Data-Analytics-Driven Selection of Die Material in Multi-Material Co-Extrusion of Ti-Mg Alloys
Abstract
:1. Introduction
2. Materials and Methods
2.1. Materials, Geometrical Dimensions, and Process Parameters
- ➢
- Process parameters: ram speed (mm/s) and temperature (°C).
- ➢
- Tooling parameters: die semi-angle (°), shear friction factor, and extrusion ratio (A0/Af).
- ➢
- Geometric parameters: shape factor (H0/D0) and diameter ratio (D0/d0).
2.2. Finite Element Modeling and Simulation Preparation
2.2.1. Tool Wear Model
2.2.2. FEM Validation
2.3. Support Vector Regression
2.4. Entropy Method
2.5. VIKOR Method
- ➢
- The ability to inmmediately recognize the proper alternative;
- ➢
- A decrease in the number of pairwise comparisons required.
2.6. Methodology
- ➢
- Simplicity;
- ➢
- Amount of data obtained from the simulations;
- ➢
- Time consumption.
3. Results
3.1. SVR Methodology
3.2. MCDM Methodology
86.019 | 136.529 | 228.511 | 307.324 | 97.898 | 88.621 | 88.040 | 84.512 | 83.759 | 0.367 | 0.318 | 0.302 | 0.124 | 0.417 | 0.253 | 0.378 | 0.361 | 0.370 |
86.153 | 124.955 | 227.644 | 307.783 | 98.095 | 88.706 | 88.140 | 84.799 | 83.850 | 0.379 | 0.382 | 0.257 | 0.219 | 0.414 | 0.271 | 0.404 | 0.381 | 0.354 |
95.608 | 128.196 | 199.579 | 307.520 | 100.048 | 87.505 | 111.049 | 87.149 | 86.484 | 0.361 | 0.327 | 0.250 | 0.211 | 0.471 | 0.445 | 0.354 | 0.341 | 0.345 |
87.120 | 134.333 | 206.128 | 309.397 | 100.654 | 86.238 | 110.203 | 88.307 | 88.602 | 0.507 | 0.369 | 0.410 | 0.452 | 0.573 | 0.462 | 0.430 | 0.436 | 0.428 |
92.531 | 132.578 | 221.350 | 306.154 | 100.708 | 87.422 | 112.221 | 89.296 | 87.588 | 0.304 | 0.257 | 0.281 | 0.236 | 0.389 | 0.303 | 0.291 | 0.292 | 0.173 |
0.1923 | 0.2079 | 0.2110 | 0.1998 | 0.1968 | 0.2021 | 0.1727 | 0.1947 | 0.1947 | 0.1913 | 0.1925 | 0.2011 | 0.1000 | 0.1842 | 0.1459 | 0.2035 | 0.1993 | 0.2214 |
0.1926 | 0.1903 | 0.2102 | 0.2001 | 0.1972 | 0.2023 | 0.1729 | 0.1954 | 0.1949 | 0.1977 | 0.2311 | 0.1717 | 0.1764 | 0.1829 | 0.1563 | 0.2175 | 0.2105 | 0.2118 |
0.2137 | 0.1952 | 0.1842 | 0.1999 | 0.2011 | 0.1996 | 0.2179 | 0.2008 | 0.2010 | 0.1884 | 0.1978 | 0.1667 | 0.1701 | 0.2078 | 0.2568 | 0.1908 | 0.1881 | 0.2067 |
