基于响应面法激光切削柞木效果

doi:10.11707/j.1001-7488.LYKX20240461

摘要/Abstract

摘要：

目的: 选取低、中、高不同程度含水率柞木，结合激光机技术参数（镜头高、进给速度、光强），以缝深和缝宽作为切削效果，引入响应面法建模分析激光切削柞木时不同含水率和激光机技术参数对切削效果的影响，为激光加工拼花地板和木质工艺品等生产实践提供技术指导。方法: 将含水率作为影响因素A，与激光机镜头高B、进给速度C、光强D组合，采用方差分析等方法确定切削效果的影响因素以及影响因素间交互作用的显著性。结果: 对缝深的影响：单因素显著性B≈C>A>D，B和C的影响极显著，A接近于显著；交互项BC的影响显著；A的二次项曲面效应影响极显著，C的曲面效应影响高度显著。滤去不显著影响项，基于响应面法得出关于缝深的回归方程为：Y=522.14A²+288.32C²+235.9BC?359.23B? 662.96C+652.69。对缝宽的影响：单因素显著性B>D>A>C，B的影响极显著；二因素间交互项的影响均不显著；A和B的二次项曲面效应影响极显著。滤去不显著影响项，基于响应面法得出关于缝宽的回归方程为：Y=202.22A²+179.51B²+ 187.62B+237.22。结论: 1）含水率的二次项影响均极显著，说明含水率对切削效果的影响是非线性的，在一定范围内显著，主要是含水率较低时影响很小造成的。2）镜头高对缝深和缝宽的影响均极显著，说明生产中首要确定的是镜头高的合理参数。如果以缝深作为主要达到的切削效果，先选择合理的进给速度，再选择合理的光强；如果以缝宽作为主要达到的切削效果，先选择合理的光强，再选择合理的进给速度。3）在含水率约27.63%，欲获得缝深2 500～2 700 μm，最佳参数范围为：镜头高约5.7 mm、进给速度约42 mm·s^?1、光强约62%。

关键词: 激光, 柞木, 响应面法, 显著性, 交互作用

Abstract:

Objective: This study selected oak wood with low, medium, and high moisture content levels, combined with laser machine technical parameters (lens height, feed speed, light intensity), to analyze the effects of moisture content and laser parameters on slot depth and slot width as cutting outcomes. Response surface methodology (RSM) was introduced to model and guide laser processing for marquetry flooring and wooden crafts. Method: Moisture content (factor A) was evaluated alongside laser parameters (lens height B, feed speed C, light intensity D). Variance analysis and other methods were used to determine the significance of individual factors and their interactions. Result: For slot depth: single-factor significance followed B≈C>A>D, with B and C being highly significant, and A being nearly significant. The interaction term BC was significant, while quadratic terms for A (nonlinear effect) were highly significant and for C notably significant. The simplified RSM regression equation for slot depth was: Y = 522.14A² + 288.32C² + 235.9BC ? 359.23B ? 662.96C + 652.69. For slot width: single-factor significance followed B>D>A>C, with B being highly significant. No significant interactions were observed, but quadratic terms for A and B showed highly significant nonlinear effects. The simplified regression equation for slot width was: Y= 202.22A² + 179.51B² + 187.62B + 237.22. Conclusion: 1) The quadratic effects of moisture content were highly significant, indicating nonlinear impacts on cutting outcomes, particularly pronounced within specific ranges due to minimal influence at low moisture levels. 2) Lens height (B) critically affected both slot depth and width, emphasizing its prioritization in production parameter optimization. For maximizing slot depth, feed speed (C) should be optimized before light intensity (D); for slot width, light intensity (D) takes precedence. 3) With a water content of approximately 27.63%, to achieve a seam depth of 2 500 to 2 700 μm, the optimal parameter range is as follows: the lens height is approximately 5.7 mm, the feed rate is approximately 42 mm·s^?1, and the light intensity is approximately 62%.

