Optimization of Laser Repair Process for Agricultural Machinery Parts Based on Genetic Algorithm

被引:0
|
作者
Yi, Qing [1 ]
Feng, Fei [1 ]
机构
[1] China Agr Univ, Coll Engn, Beijing 100083, Peoples R China
基金
中国国家自然科学基金; 北京市自然科学基金;
关键词
laser directional energy deposition; genetic algorithm; preferred algorithm; orthogonal experiment; process parameters;
D O I
10.3390/ma18040775
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Laser-directed energy deposition technology (LDED), a method for repairing worn agricultural machinery parts, is valued for its flexibility, efficiency, and economy. To improve the comprehensive quality of the parts repair layer and reduce the processing energy consumption and time, it is necessary to explore the influence law of process parameters and multi-objective optimization experiments. We used L9 (33) orthogonal experiments to evaluate the effects of laser power, scanning speed, and powder feed rate on repair quality. Variance analysis assessed factor level impacts and a multi-objective optimization model was constructed and optimized using a genetic algorithm (GA). Then, a preferred algorithm is proposed to optimize and obtain the optimal process level. The results show that the cladding efficiency increases at first and then decreases with the increase in laser power, decreases with the increase in scanning speed, and increases with the increase in powder feed rate. The dilution rate decreases at first and then increases with the increase in laser power, increases with the increase in scanning speed, and decreases with the increase in powder feed rate. In addition, it is also affected by the interaction between scanning speed and powder feed rate. Taking the maximum cladding efficiency and the minimum dilution rate as the optimization objectives, the verification test was carried out with the process parameters of laser power 1684.7370 W, scanning speed 3.0175 mm s-1, and powder feed rate 1.5901 r min-1. The error rates of cladding efficiency and dilution rate were 3.98% and 4.89%, respectively, which confirmed the method's effectiveness. The research results can provide a reference for the repair of worn parts of agricultural machinery, which is not only cost-effective but saves time, as well. The free formability of the LDED process also allows it to add special functions to simple damaged castings and forging parts during the repair process to improve their performance.
引用
收藏
页数:15
相关论文
共 50 条
  • [31] Genetic algorithm for process optimization in hospitals
    Kuehn, Matthias
    Baumann, Tommy
    Salzwedel, Horst
    PROCEEDINGS 26TH EUROPEAN CONFERENCE ON MODELLING AND SIMULATION ECMS 2012, 2012, : 103 - +
  • [32] Research on Agricultural Machinery Rental Optimization Based on the Dynamic Artificial Bee-Ant Colony Algorithm
    Hou, Jialin
    Zhang, Jingtao
    Wu, Wanying
    Jin, Tianguo
    Zhou, Kai
    ALGORITHMS, 2022, 15 (03)
  • [33] Optimization of Multiple Laser Range Finder Distribution Based on Genetic Algorithm
    Zhang, Zihao
    Wang, Xinhua
    Sun, Peng
    2017 INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS, ELECTRONICS AND CONTROL (ICCSEC), 2017, : 1481 - 1485
  • [36] Airfoil optimization design based on Gaussian process regression and genetic algorithm
    Chang L.
    Zhang Q.
    Guo X.
    Hangkong Dongli Xuebao/Journal of Aerospace Power, 2021, 36 (11): : 2306 - 2316
  • [37] Optimization for Manufacturing Process Based on Timed Petri Net and Genetic Algorithm
    Li Tingpeng
    Li Yue
    Qian Yanling
    Zeng Shuanggui
    PROCEEDINGS OF THE 2016 4TH INTERNATIONAL CONFERENCE ON MACHINERY, MATERIALS AND COMPUTING TECHNOLOGY, 2016, 60 : 638 - 645
  • [38] Analysis of the Circuit Optimization Process Based on a Generalized Approach and a Genetic Algorithm
    Zemliak, Alexander
    Osadchuk, Andrei
    JORDAN JOURNAL OF ELECTRICAL ENGINEERING, 2024, 10 (01): : 1 - 26
  • [39] Genetic Algorithm Based on Multiple Population in a Business Process Optimization Issue
    Mahammed, Nadir
    Fahsi, Mahmoud
    Bennabi, Souad
    2020 4TH INTERNATIONAL CONFERENCE ON ADVANCED ASPECTS OF SOFTWARE ENGINEERING (ICAASE'2020): 4TH INTERNATIONAL CONFERENCE ON ADVANCED ASPECTS OF SOFTWARE ENGINEERING, 2020, : 107 - 112
  • [40] Research on rapid process optimization technology based on Improved Genetic Algorithm
    Yu, Hang
    Miao, Liqin
    Jiang, Jichun
    Jiang, Heping
    Cui, Wanrui
    Meng, Fanjun
    Wang, Lijun
    Li, Yuxin
    Gao, Xiaojiao
    Fan, Yue
    PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON MATERIAL, MECHANICAL AND MANUFACTURING ENGINEERING, 2015, 27 : 740 - 746