0.1947 | 0.2046 | 0.1903 | 0.2011 | 0.2024 | 0.1967 | 0.2162 | 0.2034 | 0.2059 | 0.2642 | 0.2233 | 0.2733 | 0.3639 | 0.2532 | 0.2665 | 0.2315 | 0.2407 | 0.2565 |
0.2068 | 0.2019 | 0.2043 | 0.1990 | 0.2025 | 0.1994 | 0.2202 | 0.2057 | 0.2036 | 0.1584 | 0.1553 | 0.1871 | 0.1897 | 0.1719 | 0.1744 | 0.1567 | 0.1614 | 0.1037 |
86.019 | 136.529 | 228.511 | 307.324 | 97.898 | 88.621 | 88.040 | 84.512 | 83.759 | 0.367 | 0.318 | 0.302 | 0.124 | 0.417 | 0.253 | 0.378 | 0.361 | 0.370 | |
86.153 | 124.955 | 227.644 | 307.783 | 98.095 | 88.706 | 88.140 | 84.799 | 83.850 | 0.379 | 0.382 | 0.257 | 0.219 | 0.414 | 0.271 | 0.404 | 0.381 | 0.354 | |
95.608 | 128.196 | 199.579 | 307.520 | 100.048 | 87.505 | 111.049 | 87.149 | 86.484 | 0.361 | 0.327 | 0.250 | 0.211 | 0.471 | 0.445 | 0.354 | 0.341 | 0.345 | |
87.120 | 134.333 | 206.128 | 309.397 | 100.654 | 86.238 | 110.203 | 88.307 | 88.602 | 0.507 | 0.369 | 0.410 | 0.452 | 0.573 | 0.462 | 0.430 | 0.436 | 0.428 | |
92.531 | 132.578 | 221.350 | 306.154 | 100.708 | 87.422 | 112.221 | 89.296 | 87.588 | 0.304 | 0.257 | 0.281 | 0.236 | 0.389 | 0.303 | 0.291 | 0.292 | 0.173 | |
447.431 | 656.591 | 1083.212 | 1538.178 | 497.402 | 438.492 | 509.653 | 434.063 | 430.283 | 1.918 | 1.652 | 1.499 | 1.242 | 2.265 | 1.735 | 1.856 | 1.812 | 1.669 | |
fi* | 86.019 | 124.955 | 199.579 | 306.154 | 97.898 | 86.238 | 88.040 | 84.512 | 83.759 | 0.304 | 0.257 | 0.250 | 0.124 | 0.389 | 0.253 | 0.291 | 0.292 | 0.173 |
fi- | 95.608 | 136.529 | 228.511 | 309.397 | 100.708 | 88.706 | 112.221 | 89.296 | 88.602 | 0.507 | 0.382 | 0.410 | 0.452 | 0.573 | 0.462 | 0.430 | 0.436 | 0.428 |
Sj | Ri | ||
---|---|---|---|
0.23758265 | 0.12075866 | ||
0.36022622 | 0.11092085 | ||
0.44932592 | 0.1258087 | ||
0.98459134 | 0.37557176 | ||
0.21183731 | 0.12764252 | ||
S* | 0.21183731 | R* | 0.11092085 |
S− | 0.98459134 | R− | 0.37557176 |
Qi | |
---|---|
AISI3310 | 0.03524455 |
H13 | 0.09601303 |
AISI52100 | 0.18179111 |
25CrMo4 | 1 |
AISI3310 | 0.03159193 |
Q(2) − Q(1) = 0.0365261 |
Q(3) − Q(1) = 0.0644211 |
Q(4) − Q(1) = 0.15019917 |
Q(5) − Q(1) = 0.96840807 > DQ |
