Sustainable thin-wall machining: holistic analysis considering the energy efficiency, productivity, and product quality

被引:0
作者
Gururaj Bolar
Shrikrishna N. Joshi
Sanghamitra Das
机构
[1] Manipal Academy of Higher Education,Department of Mechanical and Manufacturing Engineering, Manipal Institute of Technology
[2] Indian Institute of Technology Guwahati,Department of Mechanical Engineering
来源
International Journal on Interactive Design and Manufacturing (IJIDeM) | 2023年 / 17卷
关键词
Thin-wall machining; Productivity; Product quality; Cutting power; Optimization;
D O I
暂无
中图分类号
学科分类号
摘要
Enhanced energy efficiency, product quality, and productivity have become crucial requirements in thin-wall machining. Therefore, the work examined the impact of axial depth of cut, radial depth of cut, feed per tooth, and tool diameter on three performance measures. Full factorial was used to design experiments, and Analysis of Variance (ANOVA), a statistical method, was employed to analyze and interpret the influence of process variables on the machining performance. Additionally, Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) was adopted to arrive at the Pareto-optimal solutions to evaluate the trade-off between the three performance measures. The optimized process parameters for roughing operation helped maximize the process productivity at the expense of product quality. In contrast, the Pareto solutions for finishing operation effectively improved energy efficiency and produced quality open straight and curved thin-wall parts. Improved surface finish with minimal deflection can be achieved by milling with a cutter of diameter 8 mm and maintaining the feed, axial, and radial depth at 0.02 mm/z, 8 mm, and 0.3125 mm, respectively. The proposed findings can provide effective solutions for machining open straight and curved thin-wall parts with improved productivity, product quality, and energy efficiency.
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页码:145 / 166
页数:21
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  • [1] Zhang Y(2015)Study on optimized principles of process parameters for environmentally friendly machining austenitic stainless steel with high efficiency and little energy consumption Int. J. Adv. Manuf. Technol 79 89-99
  • [2] Zou P(2017)Experimental investigation of machinability characteristics and multi-response optimization of end milling in aluminium composites using RSM based grey relational analysis Measurement 105 78-86
  • [3] Li B(2018)Application of signal to noise ratio and grey relational analysis to minimize forces and vibrations during precise ball end milling Precis Eng 51 582-596
  • [4] Liang S(2012)Optimizing the cutting parameters for better surface quality in 2.5 D cutting utilizing titanium coated carbide ball end mill Int. J. Precis Eng. Manuf 13 2097-2102
  • [5] Rajeswari B(2019)Modelling and optimization of surface roughness during AISI P20 milling process using Taguchi method Int. J. Adv. Manuf. Technol 102 3707-3718
  • [6] Amirthagadeswaran KS(2017)Optimization study of dry peripheral milling process for improving aeronautical part integrity using Grey relational analysis Int. J. Adv. Manuf. Technol 91 931-942
  • [7] Wojciechowski S(2018)Optimization of machining conditions for surface quality in milling AA7039-based metal matrix composites Arab. J. Sci. Eng 43 1071-1082
  • [8] Maruda RW(2014)Optimization of process parameters using a response surface method for minimizing power consumption in the milling of carbon steel J. Clean. Prod 66 309-316
  • [9] Krolczyk GM(2016)Modeling and parameter optimization for cutting energy reduction in MQL milling process Int. J. Precis Eng. Manuf. Technol 3 5-12
  • [10] Niesłony P(2017)A process parameters optimization method of multi-pass dry milling for high efficiency, low energy and low carbon emissions J. Clean. Prod 148 174-184