合金钢40CrNiMoA高速外圆磨削比能预测研究

刘伟; 石新宇; 李希晨; 唐都波; 刘顺

doi:10.13433/j.cnki.1003-8728.20200303

合金钢40CrNiMoA高速外圆磨削比能预测研究

doi: 10.13433/j.cnki.1003-8728.20200303

湖南科技大学难加工材料高效精密加工湖南省重点实验室, 湖南湘潭 411201

基金项目:

湖南省自然科学基金项目 2020JJ5178

湖南省教育厅科学研究一般项目 15C0544

湖南省电磁装备设计与制造重点实验室开放基金项目 DC201901

详细信息

作者简介:
刘伟(1986-), 副教授，硕士生导师，博士, 研究方向为高效精密智能磨削、磨抛机器人, lw1986tiger@163.com

中图分类号: TG580
计量
- 文章访问数: 118
- HTML全文浏览量: 48
- PDF下载量: 20
- 被引次数: 0
出版历程
- 收稿日期: 2020-06-28
- 刊出日期: 2022-01-01

Prediction of Specific Energy in High Speed Cylindrical Grinding of 40CrNiMoA

Hunan Provincial Key Laboratory of High Efficiency and Precision Machining of Difficult-to-cut Material, Hunan University of Science and Technology, Xiangtan 411201, Hu′nan, China

摘要

摘要: 采用正交试验法研究干磨和湿磨方式下砂轮线速度、工件转速、磨削深度对合金钢40CrNiMoA高速外圆磨削比能的影响，并建立基于BP神经网络的磨削比能预测模型，最后对预测结果进行验证。结果表明：合金钢40CrNiMoA高速外圆磨削比能随砂轮线速度增大呈现先增加后减小的趋势，随工件转速、磨削深度的增大而减小，工件转速对磨削比能的影响程度最大；湿磨方式的磨削比能比干磨方式的小。在试验工艺参数范围内，当砂轮线速度为60 m/s、工件转速为125 r/min、磨削深度为40 μm时，磨削比能最小；预测模型的预测值与试验结果的绝对误差小于10%，表明BP神经网络预测模型是有效的。
- 40CrNiMoA /
- 高速外圆磨削 /
- 正交试验 /
- 比能 /
- 预测
Abstract: According to the orthogonal experiment in the high speed cylindrical grinding under dry and wet modes, the influence of the grinding wheel linear speed, workpiece rotate speed and grinding depth on the specific energy of 40CrNiMoA was studied. The model for predicting the specific energy of grinding was established based on the BP neural network, and the prediction results were validated. The results show that the specific energy of grinding increases first and then decreases with the increasing of grinding wheel linear speed, and it decreases with the increasing of workpiece rotate speed and the grinding depth. The workpiece rotate speed has the greatest influence on the specific energy of grinding. The specific energy of grinding in wet mode is smaller than that in dry mode. For the range of process parameters in the experiment, when the grinding wheel linear speed, the workpiece rotate speed and the grinding depth are 60 m/s, 125 r/min and 40 μm, separately, the specific energy of grinding is the smallest. The absolute errors between the predicted value and the experimental are less than 10%, which shows that the prediction model based on the BP neural network is effective.
- 40CrNiMoA /
- high speed cylindrical grinding /
- orthogonal experiment /
- specific energy /
- prediction

HTML全文

图 1 机床加工过程功率图

下载: 全尺寸图片幻灯片

图 2 磨粒未变形切屑厚度示意图

下载: 全尺寸图片幻灯片

图 3 40CrNiMoA工件

下载: 全尺寸图片幻灯片

图 4 高速外圆磨削试验系统

下载: 全尺寸图片幻灯片

图 5 工艺参数对磨削

下载: 全尺寸图片幻灯片

图 6 磨削比能BP神经网络预测模型结构图

下载: 全尺寸图片幻灯片

表 1 正交试验因素水平表

因素	参数/单位	水平
因素	参数/单位	1	2	3	4
A	v_s/(m·s^-1)	60	90	120	150
B	v_w/(r·min^-1)	50	75	100	125
C	a_p/μm	10	20	30	40

下载: 导出CSV

表 2 正交试验方案

编号	因素
编号	A	B	C
1	1	1	1
2	1	2	2
3	1	3	3
4	1	4	4
5	2	1	2
6	2	2	1
7	2	3	4
8	2	4	3
9	3	1	3
10	3	2	4
11	3	3	1
12	3	4	2
13	4	1	4
14	4	2	3
15	4	3	2
16	4	4	1

