TY - JOUR
T1 - Sustainable thin-wall machining
T2 - holistic analysis considering the energy efficiency, productivity, and product quality
AU - Bolar, Gururaj
AU - Joshi, Shrikrishna N.
AU - Das, Sanghamitra
N1 - Funding Information:
This work was supported by the Science and Engineering Research Board (SERB), Department of Science and Technology, Government of India (Grant number: SR-S3-MERC-0115-2012). Open access funding provided by Manipal Academy of Higher Education, Manipal
Publisher Copyright:
© 2022, The Author(s).
PY - 2023/2
Y1 - 2023/2
N2 - 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.
AB - 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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U2 - 10.1007/s12008-022-01130-6
DO - 10.1007/s12008-022-01130-6
M3 - Article
AN - SCOPUS:85143280611
SN - 1955-2513
VL - 17
SP - 145
EP - 166
JO - International Journal on Interactive Design and Manufacturing
JF - International Journal on Interactive Design and Manufacturing
IS - 1
ER -
