采用黑洞-连续蚁群算法的数控车床切削参数优化

赵义豪; 徐莉萍; 张朝阳; 李健; 何奎

doi:10.13433/j.cnki.1003-8728.20220132

采用黑洞-连续蚁群算法的数控车床切削参数优化

doi: 10.13433/j.cnki.1003-8728.20220132

河南科技大学机电工程学院, 河南洛阳 471003

基金项目:

国家重点研发计划项目 2018YFB1701205

详细信息

作者简介:
赵义豪(1996-), 硕士研究生, 研究方向为绿色制造, zyhhkd@163.com

通讯作者:
徐莉萍, 副教授, 硕士生导师, xlpzz@163.com

中图分类号: TH16
计量
- 文章访问数: 91
- HTML全文浏览量: 37
- PDF下载量: 15
- 被引次数: 0
出版历程
- 收稿日期: 2021-09-23
- 刊出日期: 2023-10-25

Optimization of Cutting Parameters of CNC Lathe Using Black Hole-continuous Ant Colony Algorithm

School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, He′nan, China

摘要

摘要: 为实现数控车床加工能效和加工质量的多目标优化，提出了一种对车削参数进行优化的黑洞-连续蚁群优化算法ACO_R(Black hole-continuous ant colony optimization algorithm)。首先，以最低切削比能和最小表面粗糙度为优化目标，建立了数控车床材料切削阶段的多目标优化模型；其次，引入黑洞算法对连续蚁群算法进行改进，构建了适用于多目标优化的黑洞-连续蚁群算法；最后，利用黑洞-连续蚁群算法对数控车床切削阶段的切削参数进行了寻优，并将优化结果与其它优化算法进行对比分析。分析结果表明，黑洞-连续蚁群算法不仅具有良好的全局搜索能力，寻优能力也较其它算法有所提升，能够为制造业提高生产能效和加工质量提供新的解决思路。
- 切削比能 /
- 表面粗糙度 /
- 多目标优化 /
- 黑洞-连续蚁群算法
Abstract: In order to realize the multi-objective optimization of the energy efficiency and machining quality of CNC lathes, a ACO_R optimization algorithm (Black hole-continuous ant colony) for optimizing the turning parameters is proposed. Firstly, with the minimum cutting specific energy and minimum surface roughness as the optimization goals, a multi-objective optimization model for the material cutting stage of the CNC lathe was established; secondly, the black hole algorithm was introduced to improve the continuous ant colony algorithm, and a black hole-continuous ant colony algorithm for multi-objective optimization was constructed; finally, the black hole-continuous ant colony algorithm is used to optimize the cutting parameters of the CNC lathe in the cutting stage, and the optimization results are compared and analyzed with that by using the other optimization algorithms. The analysis results show that the black hole-continuous ant colony algorithm not only has good global search capabilities, but also has improved optimization capabilities comparing with the other algorithms. It can provide new solutions for the manufacturing industry to improve the production energy efficiency and processing quality.
- specific energy of cutting /
- surface roughness /
- multi-objective optimization /
- black hole-continuous ant colony algorithm

HTML全文

图 1 切削比能的预测值和试验值对比

Figure 1. Comparison of predictive and experimental values of cutting specific energy

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图 2 解档案的构造

Figure 2. Construction of solution files

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图 3 黑洞-连续蚁群算法流程图

Figure 3. BH-ACO_R flow chart

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图 4 迭代50次时的搜索结果

Figure 4. Search results for 50 iterations

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图 5 迭代100次时的搜索结果

Figure 5. Search results for 100 iterations

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图 6 迭代200次时的搜索结果

Figure 6. Search results for 200 iterations

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图 7 BH-ACO_R与其它算法优化结果对比图

Figure 7. Comparison of optimization results between BH-ACO_R and other algorithms

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表 1 试验数据^[20]

Table 1. Test data

序号	v_c/(m·min^-1)	f/(mm·r^-1)	a_p/mm	P_c/W	C_MRR/(mm³·s^-1)	Q_SEC/(J·mm^-3)
1	100	0.05	0.2	731.83	16.67	43.90
2	100	0.10	0.3	854.15	50.00	17.14
3	100	0.15	0.4	996.15	100.00	10.02
4	100	0.20	0.5	1 216.55	166.67	7.35
5	150	0.05	0.3	879.12	37.50	23.50
6	150	0.10	0.2	883.08	50.00	17.57
7	150	0.15	0.5	1 222.98	187.50	6.57
8	150	0.20	0.4	1 244.43	200.00	6.22
9	200	0.05	0.4	998.09	66.67	15.06
10	200	0.10	0.5	1 200.16	166.67	7.26
11	200	0.15	0.2	991.34	100.00	9.84
12	200	0.20	0.3	1 212.01	200.00	6.02
13	250	0.05	0.5	1 133.20	104.17	10.99
14	250	0.10	0.4	1 246.14	166.67	7.48
15	250	0.15	0.3	1 252.12	187.50	6.64
16	250	0.20	0.2	1 184.28	166.67	7.02

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表 2 黑洞-连续蚁群算法参数设置

Table 2. BH-ACO_R parameter settings

参数	数值
迭代次数	200
初始种群数k	10
强化因子q	0.5
偏移距离比ζ	0.8

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表 3 不同权重下的优化结果

Table 3. Optimization results under different weights

c	v_c/(m·min^-1)	f/(mm·r^-1)	a_p/mm	Q_SEC/(J·mm^-3)	R_a/μm
0.1	250	0.050	0.22	20.16	0.42
0.2	250	0.050	0.27	17.61	0.45
0.3	249.98	0.050	0.44	11.96	0.57
0.4	249.97	0.057	0.47	9.89	0.66
0.5	249.94	0.067	0.48	8.47	0.73
0.6	249.99	0.089	0.49	6.79	0.89
0.7	250	0.096	0.50	6.27	1.13
0.8	250	0.136	0.49	4.68	1.21
0.9	250	0.198	0.50	3.40	1.64

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表 4 优化结果对比

Table 4. Comparison of optimization results

状态	v_c/(m·min^-1)	f/(mm·r^-1)	a_p/mm	Q_SEC/(J·mm^-3)	R_a/μm
优化前	200.00	0.150	0.20	9.91	1.19
优化后	249.94	0.067	0.48	8.47	0.73

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