下载: 导出CSV

表 3 高速外圆磨削正交试验结果

编号	干磨			湿磨
编号	P_E/W	P_T/W	S_EG/(J·mm^-3)	P_E/W	P_T/W	S_EG/(J·mm^-3)
1	5 462.423	6 014.520	15.417	8 098.346	8 412.895	8.784
2	5 530.664	6 711.263	10.989	7961.715	8 536.396	5.349
3	5 714.640	7 286.054	7.314	8 112.038	8 893.785	3.638
4	5 614.028	7 515.663	5.310	10 367.266	11 541.759	3.280
5	5 750.739	6 882.819	15.807	7 985.829	8 720.928	10.264
6	5 665.352	6 558.859	16.634	8 439.756	9 001.265	10.453
7	5 609.416	7 826.232	7.738	9 340.867	10 628.412	4.494
8	6 212.147	7 890.667	6.250	8 284.150	9 264.854	3.651
9	6 074.769	8 044.098	18.331	8 667.361	9 993.287	12.342
10	6 020.220	8 438.991	11.257	9 554.067	11 401.879	8.600
11	5 778.356	6 867.941	15.213	9 209.878	9 939.591	10.189
12	5 705.449	6 934.444	6.864	8 740.058	9 712.387	5.430
13	6 945.482	9 064.753	14.795	9 582.218	11 115.396	10.703
14	6 845.512	8 455.027	9.988	10 012.609	11 259.656	7.739
15	6 729.604	8 232.459	10.492	9 689.972	10 747.351	7.382
16	6 040.234	7 088.095	11.705	9 880.640	10 657.807	8.681

下载: 导出CSV

表 4 干磨正交试验极差分析

因素	k₁	k₂	k₃	k₄	R
A	9.758	11.607	12.916	11.745	3.159
B	16.088	12.217	10.189	7.532	8.555
C	14.742	11.038	10.471	9.775	4.967
主次顺序	v_w>a_p>v_s

下载: 导出CSV

表 5 湿磨正交试验极差分析

因素	k₁	k₂	k₃	k₄	R
A	5.263	7.216	9.145	8.626	3.878
B	10.523	8.035	6.426	5.261	5.263
C	9.527	7.106	6.843	6.769	2.758
主次顺序	v_w>v_s>a_p

下载: 导出CSV

表 6 traincgf函数的相关训练参数

训练参数	取值
最大训练次数	5 000
学习率	0.000 1
显示训练迭代过程	25
训练要求精度	0.000 01

下载: 导出CSV

表 7 干磨磨削比能预测值与试验值对比

编号	因素			预测值/(J·mm^-3)	试验值/(J·mm^-3)	绝对误差/%
编号	A	B	C	预测值/(J·mm^-3)	试验值/(J·mm^-3)	绝对误差/%
1	150	50	10	24.896	22.738	9.491
2	120	50	10	24.509	22.841	7.303
3	60	125	10	13.171	12.612	4.432

下载: 导出CSV

表 8 湿磨磨削比能预测值与试验值对比

编号	因素			预测值/(J·mm^-3)	试验值/(J·mm^-3)	绝对误差/%
编号	A	B	C	预测值/(J·mm^-3)	试验值/(J·mm^-3)	绝对误差/%
1	150	50	10	14.679	14.341	2.357
2	120	50	10	13.355	14.125	5.451
3	60	125	10	6.342	6.572	3.500

下载: 导出CSV

参考文献(18)