Key words: laser, oak, response surface methodology, significance, interaction

中图分类号:

S784

赵洪刚,孙玮鸿,梁鹏鹏,赵梓旭,孙建平,乐磊. 基于响应面法激光切削柞木效果[J]. 林业科学, 2025, 61(8): 172-179.

Honggang Zhao,Weihong Sun,Pengpeng Liang,Zixu Zhao,Jianping Sun,Lei Le. Effect of Laser Cutting of Oak Wood Based on Response Surface Methodology[J]. Scientia Silvae Sinicae, 2025, 61(8): 172-179.

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参考文献 0

	陈日强, 李长春, 杨贵军, 等. 无人机机载激光雷达提取果树单木树冠信息. 农业工程学报, 2020, 36 (22): 50- 59. doi: 10.11975/j.issn.1002-6819.2020.22.006
	Chen R Q, Li C C, Yang G J, et al. Extraction of crown information from individual fruit tree by UAV LiDAR. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36 (22): 50- 59. doi: 10.11975/j.issn.1002-6819.2020.22.006
	丁志文, 邢艳秋, 尹伯卿, 等. 融合无人机和地基激光雷达点云数据估测单木结构参数. 森林工程, 2024, 40 (1): 142- 151.
	Ding Z W, Xing Y Q, Yin B Q, et al. Fusion of UAV and TLS LiDAR point cloud data for estimating individual tree structure parameters. Forest Engineering, 2024, 40 (1): 142- 151.
	姜新波, 胡　昊, 刘九庆, 等. 纳秒水导激光加工木材工艺探讨. 林业科学, 2018, 54 (1): 121- 127. doi: 10.11707/j.1001-7488.20180114
	Jiang X B, Hu H, Liu J Q, et al. Discussion on the processing of wood by nanosecond water guide laser. Scientia Silvae Sinicae, 2018, 54 (1): 121- 127. doi: 10.11707/j.1001-7488.20180114
	李伟光, 张占宽. 微坑型微织构硬质合金表面对木材摩擦特性的影响. 林业科学, 2019, 55 (4): 136- 143. doi: 10.11707/j.1001-7488.20190414
	Li W G, Zhang Z K. The effect of micro-pits texture on the coefficient of friction between wood and cemented carbide. Scientia Silvae Sinicae, 2019, 55 (4): 136- 143. doi: 10.11707/j.1001-7488.20190414
	龙泳学, 陈庆轩, 赵梓旭, 等. 基于CMA1390型CO₂激光切削机的水曲柳切削效果回归模型建立. 林业科学, 2024, 60 (11): 170- 176. doi: 10.11707/j.1001-7488.LYKX20230453
	Long Y X, Chen Q X, Zhao Z X, et al. Regression modeling of Fraxinus mandshurica cutting effect based on CMA1390 CO₂ laser cutting machine. Scientia Silvae Sinicae, 2024, 60 (11): 170- 176. doi: 10.11707/j.1001-7488.LYKX20230453
	梁鹏鹏, 吴俊华, 赵洪刚, 等. 激光加工桦木切削效果分析. 林产工业, 2023, 60 (2): 30- 34.
	Liang P P, Wu J H, Zhao H G, et al. Analysis of the cutting effect of laser processed birch. China Forest Products Industry, 2023, 60 (2): 30- 34.
	梁鹏鹏, 孙耀星, 赵洪刚, 等. 以效果为目标的激光机技术参数优化组合. 林产工业, 2024, 61 (1): 31- 33, 78.
	Liang P P, Sun Y X, Zhao H G, et al. Optimization combination of laser machine technical parameters aiming at effect. China Forest Products Industry, 2024, 61 (1): 31- 33, 78.
	任长清, 李　响, 杨春梅, 等. LOM单板层积成型试验机激光切割工艺参数的分析与仿真. 林业机械与木工设备, 2019, 47 (3): 45- 48. doi: 10.3969/j.issn.2095-2953.2019.03.011
	Ren C Q, Li X, Yang C M, et al. Design and simulation of laser cutting process parameters of LOM veneer lamination forming test machines. Forestry Machinery & Woodworking Equipment, 2019, 47 (3): 45- 48. doi: 10.3969/j.issn.2095-2953.2019.03.011
	谭　波. 2011. 响应曲面法优化激光打孔工艺参数的研究. 武汉: 华中科技大学.
	Tan B. 2011. Study on optimization of laser drilling parameters with response surface methodology. Wuhan: Huazhong University of Science and Technology. ［in Chinese］
	翁　斌, 张　超, 阙　云, 等. 基于响应面法的隧道复合式路面力学响应分析. 森林工程, 2024, 40 (2): 176- 187. doi: 10.3969/j.issn.1006-8023.2024.02.019
	Weng B, Zhang C, Que Y, et al. Mechanical response analysis of tunnel composite pavement based on response surface. Forest Engineering, 2024, 40 (2): 176- 187. doi: 10.3969/j.issn.1006-8023.2024.02.019
	晏颖杰, 范少辉, 官凤英. 地基激光雷达技术在森林调查中的应用研究进展. 世界林业研究, 2018, 31 (4): 42- 47.
	Yan Y J, Fan S H, Guan F Y. Research progress in TLS technology in forest investigation. World Forestry Research, 2018, 31 (4): 42- 47.
