Analysis of CNC turning parameters and simultaneous optimisation of surface roughness and material removal rate by MOGA for AISI 4340 alloy steel

被引:1
|
作者
Konnur, Virupakshappa S. [1 ]
Kulkarni, Sameer S. [1 ]
Hiremath, Santosh, V [1 ]
Kanal, Vishwanath S. [1 ]
Nirale, Vinod C. [1 ]
机构
[1] BLDEAs VP Dr PG Halakatti Coll Engn & Technol, Fac Dept Mech Engn, Vijayapur 586103, Karnataka, India
关键词
AISI 4340 alloy steel; CNC multipass turning; surface roughness; material removal rate; GRA; MOGA; ANN; regression; MACHINING PARAMETERS;
D O I
10.1080/2374068X.2023.2280293
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This article has made an approach towards the multipass turning of AISI 4340 alloy steel with minimum quantity of coolant to determine the Optimal Turning Parameters for simultaneous maximisation of material removal rate and minimisation of surface roughness. Nine experimental runs were planned according to Taguchi's Design of Experiments and performed on Computer Numerically Controlled Lathe Machine with high carbon steel as a tool. The mathematical models developed for surface roughness and material removal rate at different levels of speed, DOC & feed rate are considered as objective functions. Less than 5% error was observed when experimental results were compared with Regression and ANN Models for both responses. A multi-objective genetic algorithm and a Grey relational analysis were used to determine the optimal turning parameters and the results were validated experimentally. The MOGA provides the best optimal parameters for the simultaneous achievement of SR minimisation and MRR maximisation. Results reveal that speed and feed rate have a significant effect on responses. The Pareto Front Plots extracted from MOGA provide the minimum Ra value, 2.32 mu m and for the same point, maximum MRR is 6894.22 mm3/min. However, the experimental method could not meet this Ra value, but the recorded minimum was 2.67 mu m and the MRR was 6544.79 mm3/min.
引用
收藏
页码:3885 / 3908
页数:24
相关论文
共 50 条
  • [41] Investigation on the effects of ultrasonic vibration on material removal rate and surface roughness in wire electrical discharge turning
    Mohammadi, Aminollah
    Tehrani, Alireza Fadaei
    Abdullah, Amir
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2014, 70 (5-8) : 1235 - 1246
  • [42] Investigation on the effects of ultrasonic vibration on material removal rate and surface roughness in wire electrical discharge turning
    Aminollah Mohammadi
    Alireza Fadaei Tehrani
    Amir Abdullah
    The International Journal of Advanced Manufacturing Technology, 2014, 70 : 1235 - 1246
  • [43] CNC Milling of Medical-Grade PMMA: Optimization of Material Removal Rate and Surface Roughness
    Wambua, Job Maveke
    Mwema, Fredrick M.
    Tanya, Buddi
    Jen, Tien-Chien
    INTERNATIONAL JOURNAL OF MANUFACTURING MATERIALS AND MECHANICAL ENGINEERING, 2022, 12 (01)
  • [44] Multi-objective optimization of turning parameters for targeting surface roughness and maximizing material removal rate in dry turning of AISI 316L with PVD-coated cermet insert
    Touggui, Youssef
    Belhadi, Salim
    Mechraoui, Salah-Eddine
    Uysal, Alper
    Yallese, Mohammed Athmane
    Temmar, Mustapha
    SN APPLIED SCIENCES, 2020, 2 (08):
  • [45] Effect of Surface Texture Tools and Minimum Quantity Lubrication (MQL) on tool Wear and Surface Roughness in CNC Turning of AISI 52100 Steel
    Sivaiah P.
    Bodicherla U.
    Journal of The Institution of Engineers (India): Series C, 2020, 101 (01) : 85 - 95
  • [46] Parametric effect on material removal rate and surface roughness of electrical discharge machined magnesium alloy
    Ananthi, Narayanasamy
    Elaiyarasan, Uthirapathi
    Satheeshkumar, Vinaitheerthan
    Senthilkumar, Chinnamuthu
    Sathiyamurthy, Subbarayan
    Nallathambi, Kaliyamoorthi
    METALLURGICAL RESEARCH & TECHNOLOGY, 2021, 118 (06)
  • [47] Reducing the Roughness and Sound Intensity by Optimization of Cutting Parameters in Processing of AISI 2714 Steel Material on CNC Milling Machine
    Albayrak, Sirer
    Mercan, Serdar
    Karacam, Hikmet
    TEHNICKI VJESNIK-TECHNICAL GAZETTE, 2022, 29 (06): : 1941 - 1948
  • [48] Application Of Regression And Artificial Neural Network Analysis In Modelling Of Surface Roughness In Hard Turning Of AISI 52100 Steel
    Paturi, Uma Maheshwera Reddy
    Devarasetti, Harish
    Narala, Suresh Kumar Reddy
    MATERIALS TODAY-PROCEEDINGS, 2018, 5 (02) : 4766 - 4777
  • [49] A Closer Look at Precision Hard Turning of AISI4340: Multi-Objective Optimization for Simultaneous Low Surface Roughness and High Productivity
    Abbas, Adel T.
    Al-Abduljabbar, Abdulhamid A.
    Alnaser, Ibrahim A.
    Aly, Mohamed F.
    Abdelgaliel, Islam H.
    Elkaseer, Ahmed
    MATERIALS, 2022, 15 (06)
  • [50] Prediction of surface roughness and material removal rate in laser assisted turning of aluminium oxide using fuzzy logic
    Saradhi, V. Pardha
    Shashank, V.
    Teja, P. Sai
    Anbarasu, G.
    Bharat, A.
    Jagadesh, T.
    MATERIALS TODAY-PROCEEDINGS, 2018, 5 (09) : 20343 - 20350