Multi-objective optimization of steel nitriding

被引:22
|
作者
Cavaliere, P. [1 ]
Perrone, A. [1 ]
Silvello, A. [1 ]
机构
[1] Univ Salento, Dept Innovat Engn, Via Arnesano, I-73100 Lecce, Italy
来源
ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH | 2016年 / 19卷 / 01期
关键词
Nitriding; Mechanical properties; Optimization;
D O I
10.1016/j.jestch.2015.07.004
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Steel nitriding is a thermo-chemical process largely employed in the machine components production to solve mainly wear and fatigue damage in materials. The process is strongly influenced by many different variables such as steel composition, nitrogen potential (range 0.8-35), temperature (range 350-1200 degrees C), time (range 2-180 hours). In the present study, the influence of such parameters affecting the nitriding layers' thickness, hardness, composition and residual stress was evaluated. The aim was to streamline the process by numerical-experimental analysis allowing to define the optimal conditions for the success of the process. The optimization software that was used is modeFRONTIER (Esteco), through which was defined a set of input parameters (steel composition, nitrogen potential, nitriding time, etc.) evaluated on the basis of an optimization algorithm carefully chosen for the multi-objective analysis. The mechanical and microstructural results belonging to the nitriding process, performed with different processing conditions for various steels, are presented. The data were employed to obtain the analytical equations describing nitriding behavior as a function of nitriding parameters and steel composition. The obtained model was validated through control designs and optimized by taking into account physical and processing conditions. (C) 2015, Karabuk University. Production and hosting by Elsevier B.V.
引用
收藏
页码:292 / 312
页数:21
相关论文
共 50 条
  • [21] Multi-objective optimization for GPU3 Stirling engine by combining multi-objective algorithms
    Luo, Zhongyang
    Sultan, Umair
    Ni, Mingjiang
    Peng, Hao
    Shi, Bingwei
    Xiao, Gang
    RENEWABLE ENERGY, 2016, 94 : 114 - 125
  • [22] Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems
    Seyedali Mirjalili
    Pradeep Jangir
    Shahrzad Saremi
    Applied Intelligence, 2017, 46 : 79 - 95
  • [23] A multi-objective evolutionary algorithm for steady-state constrained multi-objective optimization problems
    Yang, Yongkuan
    Liu, Jianchang
    Tan, Shubin
    APPLIED SOFT COMPUTING, 2021, 101
  • [24] Jump and Shift Method for Multi-Objective Optimization
    Chen, S. X.
    Gooi, H. B.
    IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2011, 58 (10) : 4538 - 4548
  • [25] MOSTRO: A Multi-Objective System for Traffic Optimization
    Meja Estrada, Eliana
    Norena, Laura
    Sarrazola, Anna
    Espinosa, Jairo
    2017 IEEE 3RD COLOMBIAN CONFERENCE ON AUTOMATIC CONTROL (CCAC), 2017,
  • [26] Federated Learning Meets Multi-Objective Optimization
    Hu, Zeou
    Shaloudegi, Kiarash
    Zhang, Guojun
    Yu, Yaoliang
    IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING, 2022, 9 (04): : 2039 - 2051
  • [27] Interaction Design With Multi-Objective Bayesian Optimization
    Liao, Yi-Chi
    Dudley, John J.
    Mo, George B.
    Cheng, Chun-Lien
    Chan, Liwei
    Oulasvirta, Antti
    Kristensson, Per Ola
    IEEE PERVASIVE COMPUTING, 2023, 22 (01) : 29 - 38
  • [28] Multi-objective optimization for model predictive control
    Wojsznis, Willy
    Mehta, Ashish
    Wojsznis, Peter
    Thiele, Dirk
    Blevins, Terry
    ISA TRANSACTIONS, 2007, 46 (03) : 351 - 361
  • [29] Survey of multi-objective optimization methods for engineering
    Marler, RT
    Arora, JS
    STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2004, 26 (06) : 369 - 395
  • [30] Survey of multi-objective optimization methods for engineering
    R.T. Marler
    J.S. Arora
    Structural and Multidisciplinary Optimization, 2004, 26 : 369 - 395