	赵洪刚, 乐　磊, 刘明利, 等. 拼花实木复合地板激光切割制备工艺研究. 北京林业大学学报, 2016, 38 (6): 110- 115.
	Zhao H G, Le L, Liu M L, et al. Laser cutting preparation technology of solid wood parquet laminate flooring. Journal of Beijing Forestry University, 2016, 38 (6): 110- 115.
	赵洪刚, 孙耀星, 高金贵, 等. 激光切割蒙古栎合理技术参数组合优化. 林业科学, 2017, 53 (12): 112- 119. doi: 10.11707/j.1001-7488.20171212
	Zhao H G, Sun Y X, Gao J G, et al. Combinatorial optimization of reasonable technical parameters for laser cutting oak. Scientia Silvae Sinicae, 2017, 53 (12): 112- 119. doi: 10.11707/j.1001-7488.20171212
	赵洪刚, 覃　胜, 赵梓旭, 等. 激光机加工落叶松技术参数的优化. 机床与液压, 2021, 49 (4): 27- 30.
	Zhao H G, Qin S, Zhao Z X, et al. Optimization for parameters of larch laser processing technology. Machine Tool & Hydraulics, 2021, 49 (4): 27- 30.
	赵　静, 钱　桦, 张厚江, 等. 激光雕刻木材工艺参数的研究. 木材加工机械, 2006, 17 (6): 15- 17. doi: 10.3969/j.issn.1001-036X.2006.06.005
	Zhao J, Qian H, Zhang H J, et al. Study on the technical paramers for laser carving wood. Wood Processing Machinery, 2006, 17 (6): 15- 17. doi: 10.3969/j.issn.1001-036X.2006.06.005
	赵永辉, 刘雪妍, 吕　勇, 等. 基于激光点云数据的单木骨架三维重构. 森林工程, 2024, 40 (1): 128- 134.
	Zhao Y H, Liu X Y, Lyu Y, et al. 3D reconstruction of single wood skeleton based on laser point cloud data. Forest Engineering, 2024, 40 (1): 128- 134.
	Angın D, Tiryaki A E. 2016. Application of response surface methodology and artificial neural network on pyrolysis of safflower seed press cake. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 38(8): 1055−1061.
	Barcikowski S, Koch G, Odermatt J. Characterisation and modification of the heat affected zone during laser material processing of wood and wood composites. Holz Als Roh- und Werkstoff, 2006, 64 (2): 94- 103. doi: 10.1007/s00107-005-0028-1
	Bolton D K, Coops N C, Wulder M A, et al. Assessing the impact of a snowstorm on forest structure in Ontario, Canada using a combination of Landsat time-series and airborne LiDAR data. Proceedings of the 13th International Conference on LiDAR Applications in Forestry, 2013, Beijing, China.
	Calders K, Adams J, Armston J, et al. Terrestrial laser scanning in forest ecology: expanding the horizon. Remote Sensing of Environment, 2020, 251, 112102. doi: 10.1016/j.rse.2020.112102
	Fukuta S, Nomura M, Ikeda T, et al. UV laser machining of wood. European Journal of Wood & Wood Products, 2016, 74 (2): 261- 267.
	Gräf S, Staupendahl G, Krämer A, et al. High percision materials processing using a novel Q-switched CO₂ laser. Optics and Lasers in Engineering, 2014, (66): 152- 157.
	Hao H J, Wang M L, Hao F Q. 2013. Multi-objective optimization of quality in laser cutting based on response surface model. Advanced Materials Research, 756/757/758/759: 3712-3716.
	Li R R, Guo X L, Cao P X, et al. Optimization of laser cutting parameters for recombinant bamboo based on response surface methodology. Wood Research, 2016, 61 (2): 275- 285.
	Madić M, Radovanović M, Manić M, et al. Optimization of CO₂ laser cutting process using taguchi and dual response surface methodology. Tribology in Industry, 2014, 36 (3): 236- 243.
	Peng X, Zhao A J, Chen Y F, et al. Tree height measurements in degraded tropical forests based on UAV-LiDAR data of different point cloud densities: a case study on Dacrydium pierrei in China. Forests, 2021, 12 (3): 328. doi: 10.3390/f12030328
	Ružiak I, Igaz R, Kubovský I, et al. Prediction of the effect of CO₂ laser cutting conditions on spruce wood cut characteristics using an artificial neural network. Applied Sciences, 2022, 12 (22): 11355. doi: 10.3390/app122211355

