Tool path planning is a crucial factor of computer-aided design and manufacturing (CAD/CAM). To generate suitable tool paths, the previous methods often transform the problem into local or global optimization methods to solve it, which leads to a long computational time. To achieve real-time planning, we try to propose an efficient neural network-based direct tool path generating method on B-spline surface. An intelligent neural network reparameterizes the workpiece surface based on the scallop height constraint.
报告人简介:申立勇为中国科学院大学数学科学学院教授,密码学院副院长,中科院大数据与知识发现重点实验室成员,辽宁省智能化数控工程技术研究中心特聘专家。研究兴趣包括计算几何,计算机辅助设计,数字化设计与数控技术,数据处理等。在国内外专业核心期刊TOG、CAD、CAGD、JSSC及国际学术会议上发表学术论文60余篇;先后主持国家重点研发计划课题,国防专项课题,北京市重点专项,国家自然科学基金青年项目和面上项目,国家发改委综合司课题,国家安监总局科技项目等。