孔群数控加工切削参数及工艺路径优化的研究
发布时间：2019-06-29 18:51
【摘要】：孔群零件在机械零件中占有很大的比例,例如箱体零件和底板零件都包含各种类型的孔(螺纹孔、沉头孔、通孔等)。合适的切削参数和加工序列是提高孔群加工效率的一个重要因素,特别当孔的数量巨大时,优化孔群的加工路径可以很大程度减少加工时间。实际加工中,由于各种因素的影响,使用传统分析方法,工艺人员获得最优加工方案非常不易。因此,利用CAPP技术,优化孔群的加工工艺具有很重要的意义。本文对箱体类零件的孔群切削参数和工艺路径进行了较为系统的研究,其主要内容如下:(1)研究了孔群加工工艺的特点,总结了目前孔群加工工艺常用的优化方法。(2)介绍了遗传算法的基础原理,并指出其缺点。利用模拟退火算法的原理,改进了遗传算法,对新算法的性能进行了测试,证明了它的可靠性。(3)建立了孔加工切削参数优化模型,设计了孔的加工工时和加工成本两个目标函数。并且,基于改进遗传算法对零件的切削参数优化模型进行了求解。(4)建立了孔群加工成本优化模型,目标函数为空程走刀成本和更换刀具成本。然后,分别使用遗传算法和改进遗传算法对优化模型进行了求解。结果表明改进遗传算法求解性能稳定,更加适应实际加工的要求。
[Abstract]:Hole group parts account for a large proportion of mechanical parts, such as box parts and floor parts contain various types of holes (threaded holes, countersunk holes, through holes, etc.). Appropriate cutting parameters and machining sequences are an important factor to improve the machining efficiency of hole groups, especially when the number of holes is large, optimizing the machining path of hole groups can greatly reduce the machining time. In practical processing, due to the influence of various factors, it is very difficult for craftsmen to obtain the optimal processing scheme by using the traditional analysis method. Therefore, it is of great significance to optimize the processing technology of hole group by using CAPP technology. In this paper, the cutting parameters and process paths of box parts are studied systematically. The main contents are as follows: (1) the characteristics of hole group machining technology are studied, and the common optimization methods of hole group machining technology are summarized. (2) the basic principle of genetic algorithm is introduced and its shortcomings are pointed out. Based on the principle of simulated annealing algorithm, the genetic algorithm is improved, and the performance of the new algorithm is tested, and its reliability is proved. (3) the optimization model of hole cutting parameters is established, and two objective functions of machining man-hour and machining cost are designed. Moreover, the optimization model of cutting parameters is solved based on the improved genetic algorithm. (4) the optimization model of machining cost of hole group is established, and the objective function is empty tool walking cost and tool replacement cost. Then, genetic algorithm and improved genetic algorithm are used to solve the optimization model. The results show that the performance of the improved genetic algorithm is stable and more suitable for practical machining.
【学位授予单位】：西安建筑科技大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TG659;TP18
，
本文编号：2508031
[Abstract]:Hole group parts account for a large proportion of mechanical parts, such as box parts and floor parts contain various types of holes (threaded holes, countersunk holes, through holes, etc.). Appropriate cutting parameters and machining sequences are an important factor to improve the machining efficiency of hole groups, especially when the number of holes is large, optimizing the machining path of hole groups can greatly reduce the machining time. In practical processing, due to the influence of various factors, it is very difficult for craftsmen to obtain the optimal processing scheme by using the traditional analysis method. Therefore, it is of great significance to optimize the processing technology of hole group by using CAPP technology. In this paper, the cutting parameters and process paths of box parts are studied systematically. The main contents are as follows: (1) the characteristics of hole group machining technology are studied, and the common optimization methods of hole group machining technology are summarized. (2) the basic principle of genetic algorithm is introduced and its shortcomings are pointed out. Based on the principle of simulated annealing algorithm, the genetic algorithm is improved, and the performance of the new algorithm is tested, and its reliability is proved. (3) the optimization model of hole cutting parameters is established, and two objective functions of machining man-hour and machining cost are designed. Moreover, the optimization model of cutting parameters is solved based on the improved genetic algorithm. (4) the optimization model of machining cost of hole group is established, and the objective function is empty tool walking cost and tool replacement cost. Then, genetic algorithm and improved genetic algorithm are used to solve the optimization model. The results show that the performance of the improved genetic algorithm is stable and more suitable for practical machining.
【学位授予单位】：西安建筑科技大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TG659;TP18
，
本文编号：2508031
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