Computational Analysis of Machining Induced Stress Distribution during Dry and Cryogenic Orthogonal Cutting of 7075 Aluminium Closed Cell Syntactic Foams
Abstract
:1. Introduction
2. Materials and Methods
2.1. Numerical Model of Machining Aluminum Closed Cell Syntactic Foams
2.2. Material Model
2.3. Chip Separation Criterion
2.4. Chip–Tool Interaction
2.5. Experimental Validation
3. Results and Discussion
3.1. Cutting Forces and Peak Cutting Temperature Distribution
3.2. Effect of Process Parameters on Simulated Machining Induced Stress
4. Conclusions
- An increase in cutting speed from 25 m/min to 100 m/min caused the machining induced surface to be more tensile in nature at higher cutting speeds up to a depth of 90 microns because of thermal softening of the matrix (as shown through peak cutting temperature contours).
- Orthogonal machining at a higher uncut chip thickness raised the cutting forces by 129%, with a corresponding increase of up to 600 microns in the depth of machining induced tensile stress beneath the cutting surface.
- The depth of machining induced tensile stress was increased by 350% for the syntactic foams reinforced with a coarser size of hollow aluminium oxide bubbles.
- Compared to dry cutting, liquid nitrogen coolant resulted in a significant (28.57%) reduction in the depth of machining induced tensile stress distribution.
- A favorable machined stress distribution was found at a cutting speed of 25 m/min, an uncut chip thickness of 0.07 mm, a 10% volume fraction, and a 0.3 mm average size of hollow aluminium oxide bubbles using liquid nitrogen coolant.
- The main novelty behind this software is the assumption of material foam as a homogeneous material model in order to simplify the material model as a complex heterogeneous material system for simulations; this enabled the production of FE results with reasonable accuracy. With this FE model developed using the AdvantEdgeTM software, it is possible to generate machining induced stress distribution from the contour, reducing effort in comparison to experimental work.
- Future studies must explore the addition of hollow particles to the matrix in the FE model, as well as techniques which can combine virtual reality technology with machining simulations thereby reducing unnecessary work for the technician.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Chemical Composition of 7075 Matrix in Weight (%) | |||||||
Al | Si | Fe | Cu | Mn | Mg | Cr | Zn |
89.72 | 0.08 | 0.24 | 1.5 | 0.06 | 2.4 | 0.2 | 5.8 |
Chemical Composition of the alumina bubble in weight (%) | |||||||
Al2O3 | Fe2O3 | CaO | SiO2 | Na2O | |||
99.7 | 0.003 | 0.01 | 0.025 | 0.26 |
Alumina Bubble | |||||
Physical Properties | |||||
Bulk Density (kg/m3) | Average Porosity (%) | Average Wall Thickness (μm) | Average Bubble Size (mm) | Bubble vol% | Thermal Conductivity (W m−1K−1) |
1800 | 83 | 0.04–0.08 | 0.3–0.6 | 10%, 20% | 1.5 |
Mechanical Properties | |||||
Crush strength (MPa) | Poisson’s ratio | ||||
120 ± 10 | 0.231 | ||||
7075 Matrix | |||||
Physical Properties | |||||
Density (kg/m3) | Thermal conductivity (W m−1K−1) | Specific heat (J kg−1K−1) | |||
2810 | 143 | 930 | |||
Mechanical Properties | |||||
Poisson ratio | Compressive strength (MPa) | Yield strength (MPa) | Young’s modulus (GPa) | ||
0.33 | 330 | 170 | 71.7 |
Thermal Conductivity (W m−1K−1) | Coefficient of Thermal Expansion (1/K) | Young’s Modulus (GPa) | Poisson’s Ratio | Density (kg/m3) | Specific Heat (J kg−1K−1) |
---|---|---|---|---|---|
110 | 5.5 × 10−6 | 700 | 0.31 | 15,600 | 39.8 |
Matrix | 7075 |
---|---|
Reinforcement Particle | Alumina Bubble |
Tool | Carbide coated inserts from Kennametal |
Rake angle and clearance angle | 0° and 7° |
Cutting speed (m/min) | 25, 50, 100 |
Undeformed Chip Thickness (mm) | 0.07, 0.15, 0.2 |
Volume fraction of hollow microsphere | 10%, 20% |
Average size of hollow microsphere (mm) | 0.1–0.5 mm, 0.5–1 mm |
Width of cut (mm) | 3 |
Lubrication | Dry, Liquid Nitrogen |
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Thomas, K.K.; Kannan, S.; Pervaiz, S.; Nazzal, M.; Karthikeyan, R. Computational Analysis of Machining Induced Stress Distribution during Dry and Cryogenic Orthogonal Cutting of 7075 Aluminium Closed Cell Syntactic Foams. Micromachines 2023, 14, 174. https://doi.org/10.3390/mi14010174
Thomas KK, Kannan S, Pervaiz S, Nazzal M, Karthikeyan R. Computational Analysis of Machining Induced Stress Distribution during Dry and Cryogenic Orthogonal Cutting of 7075 Aluminium Closed Cell Syntactic Foams. Micromachines. 2023; 14(1):174. https://doi.org/10.3390/mi14010174
Chicago/Turabian StyleThomas, Kevin K., Sathish Kannan, Salman Pervaiz, Mohammad Nazzal, and Ramanujam Karthikeyan. 2023. "Computational Analysis of Machining Induced Stress Distribution during Dry and Cryogenic Orthogonal Cutting of 7075 Aluminium Closed Cell Syntactic Foams" Micromachines 14, no. 1: 174. https://doi.org/10.3390/mi14010174
APA StyleThomas, K. K., Kannan, S., Pervaiz, S., Nazzal, M., & Karthikeyan, R. (2023). Computational Analysis of Machining Induced Stress Distribution during Dry and Cryogenic Orthogonal Cutting of 7075 Aluminium Closed Cell Syntactic Foams. Micromachines, 14(1), 174. https://doi.org/10.3390/mi14010174