水平 Level	因素 Factor
水平 Level	含水率 Moisture content （A）（%）	镜头高 Lens height （B）/mm	进给速度 Cutting speed （C）/（mm·s^?1）	光强 Light intensity （D）（%）
1	8.29	3	40	25
2	18.81	8	100	63
3	28.41	12.5	160	100

序号 Serial number	影响因素Influencing factors				缝深 Seam depth/ μm	缝宽 Seam width/ μm
序号 Serial number	含水率 Moisture content （A）（%）	镜头高 Lens height （B）/ mm	进给速度 Cutting speed （C）/ （mm·s^?1）	光强 Light intensity （D）（%）	缝深 Seam depth/ μm	缝宽 Seam width/ μm
1	8.29	3	100	63	1 565	326
2	8.29	8	40	63	2 174	507
3	8.29	8	100	25	869	395
4	8.29	8	100	100	988	435
5	8.29	8	160	63	613	475
6	8.29	12.5	100	63	711	838
7	18.81	3	40	63	2 314	266
8	18.81	3	100	25	514	278
9	18.81	3	100	100	1 293	263
10	18.81	3	160	63	511	263
11	18.81	8	40	25	1 315	313
12	18.81	8	40	100	1 454	406
13	18.81	8	100	63	640	254
14	18.81	8	100	63	723	212
15	18.81	8	100	63	564	282
16	18.81	8	100	63	756	203
17	18.81	8	100	63	526	293
18	18.81	8	160	25	515	192
19	18.81	8	160	100	529	300
20	18.81	12.5	40	63	1 115	497
21	18.81	12.5	100	25	385	632
22	18.81	12.5	100	100	346	682
23	18.81	12.5	160	63	256	643
24	28.41	3	100	63	1 669	496
25	28.41	8	100	25	1 140	475
26	28.41	8	100	100	1 370	557
27	28.41	8	40	63	2 720	493
28	28.41	8	160	63	777	438
29	28.41	12.5	100	63	714	837

