TY - JOUR
T1 - Tool performance optimization while machining aluminium-based metal matrix composite
AU - Umer, Usama
AU - Abidi, Mustufa Haider
AU - Qudeiri, Jaber Abu
AU - Alkhalefah, Hisham
AU - Kishawy, Hossam
N1 - Funding Information:
Acknowledgments: This project was funded by the National Plan for Science, Technology, and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabia, Award No. 13-ADV-971-02.
Funding Information:
Funding: This research and APC was funded by the National Plan for Science, Technology, and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabia, Award No. 13-ADV-971-02.
Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2020/6
Y1 - 2020/6
N2 - Finite element (FE) models and the multi objective genetic algorithm (MOGA-II) have been applied for tool performance optimization while machining aluminum-based metal matrix composites. The developed and verified FE models are utilized to generate data for the full factorial design of experiment (DOE) plan. The FE models consist of a heterogenous workpiece, which assumes uniform distribution of reinforced particles according to size and volume fraction. Cutting forces, chip morphology, temperature contours, stress distributions in the workpiece and tool by altering cutting speed, feed rate, and reinforcement particle size can be estimated using developed FE models. The DOE data are then utilized to develop response surfaces using radial basis functions. To reduce computational time, these response surfaces are used as solver for optimization runs using MOGA-II. Tool performance has been optimized with regard to cutting temperatures and stresses while setting a limit on specific cutting energy. Optimal solutions are found with low cutting speed and moderate feed rates for each particle size metal matrix composite (MMC).
AB - Finite element (FE) models and the multi objective genetic algorithm (MOGA-II) have been applied for tool performance optimization while machining aluminum-based metal matrix composites. The developed and verified FE models are utilized to generate data for the full factorial design of experiment (DOE) plan. The FE models consist of a heterogenous workpiece, which assumes uniform distribution of reinforced particles according to size and volume fraction. Cutting forces, chip morphology, temperature contours, stress distributions in the workpiece and tool by altering cutting speed, feed rate, and reinforcement particle size can be estimated using developed FE models. The DOE data are then utilized to develop response surfaces using radial basis functions. To reduce computational time, these response surfaces are used as solver for optimization runs using MOGA-II. Tool performance has been optimized with regard to cutting temperatures and stresses while setting a limit on specific cutting energy. Optimal solutions are found with low cutting speed and moderate feed rates for each particle size metal matrix composite (MMC).
KW - Cutting tools
KW - Finite element model (FEM)
KW - Metal matrix composite (MMC)
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U2 - 10.3390/met10060835
DO - 10.3390/met10060835
M3 - Article
AN - SCOPUS:85086833497
SN - 2075-4701
VL - 10
SP - 1
EP - 16
JO - Metals
JF - Metals
IS - 6
M1 - 835
ER -