A 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., to achieve the effects of improving formulation efficiency, superior generalization performance, and high prediction accuracy
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[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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