基于遗传算法和神经网络的3D增材印花工艺参数优化

Optimization of process parameters for 3D additive screen printing based on genetic algorithm and neural network

针对3D增材印花工艺中刮刀压力、刮印速度、刮刀角度和油墨黏度等参数的组合对印花质量存在较大影响,但实际生产中各工艺参数组合无法实现最优这一问题,利用附加动量法改进下的BP神经网络构建3D增材印花工艺模型,通过实验参数对模型进行训练,确定工艺参数和印花质量间的非线性关系。利用遗传算法对该非线性函数进行极值寻优,从而得到3D增材印花工艺的最优参数组合:印花压力为4800N,刮印角度为18°,刮印速度为400 mm/s,油墨黏度为170.5 Pa·s,该模型预测误差基本稳定在0.01范围之内。利用优化前后的参数进行对比实验,结果证明该算法可以实现3D增材印花工艺的质量预测和参数寻优,从而提升印花质量,缩短产品开发时间。

Aiming at the problem that the combination of parameters such as blade pressure,squeegee speed,blade angle and ink viscosity in the 3D additive printing process has a great influence on the printing quality,but the combination of various process parameters in actual production cannot be optimal,the BP neural network improved by the additional momentum method was adopted to construct a 3D additive printing process model.The model was trained by the experimental parameters to determine the nonlinear relationship between process parameters and printing quality.The genetic algorithm was adopted to optimize the nonlinear function to achieve the optimal parameter combination of the 3D additive printing process:printing pressure of 4800 N,squeezing angle of 18 degrees,squeezing speed of 400 mm/s and ink viscosity of 170.5 Pa·s,The model prediction error is basically stable within the range of 0.01.The comparison experiments were carried out using the parameters before and after optimization.The experimental results show that the algorithm can realize the quality prediction and parameter optimization of 3D additive printing process,thereby improving the printing quality and shortening the product development time.