Modeling and multiobjective optimization of thermal effects of fiber laser cutting of Inconel 600 sheet by employing the ANN and multi-objective PSO algorithm

IF 1.7 4区 工程技术 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY
Mohammad Hossein Razavi Dehkordi, Dheyaa J. Jasim, Ameer H. Al-Rubaye, Mohammad Akbari, Seyed Amin Bagherzadeh, Mohammadreza Ghazi, Hamed Mohammadkarimi
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引用次数: 0

Abstract

In this study, the experimental results of fiber laser cutting of Inconel 600 was modeled and optimized by combining artificial neural networks (ANNs) and particle swarm optimization (PSO). The impact of cutting criteria on the temperature adjacent to the cut kerf and roughness of the cutting edge was experimentally evaluated. The independent variables are the cutting speed, focal length, and laser power. The fiber laser cutting characteristics are modeled at different cutting conditions by the ANN method according to the experimental data. The findings indicated that the ANN is performing reasonably well in dealing with the training and test datasets. Also, the multiobjective PSO has been developed to effectively optimize the laser cutting procedure parameters in order to achieve the maximum temperature (the temperature upper than 370 °C) and minimum roughness (lower than 3 μm) simultaneously in order to improve the laser cutting efficiency. Based on the PSO results, the optimal laser power gained at a laser power of 830 and 1080 W at cutting speed ranges from 2 to 4 m/min and maximum focal length ranges between 0.75 and 0.8 mm where the lowest amount of roughness was created. The optimum temperature ranges were between 370 and 419°C. At a laser power of 1000 W and speed of 4 m/min, the smooth cutting edge at minimum roughness was gained without any defects. Transmission of the focal point up to 1.5 mm below the top surface of the sheet improved the roughness of the cutting edge and the cut quality by producing the smooth surface without slags.
基于神经网络和多目标粒子群算法的光纤激光切割Inconel 600板材热效应建模与多目标优化
本研究采用人工神经网络(ann)和粒子群优化(PSO)相结合的方法对光纤激光切割Inconel 600的实验结果进行建模和优化。实验评价了切削准则对切削刃附近温度和切削刃粗糙度的影响。自变量是切割速度、焦距和激光功率。根据实验数据,采用神经网络方法对不同切割条件下的光纤激光切割特性进行了建模。结果表明,人工神经网络在处理训练和测试数据集方面表现相当好。同时,开发了多目标粒子群算法,有效地优化了激光切割工艺参数,以同时实现最高温度(高于370°C)和最小粗糙度(低于3 μm),从而提高激光切割效率。基于PSO结果,激光功率为830和1080 W,切割速度为2 ~ 4 m/min,最大焦距为0.75 ~ 0.8 mm时,产生的粗糙度最小。最佳温度范围为370 ~ 419℃。在激光功率为1000 W,速度为4 m/min的条件下,获得了粗糙度最小的光滑刃口,没有任何缺陷。焦点传输到板材顶面以下1.5 mm处,通过产生无渣的光滑表面,提高了切割边缘的粗糙度和切割质量。
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来源期刊
CiteScore
3.60
自引率
9.50%
发文量
125
审稿时长
>12 weeks
期刊介绍: The Journal of Laser Applications (JLA) is the scientific platform of the Laser Institute of America (LIA) and is published in cooperation with AIP Publishing. The high-quality articles cover a broad range from fundamental and applied research and development to industrial applications. Therefore, JLA is a reflection of the state-of-R&D in photonic production, sensing and measurement as well as Laser safety. The following international and well known first-class scientists serve as allocated Editors in 9 new categories: High Precision Materials Processing with Ultrafast Lasers Laser Additive Manufacturing High Power Materials Processing with High Brightness Lasers Emerging Applications of Laser Technologies in High-performance/Multi-function Materials and Structures Surface Modification Lasers in Nanomanufacturing / Nanophotonics & Thin Film Technology Spectroscopy / Imaging / Diagnostics / Measurements Laser Systems and Markets Medical Applications & Safety Thermal Transportation Nanomaterials and Nanoprocessing Laser applications in Microelectronics.
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