Q(1) = S* |
4. Discussion
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Simulation | Material | Ram Speed (mm/s) | Core Diameter (mm) | Billet Height (H) | Temperature (° C) | Friction | Die Semi-Angle (°) | Extrusion Ratio |
---|---|---|---|---|---|---|---|---|
1 | AISI316 | 2 | 5 | 20 | 200 | 0.2 | 30 | 1.78 |
2 | AISI316 | 2 | 6 | 15 | 100 | 0.2 | 30 | 2.25 |
3 | AISI316 | 2 | 7 | 25 | 100 | 0.3 | 30 | 1.44 |
4 | AISI316 | 3 | 6 | 15 | 200 | 0.3 | 15 | 2.25 |
5 | AISI316 | 3 | 7 | 15 | 300 | 0.2 | 45 | 1.44 |
6 | AISI316 | 2 | 6 | 20 | 200 | 0.1 | 30 | 1.78 |
7 | AISI316 | 2 | 6 | 20 | 200 | 0.1 | 15 | 1.78 |
8 | AISI316 | 2 | 6 | 20 | 200 | 0.1 | 45 | 1.78 |
9 | AISI316 | 2 | 6 | 20 | 200 | 0.1 | 60 | 1.78 |
10 | AISI316 | 2 | 6 | 20 | 200 | 0.1 | 75 | 1.78 |
11 | AISI316 | 2 | 6 | 20 | 200 | 0.1 | 90 | 1.78 |
12 | AISI316 | 2 | 2 | 20 | 200 | 0.1 | 30 | 1.78 |
13 | AISI316 | 2 | 4 | 20 | 200 | 0.1 | 30 | 1.78 |
14 | AISI316 | 2 | 8 | 20 | 200 | 0.1 | 30 | 1.78 |
15 | AISI316 | 2 | 10 | 20 | 200 | 0.1 | 30 | 1.78 |
16 | AISI316 | 2 | 6 | 15 | 200 | 0.1 | 30 | 1.78 |
17 | AISI316 | 2 | 6 | 25 | 200 | 0.1 | 30 | 1.78 |
18 | AISI316 | 2 | 6 | 30 | 200 | 0.1 | 30 | 1.78 |
19 | AISI316 | 2 | 6 | 35 | 200 | 0.1 | 30 | 1.78 |
20 | AISI316 | 2 | 6 | 20 | 200 | 0.2 | 30 | 1.78 |
21 | AISI316 | 2 | 6 | 20 | 200 | 0.3 | 30 | 1.78 |
22 | AISI316 | 2 | 6 | 20 | 200 | 0.4 | 30 | 1.78 |
23 | AISI316 | 2 | 6 | 20 | 200 | 0.5 | 30 | 1.78 |
24 | AISI316 | 2 | 6 | 20 | 200 | 0.6 | 30 | 1.78 |
25 | AISI316 | 2 | 6 | 20 | 200 | 0.7 | 30 | 1.78 |
26 | AISI316 | 2 | 6 | 20 | 100 | 0.1 | 30 | 1.78 |
27 | AISI316 | 2 | 6 | 20 | 300 | 0.1 | 30 | 1.78 |
28 | AISI316 | 1 | 6 | 20 | 300 | 0.1 | 30 | 1.78 |
29 | AISI316 | 3 | 6 | 20 | 300 | 0.1 | 30 | 1.78 |
30 | AISI316 | 4 | 6 | 20 | 300 | 0.1 | 30 | 1.78 |
31 | AISI316 | 2 | 6 | 20 | 200 | 0.1 | 30 | 1.44 |
32 | AISI316 | 2 | 6 | 20 | 200 | 0.1 | 30 | 2.25 |
33 | AISI316 | 2 | 6 | 20 | 200 | 0.1 | 30 | 2.94 |
34 | H13 | 2 | 5 | 15 | 100 | 0.1 | 15 | 1.44 |
35 | H13 | 2 | 6 | 25 | 300 | 0.1 | 15 | 1.78 |
36 | H13 | 3 | 5 | 15 | 300 | 0.3 | 30 | 1.78 |
