owner SHANDONG UNIV
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