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550t多功能专用抱钳式吊具结构设计与优化 被引量:3

Structural design and optimization of 550 t multi-functional special clamp sling
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摘要 针对大型铸锻件锻造过程中常常需要配备吊具、翻转机等工装辅助设备导致锻造工艺流程复杂、生产周期长、生产成本高等问题,设计了一种550 t多功能专用抱钳式吊具。根据锻件结构特征和锻造工艺流程,提出一种基于五连杆机构的吊具结构方案,可实现直径6 m、重550 t的铸锻件起吊、抱运、翻身、立料和立吊等功能;采用V形块结构确保立料过程中吊具与锻件表面始终保持线接触,避免锻件表面的损伤;建立吊具夹紧系数模型,采用多目标遗传算法对吊具杆长进行优化,确保吊具工作的可靠性。实践表明,吊具在起吊范围内夹紧系数始终大于1,其宽度、高度均满足设计要求,该吊具可配合1.65万吨自由锻造油压机实现大型铸锻件的镦粗、拔长等复杂工艺。 In the forging process of large castings and forgings,it is often necessary to equip with lifting tools,turning machines and other auxiliary equipment,resulting in complex forging process,long production cycle and high production cost.In this paper,a 550 t multi-functional special clamp sling is designed.According to the structural characteristics of forgings and forging process,a structure scheme based on five-bar mechanism is proposed,which can realize transporting,turning over,erecting and vertical slinging of forgings with diameter of 6m and weight of 550 t.The V-shaped structure is adopted to ensure that the hanger keeps linear contact with the surface of forging,so as to avoid the damage of the forging surface.In order to ensure the reliability,the clamping coefficient model was established and the multi-objective genetic algorithm was used to optimize the length of rod.The results show that the clamping coefficient of the clamp sling is always greater than 1 in the lifting range,and its width and height meet the design requirements.The special clamp sling can cooperate with the world's largest 165000 tons forging press to achieve upsetting and drawing of large forgings.
作者 刘加亮 王朝华 高志 吴凤和 LIU Jialiang;WANG Zhaohua;GAO Zhi;WU Fenghe(Yanshan University,Qinhuangdao 066004,CHN;Shanghai Dianji University,Shanghai 200240,CHN;Beijing Sany Intelligent Technology Co.,Ltd.,Kunshan 215300,CHN)
出处 《制造技术与机床》 北大核心 2020年第1期52-55,共4页 Manufacturing Technology & Machine Tool
基金 高档数控机床与基础制造装备国家科技重大专项课题(2018ZX04007002) 河北省自然科学基金智能制造专项项目(E2017203372)
关键词 吊具 铸锻件 结构设计 遗传算法 优化 clamp sling casting and forging structural design genetic algorithm optimization
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