Optimization of energy consumption and surface roughness in slot milling of AISI 6061 T6 using the response surface method

被引:18
|
作者
Camposeco-Negrete, Carmita [1 ]
de Dios Calderon-Najera, Juan [1 ]
机构
[1] Tecnol Monterrey, Sch Engn & Sci, Eduardo Monroy Cardenas 2000, Toluca 50110, Estado De Mexic, Mexico
关键词
Energy consumption reduction; Slot milling; Green manufacturing; Response surface methodology; PARAMETERS; RSM;
D O I
10.1007/s00170-019-03848-2
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Industrial sectors are looking for reducing their energy consumption due to the concerns on fossil fuel depletion and climate change, along with the increasing energy costs. In recent years, research on optimizing the energy consumption of machining processes has received significant attention as a way to diminish their environmental impact.Milling is a manufacturing process widely used. This paper presents an experimental study to optimize the energy consumption during slot milling of AISI 6061 T6. Surface roughness and specific energy were selected as the key characteristics of the milling process to be studied. The response surface method was employed to generate the regression model for the aforementioned variables. Moreover, the desirability method was used to obtain the values of the cutting parameters that achieved a minimum value of electrical energy consumed during the process by the machine tool and surface roughness.The results obtained showed that the approach presented in this work allowed a reduction of the energy consumed by the machine tool and a significant improvement with regard to the surface roughness. Therefore, the impacts on the environment related to the milling process were reduced without compromising the performance and quality of the final product.
引用
收藏
页码:4063 / 4069
页数:7
相关论文
共 50 条
  • [31] PREDICTION AND OPTIMIZATION OF SURFACE ROUGHNESS AND CUTTING FORCE IN TURNING OF UHMWPE BY USING TAGUCHI METHOD, RESPONSE SURFACE METHODOLOGY AND NEURAL NETWORKS
    Susac, Florin
    Stan, Felicia
    Fetecau, Catalin
    Besliu, Irina
    PROCEEDINGS OF THE ASME 2020 15TH INTERNATIONAL MANUFACTURING SCIENCE AND ENGINEERING CONFERENCE (MSEC2020), VOL 2A, 2020,
  • [32] Development of surface roughness prediction model using response surface methodology in high speed end milling of AISI H13 tool steel
    Hafiz, A. M. K.
    Amin, A. K. M. N.
    Karim, A. N. M.
    Lajis, M. A.
    2007 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT, VOLS 1-4, 2007, : 1868 - +
  • [33] Optimization of Surface Roughness and Cutting Force in MQL Hard-Milling of AISI H13 Steel
    The-Vinh Do
    Nguyen-Anh-Vu Le
    ADVANCES IN ENGINEERING RESEARCH AND APPLICATION, 2019, 63 : 448 - 454
  • [34] Analysis and optimization of surface roughness in the ball burnishing process using response surface methodology and desirabilty function
    Sagbas, Aysun
    ADVANCES IN ENGINEERING SOFTWARE, 2011, 42 (11) : 992 - 998
  • [35] Optimization of surface roughness in milling of EN 24 steel with WC-Coated inserts using response surface methodology: analysis using surface integrity microstructural characterizations
    Patil, Shashwath
    Sathish, Thanikodi
    Rao, P. S.
    Prabhudev, M. S.
    Vijayan, V.
    Rajkumar, S.
    Sharma, Shubham
    Kumar, Abhinav
    Abbas, Mohamed
    Makki, Emad
    FRONTIERS IN MATERIALS, 2024, 11
  • [36] Modeling and optimization of roller burnishing of Al6061-T6 process for minimum surface roughness, better microhardness and roundness
    Dwivedi, Rashmi
    Somatkar, Avinash
    Chinchanikar, Satish
    OBRABOTKA METALLOV-METAL WORKING AND MATERIAL SCIENCE, 2024, 26 (03): : 52 - 65
  • [37] PREDICTION OF SURFACE ROUGHNESS IN END MILLING OPERATION OF DUPLEX STAINLESS STEEL USING RESPONSE SURFACE METHODOLOGY
    Philip, S. D.
    Chandramohan, P.
    Rajesh, P. K.
    JOURNAL OF ENGINEERING SCIENCE AND TECHNOLOGY, 2015, 10 (03) : 340 - 352
  • [38] Investigating temperature variation of Al 6065 T6 during milling operation for aerospace applications using response surface methodology and support vector regression
    Ilesanmi Afolabi Daniyan
    Felix Ale
    Lanre Daniyan
    Adefemi Adeodu
    Siviwe Mrausi
    The International Journal of Advanced Manufacturing Technology, 2025, 137 (1) : 933 - 949
  • [39] Assessment of Surface Roughness in Milling of Beech Using a Response Surface Methodology and an Adaptive Network-Based Fuzzy Inference System
    Zhu, Zhaolong
    Jin, Dong
    Wu, Zhanwen
    Xu, Wei
    Yu, Yingyue
    Guo, Xiaolei
    Wang, Xiaodong
    MACHINES, 2022, 10 (07)
  • [40] Optimization Of Roller Burnishing Process Parameters On Surface Roughness Using Response Surface Methodology
    Ulhe, Prashant N.
    Patil, U. D.
    Patil, C. R.
    MATERIALS TODAY-PROCEEDINGS, 2019, 18 : 3632 - 3637