37 | H13 | 3 | 6 | 25 | 100 | 0.2 | 45 | 1.44 |
38 | H13 | 3 | 7 | 25 | 200 | 0.1 | 30 | 2.25 |
39 | H13 | 2 | 6 | 20 | 200 | 0.1 | 30 | 1.78 |
40 | H13 | 2 | 6 | 20 | 200 | 0.1 | 15 | 1.78 |
41 | H13 | 2 | 6 | 20 | 200 | 0.1 | 45 | 1.78 |
42 | H13 | 2 | 6 | 20 | 200 | 0.1 | 60 | 1.78 |
43 | H13 | 2 | 6 | 20 | 200 | 0.1 | 75 | 1.78 |
44 | H13 | 2 | 6 | 20 | 200 | 0.1 | 90 | 1.78 |
45 | H13 | 2 | 2 | 20 | 200 | 0.1 | 30 | 1.78 |
46 | H13 | 2 | 4 | 20 | 200 | 0.1 | 30 | 1.78 |
47 | H13 | 2 | 8 | 20 | 200 | 0.1 | 30 | 1.78 |
48 | H13 | 2 | 10 | 20 | 200 | 0.1 | 30 | 1.78 |
49 | H13 | 2 | 6 | 15 | 200 | 0.1 | 30 | 1.78 |
50 | H13 | 2 | 6 | 25 | 200 | 0.1 | 30 | 1.78 |
51 | H13 | 2 | 6 | 30 | 200 | 0.1 | 30 | 1.78 |
52 | H13 | 2 | 6 | 35 | 200 | 0.1 | 30 | 1.78 |
53 | H13 | 2 | 6 | 20 | 200 | 0.2 | 30 | 1.78 |
54 | H13 | 2 | 6 | 20 | 200 | 0.3 | 30 | 1.78 |
55 | H13 | 2 | 6 | 20 | 200 | 0.4 | 30 | 1.78 |
56 | H13 | 2 | 6 | 20 | 200 | 0.5 | 30 | 1.78 |
57 | H13 | 2 | 6 | 20 | 200 | 0.6 | 30 | 1.78 |
58 | H13 | 2 | 6 | 20 | 200 | 0.7 | 30 | 1.78 |
59 | H13 | 2 | 6 | 20 | 100 | 0.1 | 30 | 1.78 |
60 | H13 | 2 | 6 | 20 | 300 | 0.1 | 30 | 1.78 |
61 | H13 | 1 | 6 | 20 | 300 | 0.1 | 30 | 1.78 |
62 | H13 | 3 | 6 | 20 | 300 | 0.1 | 30 | 1.78 |
63 | H13 | 4 | 6 | 20 | 300 | 0.1 | 30 | 1.78 |
64 | H13 | 2 | 6 | 20 | 200 | 0.1 | 30 | 1.44 |
65 | H13 | 2 | 6 | 20 | 200 | 0.1 | 30 | 2.25 |
66 | H13 | 2 | 6 | 20 | 200 | 0.1 | 30 | 2.94 |
67 | AISI52100 | 2 | 5 | 15 | 100 | 0.1 | 15 | 1.44 |
68 | AISI52100 | 2 | 6 | 25 | 300 | 0.1 | 15 | 1.78 |
69 | AISI52100 | 3 | 5 | 15 | 300 | 0.3 | 30 | 1.78 |
70 | AISI52100 | 3 | 6 | 25 | 100 | 0.2 | 45 | 1.44 |
71 | AISI52100 | 3 | 7 | 25 | 200 | 0.1 | 30 | 2.25 |
72 | AISI52100 | 2 | 6 | 20 | 200 | 0.1 | 30 | 1.78 |
73 | AISI52100 | 2 | 6 | 20 | 200 | 0.1 | 15 | 1.78 |
74 | AISI52100 | 2 | 6 | 20 | 200 | 0.1 | 45 | 1.78 |
75 | AISI52100 | 2 | 6 | 20 | 200 | 0.1 | 60 | 1.78 |
76 | AISI52100 | 2 | 6 | 20 | 200 | 0.1 | 75 | 1.78 |
77 | AISI52100 | 2 | 2 | 20 | 200 | 0.1 | 30 | 1.78 |
78 | AISI52100 | 2 | 4 | 20 | 200 | 0.1 | 30 | 1.78 |
79 | AISI52100 | 2 | 8 | 20 | 200 | 0.1 | 30 | 1.78 |
