摘要
新一代信息技术飞速发展,加速与制造业融合,我国要想全面推进产业升级,必须提高我国制造业的自动化和智能化水平。伴随着产品和制造流程变得更加复杂,给制造商带来了“快速上市”和“资产优化”方面的挑战。制造工程团队既需要推出无缺陷的产品,又要同时满足低成本、高质量和投产目标一致的要求。为了应对这些挑战,行业领先的制造商需要利用企业知识和产品的三维模型及相关资源,以虚拟方式对制造流程进行事先验证。针对传统机器人装配工艺的缺陷与不足,利用Process Designer(PD)和Process simulate(PS)模拟工业机器人在实际环境中的动作,对装配路径及运动序列进行设计规划与仿真优化,通过可达性分析、干涉分析提高装配过程的可预见性,对出现的问题提出解决方案并改进,从而提高生产质量和效率,缩短生产周期,降低生产成本,通过仿真验证设计方案的可行性。
The rapid development of a new generation of information technology is accelerating its integration with the manufacturing industry.If my country wants to promote industrial upgrading in an all-round way,it must improve the automation and intelligence level of my country's manufacturing industry.As products and manufacturing processes have become more complex,it has brought challenges to manufacturers in terms of"quick time to market"and"asset optimization".The manufacturing engineering team not only needs to launch defect-free products,but also meet the requirements of low cost,high quality and consistent production goals.In order to cope with these challenges,industry-leading manufacturers need to use corporate knowledge and product 3D models and related resources to verify the manufacturing process in advance in a virtual way.Aiming at the defects and deficiencies of the traditional robot assembly process,Process Designer(PD)and Process simulate(PS)are used to simulate the actions of industrial robots in the actual environment,and the assembly path and motion sequence are designed,planned and simulated and optimized,and accessibility is analyzed.Interference analysis improves the predictability of the assembly process,proposes solutions and improvements to problems that arise,thereby improving production quality and efficiency,shortening the production cycle,reducing production costs,and verifying the feasibility of the design through simulation.
作者
马可
史朝顺
许蒙
MA Ke;SHI Chaoshun;XU Meng(College of Mechanical and Electrical Engineering,Hohai University,Changzhou 213022)
出处
《计算机与数字工程》
2023年第2期526-531,共6页
Computer & Digital Engineering
基金
国家自然科学基金项目(编号:51905150)
常州市科技计划项目(编号:CE20205044)资助。
