摘要
针对3D打印批次排样是一个建立在多约束条件下的复杂优化问题,本文提出一种基于Hopfield人工神经网络的3D打印批次排样方法。通过对3D打印批次排样目标的设定,建立了Hopfield人工神经网络的能量函数。通过神经网络优化计算,实现了将3D打印工件在三维空间的排布问题转化为二维图形的输出。该算法实用性强,提高了工件的加工效率。
The optimal layout of 3D printing is a complicated problem under the control of multi-circumstances.Aiming at solving this problem,this article proposed a new kind of algorithm based on the artificial neural network.The paper established energy function of neural network by setting the target of 3D printing layout.This paper optimized calculation by using the neural network,and the output of the 3D printed workpiece in the three-dimensional space is converted into the output of the two-dimensional graphics.The algorithm is practical and improves the processing efficiency of the workpiece.
作者
李方娟
赵玉佳
赵君嫦
孟祥丽
郭强
孟繁钦
LI Fang-juan;ZHAO Yu-jia;ZHAO Jun-chang;MENG Xiang-li;GUO Qiang;MENG Fan-qin(Department of Pharmacy,Mudanjiang Medical University,Mudanjiang 157011,China;Hongqi hospital of Mudanjiang Medical University,Mudanjiang 157011,China)
出处
《软件》
2020年第4期35-37,共3页
Software
基金
牡丹江市科学技术计划项目(Z2018g021)。
作者简介
李方娟(1978–),女,副教授,主要研究方向:制造技术与人工智能;赵玉佳(1970–),男,副教授,主要研究方向:制药工程与人工智能;赵君嫦(1977–),女,副教授,主要研究方向:医学与人工智能。