80 | AISI52100 | 2 | 10 | 20 | 200 | 0.1 | 30 | 1.78 |
81 | AISI52100 | 2 | 6 | 15 | 200 | 0.1 | 30 | 1.78 |
82 | AISI52100 | 2 | 6 | 25 | 200 | 0.1 | 30 | 1.78 |
83 | AISI52100 | 2 | 6 | 30 | 200 | 0.1 | 30 | 1.78 |
84 | AISI52100 | 2 | 6 | 35 | 200 | 0.1 | 30 | 1.78 |
85 | AISI52100 | 2 | 6 | 20 | 200 | 0.2 | 30 | 1.78 |
86 | AISI52100 | 2 | 6 | 20 | 200 | 0.3 | 30 | 1.78 |
87 | AISI52100 | 2 | 6 | 20 | 200 | 0.4 | 30 | 1.78 |
88 | AISI52100 | 2 | 6 | 20 | 200 | 0.5 | 30 | 1.78 |
89 | AISI52100 | 2 | 6 | 20 | 200 | 0.6 | 30 | 1.78 |
90 | AISI52100 | 2 | 6 | 20 | 200 | 0.7 | 30 | 1.78 |
91 | AISI52100 | 2 | 6 | 20 | 100 | 0.1 | 30 | 1.78 |
92 | AISI52100 | 2 | 6 | 20 | 300 | 0.1 | 30 | 1.78 |
93 | AISI52100 | 1 | 6 | 20 | 300 | 0.1 | 30 | 1.78 |
94 | AISI52100 | 3 | 6 | 20 | 300 | 0.1 | 30 | 1.78 |
95 | AISI52100 | 4 | 6 | 20 | 300 | 0.1 | 30 | 1.78 |
96 | AISI52100 | 2 | 6 | 20 | 200 | 0.1 | 30 | 1.44 |
97 | AISI52100 | 2 | 6 | 20 | 200 | 0.1 | 30 | 2.25 |
98 | 25CrMo4 | 2 | 6 | 20 | 200 | 0.3 | 45 | 1.44 |
99 | 25CrMo4 | 2 | 7 | 15 | 200 | 0.1 | 45 | 1.78 |
100 | 25CrMo4 | 3 | 5 | 25 | 200 | 0.2 | 15 | 1.44 |
101 | 25CrMo4 | 3 | 7 | 20 | 100 | 0.3 | 15 | 1.78 |
102 | 25CrMo4 | 2 | 6 | 20 | 300 | 0.1 | 30 | 1.78 |
103 | 25CrMo4 | 2 | 6 | 20 | 200 | 0.1 | 30 | 1.78 |
104 | 25CrMo4 | 2 | 6 | 20 | 200 | 0.1 | 15 | 1.78 |
105 | 25CrMo4 | 2 | 6 | 20 | 200 | 0.1 | 45 | 1.78 |
106 | 25CrMo4 | 2 | 6 | 20 | 200 | 0.1 | 60 | 1.78 |
107 | 25CrMo4 | 2 | 6 | 20 | 200 | 0.1 | 75 | 1.78 |
108 | 25CrMo4 | 2 | 6 | 20 | 200 | 0.1 | 90 | 1.78 |
109 | 25CrMo4 | 2 | 2 | 20 | 200 | 0.1 | 30 | 1.78 |
110 | 25CrMo4 | 2 | 4 | 20 | 200 | 0.1 | 30 | 1.78 |
111 | 25CrMo4 | 2 | 8 | 20 | 200 | 0.1 | 30 | 1.78 |
112 | 25CrMo4 | 2 | 10 | 20 | 200 | 0.1 | 30 | 1.78 |
113 | 25CrMo4 | 2 | 6 | 15 | 200 | 0.1 | 30 | 1.78 |
114 | 25CrMo4 | 2 | 6 | 25 | 200 | 0.1 | 30 | 1.78 |
115 | 25CrMo4 | 2 | 6 | 30 | 200 | 0.1 | 30 | 1.78 |
116 | 25CrMo4 | 2 | 6 | 35 | 200 | 0.1 | 30 | 1.78 |
117 | 25CrMo4 | 2 | 6 | 20 | 200 | 0.2 | 30 | 1.78 |
118 | 25CrMo4 | 2 | 6 | 20 | 200 | 0.3 | 30 | 1.78 |
119 | 25CrMo4 | 2 | 6 | 20 | 200 | 0.4 | 30 | 1.78 |