[1]	何加群. 不忘建设世界轴承强国的初心——《全国轴承行业"十三五"发展规划》解读[J]. 轴承, 2017(5): 54-66 https://www.cnki.com.cn/Article/CJFDTOTAL-CUCW201705016.htm HE J Q. Do not forget the original intention to construct world bearing power-interpretation of '13th five-year plan of China bearing industry'[J]. Bearing, 2017(5): 54-66 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CUCW201705016.htm
[2]	葛泉江, 张兴洪, 马欣新, 等. M50高温轴承钢表面TiN涂层与镀银层对磨的摩擦特性[J]. 航空制造技术, 2017(23-24): 54-58 https://www.cnki.com.cn/Article/CJFDTOTAL-HKGJ2017Z2012.htm GE Q J, ZHANG X H, MA X X, et al. Tribological properties of TiN coated M50 steel for high temperature bearing against silver coatings[J]. Aeronautical Manufacturing Technology, 2017(23-24): 54-58 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HKGJ2017Z2012.htm
[3]	孙立才, 师歌, 李凌鑫, 等. 航空轴承钢制保持架的加工工艺[J]. 轴承, 2016(3): 23-25 doi: 10.3969/j.issn.1000-3762.2016.03.007 SUN L C, SHI G, LI L X, et al. Machining technology for steel cages of aviation bearings[J]. Bearing, 2016(3): 23-25 (in Chinese) doi: 10.3969/j.issn.1000-3762.2016.03.007
[4]	尹晖, 邓朝晖, 张华, 等. 典型机床关键零部件切削/磨削比能数据库系统研发[J]. 金刚石与磨料磨具工程, 2017, 37(4): 73-78, 85 https://www.cnki.com.cn/Article/CJFDTOTAL-JGSM201704016.htm YIN H, DENG Z H, ZHANG H, et al. Research and development of cutting or grinding specific energy database system for key components of typical machine tools[J]. Diamond & Abrasives Engineering, 2017, 37(4): 73-78, 85 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JGSM201704016.htm
[5]	严灿. 典型机床加工能耗分析与切削磨削比能预测研究[D]. 湘潭: 湖南科技大学, 2016 YAN C. Research on the machining energy analysis and prediction of cutting/grinding specific energy consumption of the typical CNC machine tool[D]. Xiangtan: Hunan University of Science and Technology, 2016 (in Chinese)
[6]	周振新. 难加工材料的高速外圆磨削仿真与工程实验研究[D]. 上海: 上东华大学, 2010 ZHOU Z X. Simulational and experimental research on high speed external cylindrical grinding of difficult-to-cut materials[D]. Shanghai: Donghua University, 2010 (in Chinese)
[7]	张强, 庞静珠, 李蓓智, 等. 外圆磨削力测试方法及高速磨削特性研究[J]. 组合机床与自动化加工技术, 2012(9): 9-12 doi: 10.3969/j.issn.1001-2265.2012.09.003 ZHANG Q, PANG J Z, LI B Z, et al. Study on test methods of external cylindrical grinding force and high-speed grinding characteristics[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2012(9): 9-12 (in Chinese) doi: 10.3969/j.issn.1001-2265.2012.09.003
[8]	詹友基, 田笑, 许永超, 等. 基于超细硬质合金磨削过程的机床能耗研究[J]. 金刚石与磨料磨具工程, 2020, 40(2): 61-66 https://www.cnki.com.cn/Article/CJFDTOTAL-JGSM202002012.htm ZHAN Y J, TIAN X, XU Y C, et al. Analysis on machine tool energy consumption based on ultra-fine cemented carbide grinding process[J]. Diamond & Abrasives Engineering, 2020, 40(2): 61-66 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JGSM202002012.htm
[9]	ZHAN Y J, TIAN X, XU Y C, et al. Study on the specific grinding energy of cemented carbide (YG8) grinding with a vitrified diamond wheel in high speed regime[J]. International Journal of Abrasive Technology, 2019, 9(4): 286-302 doi: 10.1504/IJAT.2019.106675
[10]	MONICI R D, BIANCHI E C, CATAI R E, et al. Analysis of the different forms of application and types of cutting fluid used in plunge cylindrical grinding using conventional and superabrasive CBN grinding wheels[J]. International Journal of Machine Tools and Manufacture, 2006, 46(2): 122-131 doi: 10.1016/j.ijmachtools.2005.05.009
[11]	ZAHEDI A, TAWAKOLI T, AKBARI J. Energy aspects and workpiece surface characteristics in ultrasonic-assisted cylindrical grinding of alumina-zirconia ceramics[J]. International Journal of Machine Tools and Manufacture, 2015, 90: 16-28 doi: 10.1016/j.ijmachtools.2014.12.002
[12]	李涛, 孔露露, 张洪潮, 等. 典型切削机床能耗模型的研究现状及发展趋势[J]. 机械工程学报, 2014, 50(7): 102-111 https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201407014.htm LI T, KONG L L, ZHANG H C, et al. Recent research and development of typical cutting machine tool's energy consumption model[J]. Journal of Mechanical Engineering, 2014, 50(7): 102-111 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201407014.htm
[13]	任敬心, 华定安. 磨削原理[M]. 北京: 北京电子工业出版社, 2011 REN J X, HUA D A. Grinding principle[M]. Beijing: Beijing Electronic Industry Press, 2011 (in Chinese)
[14]	刘伟, 商圆圆, 邓朝晖, 等. 基于响应曲面法的轴承钢GCr15高速外圆磨削参数优化[J]. 中国机械工程, 2019, 30(23): 2829-2834 doi: 10.3969/j.issn.1004-132X.2019.23.008 LIU W, SHANG Y Y, DENG Z H, et al. Parameter optimization of high speed cylindrical grinding for bearing steel GCr15 based on response surface method[J]. China Mechanical Engineering, 2019, 30(23): 2829-2834 (in Chinese) doi: 10.3969/j.issn.1004-132X.2019.23.008
[15]	赵恒华, 孙顺利, 高兴军, 等. 超高速磨削的比磨削能研究[J]. 中国机械工程, 2006, 17(5): 453-456 doi: 10.3321/j.issn:1004-132X.2006.05.004 ZHAO H H, SUN S L, GAO X J, et al. Study on specific grinding energy of ultra high speed grinding[J]. China Mechanical Engineering, 2006, 17(5): 453-456 (in Chinese) doi: 10.3321/j.issn:1004-132X.2006.05.004
[16]	宫运启. 基于神经网络的机械加工工序能耗预测[J]. 计算机工程与应用, 2012, 48(21): 235-239 https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG201221053.htm GONG Y Q. Energy consumption prediction in machining procedure based on neural network[J]. Computer Engineering and Applications, 2012, 48(21): 235-239 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG201221053.htm
[17]	陈雯柏. 人工神经网络原理与实践[M]. 西安: 西安电子科技大学出版社, 2016 CHEN W B. Principle and practice of artificial neural network[M]. Xi'an: Xidian University Press, 2016 (in Chinese)
[18]	仝卫国, 李敏霞, 张一可. 深度学习优化算法研究[J]. 计算机科学, 2018, 45(S2): 155-159 https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA2018S2030.htm TONG W G, LI M X, ZHANG Y K. Research on optimization algorithm of deep learning[J]. Computer Science, 2018, 45(S2): 155-159 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA2018S2030.htm