吴文宁,孙文磊,刘志远,于江通,杨玉林,黄勇.激光熔覆CoCrFeNiTi高熵合金工艺优化及预测模型研究[J].表面技术,2024,53(11):205-216.
WU Wenning,SUN Wenlei,LIU Zhiyuan,YU Jiangtong,YANG Yulin,HUANG Yong.Process Optimization and Prediction Model of Laser Cladding CoCrFeNiTi High-entropy Alloy[J].Surface Technology,2024,53(11):205-216 |
| 激光熔覆CoCrFeNiTi高熵合金工艺优化及预测模型研究 |
| Process Optimization and Prediction Model of Laser Cladding CoCrFeNiTi High-entropy Alloy |
| 投稿时间：2023-05-05 修订日期：2023-09-27 |
| DOI：10.16490/j.cnki.issn.1001-3660.2024.11.018 |
| 中文关键词: 激光熔覆 CoCrFeNiTi 工艺参数 方差分析 信噪比 支持向量回归 预测模型 |
| 英文关键词:laser cladding CoCrFeNiTi process parameters ANOVA signal-to-noise ratio support vector regression prediction model |
| 基金项目:新疆维吾尔自治区重点研究项目(2022B01036);新疆维吾尔自治区科研创新项目(XJ2022G011) |
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| Author | Institution |
| WU Wenning | College of Mechanical Engineering, Xinjiang University, Urumqi 830017, China |
| SUN Wenlei | College of Mechanical Engineering, Xinjiang University, Urumqi 830017, China |
| LIU Zhiyuan | College of Mechanical Engineering, Xinjiang University, Urumqi 830017, China |
| YU Jiangtong | College of Mechanical Engineering, Xinjiang University, Urumqi 830017, China |
| YANG Yulin | College of Mechanical Engineering, Xinjiang University, Urumqi 830017, China |
| HUANG Yong | College of Mechanical and Electrical Engineering, Xinjiang Institute of Engineering, Urumqi 830091, China |
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| 中文摘要: |
| 目的 探究激光熔覆工艺参数对CoCrFeNiTi高熵合金涂层质量及形貌的影响，实现激光熔覆CoCrFeNiTi高熵合金涂层形貌的精确控制。方法 基于田口正交法，设计不同激光工艺参数下30CrMnSiA表面激光熔覆CoCrFeNiTi高熵合金实验，以激光功率、扫描速度、送粉速率为影响因素，以涂层稀释率、高度、宽度、裂纹密度、宽高比为响应目标，通过方差和信噪比分析影响因素与响应目标的关系，并确定最优工艺参数，建立工艺参数与CoCrFeNiTi高熵合金涂层性能和形貌的支持向量回归预测模型。结果 激光功率对熔覆层稀释率、宽度和裂纹密度的影响较大，且与熔覆层稀释率、高度、宽度、裂纹密度、宽高比呈正相关。扫描速度对涂层高度、裂纹密度和宽高比的影响较大，与涂层高度呈负相关，与涂层裂纹密度和宽高比呈正相关。送粉速率对熔覆层稀释率、高度和宽高比的影响较大，与熔覆层稀释率和高度呈负相关，与熔覆层宽高比呈正相关。得到了最优工艺参数，激光功率为600 W，扫描速度为18 mm/s，送粉速率为1.6 r/min。通过预测模型测试可知，熔覆层稀释率、高度、宽度、裂纹密度和宽高比预测模型的决定系数均大于0.93。结论 基于支持向量回归的CoCrFeNiTi高熵合金涂层形貌预测模型的精度较高，能够实现CoCrFeNiTi高熵合金熔覆层形貌的精确预测，为熔覆层形貌的控制提供了新的思路。 |
| 英文摘要: |
| In laser cladding process, as important factors, cladding parameters directly determine the surface quality and morphology characteristics of coatings. At present, there are few explorers on the impact of cladding parameters on the surface quality and morphology characteristics of CoCrFeNiTi high-entropy alloy coatings. The work aims to investigate how cladding parameters affect the quality and morphology characteristics of CoCrFeNiTi high-entropy alloy coatings. In order to accurately control of geometric morphology of laser cladding, a prediction model for the properties and geometric morphology of the CoCrFeNiTi high-entropy alloy coatings was established based on support vector regression. According to the Taguchi Design experiment scheme in Minitab 19 software, L25(53) orthogonal experiment was schemed. CoCrFeNiTi high-entropy alloy coatings were prepared on the surface of 30CrMnSiA under different laser process parameters. The most influential cladding parameters (laser power, scanning speed, and powder feeding rate) were selected as impact factors and the dilution rate, height, width, crack density and width to height ratio as response targets. The relationship between influencing factors and response targets were analyzed through variance and signal-to-noise ratio method in detail. By comprehensively considering various response objectives the optimized process parameters were obtained. Based on the experimental data, a prediction model for the properties and geometric morphology of the CoCrFeNiTi high-entropy alloy coating was established by support vector regression. The results indicated that the dilution rate, crack density and width of coatings were impacted by laser power, and they all magnified with the increase of the laser power. The height and width to height ratio were affected by scanning speed and powder feeding. The scanning speed and powder feeding rate were positively correlated to the height of cladding layer and negatively correlated to the width to height ratio. The optimal cladding parameters were laser power 600 W, scanning speed 18 mm/s, and powder feeding rate 1.6 r/min. Based on experimental data, the predictive model was tested, the predictive model determination coefficient values of each feature were all greater than 0.93. Especially, the predictive model determination coefficient value of the height of cladding layer was up to 0.989 9. These consequences indicated that there was a good correlation between the predicted consequences of characteristics and the input parameters of the prediction model, the established prediction model could accurately predict the morphology characteristics of the cladding layer. The geometric morphology prediction model of CoCrFeNiTi high-entropy alloy coating based on support vector regression has high prediction accuracy, which can achieve accurate prediction of the morphology of the CoCrFeNiTi high-entropy alloy cladding layer. By using this model, the geometric characteristic parameters of the cladding layer can be obtained prior to its application. By adjusting the process parameters, it is possible to obtain the cladding layer with the least machining, thus enhancing the work efficiency of subsequent cutting processing. This research work gives a guideline for the selection of appropriate cladding parameters for CoCrFeNiTi high-entropy alloy coatings and provides a new idea for controlling the morphology of the cladding layer. |
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