基于脑模型联接控制器的SLS工艺参数优化方法研究

Research on the Optimization of SLS Technique Parameters Based on CMAC

目前 ,在SLS成形的过程中 ,成形工艺参数的制定没有一个系统的理论体系和科学方法 ,基本上是凭人的经验来确定的 ,当成形设备、烧结材料和成形件的几何形状发生变化时 ,成形工艺参数又得重新通过大量的试验确定。因此 ,工作量大 ,烧结材料浪费严重 ,导致效率降低和成本增高。本文针对这种现状 ,提出了通过脑模型联接控制器即CMAC神经网络对SLS工艺参数进行优化 ,并在此基础上开发了SLS工艺参数优化系统 ,通过它在线自动给出优化参数 ,这样就大大降低了对操作人员的技术要求并减少了因重复试验而带来的浪费。

Presently, the choice of SLS(Selective Laser Sintering) technique parameters mainly depends on the experts′ experience. When the SLS machine, the sintering material and the shape of parts are changed, SLS technique parameters must be chosen again through many experiments, which will waste sintering material seriously, reduce efficiency and increase cost. To solve this problem, we propose optimizing SLS technique parameters through a neural network——CMAC. We developed a system for the optimization of SLS technique parameters based on CMAC(Cerebellar Model Articulation Controller). With this system the optimized parameters can be shown on-line automatically, which will reduce the requirement for the operators and waste from repeated experiments.