来源Source	平方和 Sum of squares	自由度df	均方 Mean square	F	P
方程Equation	1.014E+07	14	7.240E+05	16.63	<0.000 1
A	1.803E+05	1	1.803E+05	4.14	0.061 2
B	1.544E+06	1	1.544E+06	35.46	<0.000 1
C	5.239E+06	1	5.239E+06	120.33	<0.000 1
D	1.443E+05	1	1.443E+05	3.31	0.090 1
AB	1 039.29	1	1 039.29	0.023 9	0.879 4
AC	29 084.59	1	29 084.59	0.668 0	0.427 4
AD	3 080.82	1	3 080.82	0.070 8	0.794 1
BC	2.230E+05	1	2.230E+05	5.12	0.040 0
BD	1.760E+05	1	1.760E+05	4.04	0.064 0
CD	4 686.19	1	4 686.19	0.107 6	0.747 7
A²	1.759E+06	1	1.759E+06	40.40	<0.000 1
B²	301.44	1	301.44	0.006 9	0.934 9
C²	9.781E+05	1	9.781E+05	22.47	0.000 3
D²	21 184.70	1	21 184.70	0.486 6	0.496 9
残差Residual	6.096E+05	14	43 539.41
失拟项Lack-of-fit	5.705E+05	10	57 045.09	5.84	0.051 9
误差项Error term	39 100.80	4	9 775.20
总和Sum	1.075E+07	28

来源Source	平方和 Sum of squares	自由度 df	均方 Mean square	F	P
方程Equation	8.458E+05	14	60 416.54	17.43	<0.000 1
A	9 467.08	1	9 467.08	2.73	0.120 7
B	4.212E+05	1	4.212E+05	121.48	<0.000 1
C	2 803.87	1	2 803.87	0.808 7	0.383 7
D	9 971.53	1	9 971.53	2.88	0.112 0
AB	7 994.90	1	7 994.90	2.31	0.151 1
AC	115.55	1	115.55	0.033 3	0.857 8
AD	411.03	1	411.03	0.118 6	0.735 7
BC	4 783.99	1	4 783.99	1.38	0.259 7
BD	1 191.53	1	1 191.53	0.343 7	0.567 0
CD	74.77	1	74.77	0.021 6	0.885 3
A²	2.638E+05	1	2.638E+05	76.10	<0.000 1
B²	2.076E+05	1	2.076E+05	59.87	<0.000 1
C²	1 595.83	1	1 595.83	0.460 3	0.508 5
D²	7 043.80	1	7 043.80	2.03	0.176 0
残差Residual	48 537.49	14	3 466.96
失拟项Lack-of-fit	41 724.29	10	4 172.43	2.45	0.201 1
误差项Error term	6 813.20	4	1 703.30
总和Sum	8.944E+05	28

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[2]	熊晓燕,李彩霞,柴国奇,陈龙,贾翔,雷令婷,张晓丽. 联合UAV-LiDAR和GEDI数据的区域森林地上生物量估算[J]. 林业科学, 2025, 61(8): 142-153.
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[5]	姚黎阳,朱阅,王亚宁,庞帅. 林用小型轮式移动平台轮胎尺寸参数对其行驶性能的影响[J]. 林业科学, 2025, 61(2): 180-189.
[6]	刘鲁霞,胡波,桑国庆,刘玉玉. 激光雷达森林结构指标在森林植物多样性评估中的研究进展[J]. 林业科学, 2025, 61(1): 176-196.
[7]	胡中洋,陕亮,陈翔宇,余坤勇,刘健. CHM与DSM相结合的无人机激光雷达单木分割[J]. 林业科学, 2024, 60(8): 14-24.
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[13]	杨晓慧,吴金卓,刘浩然,钟浩,林文树. 基于UAV-LiDAR的人工林林分郁闭度估测[J]. 林业科学, 2023, 59(8): 12-21.
[14]	张昶,韩文静,王成. 植物色彩对潮白河城镇河岸夏秋季景观视觉吸引力的影响[J]. 林业科学, 2023, 59(8): 30-39.
[15]	谢栋博,雷雅凯,张宇超,刘清旺,符利勇,陈巧. 基于机载LiDAR数据的崇礼冬奥核心区树冠覆盖率估算[J]. 林业科学, 2022, 58(10): 24-34.