120 | 25CrMo4 | 2 | 6 | 20 | 200 | 0.5 | 30 | 1.78 |
121 | 25CrMo4 | 2 | 6 | 20 | 200 | 0.6 | 30 | 1.78 |
122 | 25CrMo4 | 2 | 6 | 20 | 200 | 0.7 | 30 | 1.78 |
123 | 25CrMo4 | 2 | 6 | 20 | 100 | 0.1 | 30 | 1.78 |
124 | 25CrMo4 | 2 | 6 | 20 | 300 | 0.1 | 30 | 1.78 |
125 | 25CrMo4 | 1 | 6 | 20 | 300 | 0.1 | 30 | 1.78 |
126 | 25CrMo4 | 3 | 6 | 20 | 300 | 0.1 | 30 | 1.78 |
127 | 25CrMo4 | 4 | 6 | 20 | 300 | 0.1 | 30 | 1.78 |
128 | 25CrMo4 | 2 | 6 | 20 | 200 | 0.1 | 30 | 1.44 |
129 | 25CrMo4 | 2 | 6 | 20 | 200 | 0.1 | 30 | 2.25 |
130 | AISI3310 | 2 | 6 | 20 | 200 | 0.1 | 30 | 1.78 |
131 | AISI3310 | 2 | 6 | 20 | 200 | 0.1 | 15 | 1.78 |
132 | AISI3310 | 2 | 6 | 20 | 200 | 0.1 | 45 | 1.78 |
133 | AISI3310 | 2 | 6 | 20 | 200 | 0.1 | 60 | 1.78 |
134 | AISI3310 | 2 | 6 | 20 | 200 | 0.1 | 75 | 1.78 |
135 | AISI3310 | 2 | 6 | 20 | 200 | 0.1 | 90 | 1.78 |
136 | AISI3310 | 2 | 2 | 20 | 200 | 0.1 | 30 | 1.78 |
137 | AISI3310 | 2 | 4 | 20 | 200 | 0.1 | 30 | 1.78 |
138 | AISI3310 | 2 | 8 | 20 | 200 | 0.1 | 30 | 1.78 |
139 | AISI3310 | 2 | 10 | 20 | 200 | 0.1 | 30 | 1.78 |
140 | AISI3310 | 2 | 6 | 15 | 200 | 0.1 | 30 | 1.78 |
141 | AISI3310 | 2 | 6 | 25 | 200 | 0.1 | 30 | 1.78 |
142 | AISI3310 | 2 | 6 | 30 | 200 | 0.1 | 30 | 1.78 |
143 | AISI3310 | 2 | 6 | 35 | 200 | 0.1 | 30 | 1.78 |
144 | AISI3310 | 2 | 6 | 20 | 200 | 0.2 | 30 | 1.78 |
145 | AISI3310 | 2 | 6 | 20 | 200 | 0.3 | 30 | 1.78 |
146 | AISI3310 | 2 | 6 | 20 | 200 | 0.4 | 30 | 1.78 |
147 | AISI3310 | 2 | 6 | 20 | 200 | 0.5 | 30 | 1.78 |
148 | AISI3310 | 2 | 6 | 20 | 200 | 0.6 | 30 | 1.78 |
149 | AISI3310 | 2 | 6 | 20 | 200 | 0.7 | 30 | 1.78 |
150 | AISI3310 | 2 | 6 | 20 | 100 | 0.1 | 30 | 1.78 |
151 | AISI3310 | 2 | 6 | 20 | 300 | 0.1 | 30 | 1.78 |
152 | AISI3310 | 1 | 6 | 20 | 300 | 0.1 | 30 | 1.78 |
153 | AISI3310 | 3 | 6 | 20 | 300 | 0.1 | 30 | 1.78 |
154 | AISI3310 | 4 | 6 | 20 | 300 | 0.1 | 30 | 1.78 |
155 | AISI3310 | 2 | 6 | 20 | 200 | 0.1 | 30 | 1.44 |
156 | AISI3310 | 2 | 6 | 20 | 200 | 0.1 | 30 | 2.25 |
157 | AISI3310 | 2 | 6 | 20 | 200 | 0.1 | 30 | 2.94 |
References
- Sheng, L.Y.; Du, B.N.; Hu, Z.Y.; Qiao, Y.X.; Xiao, Z.P.; Wang, B.J.; Xu, D.K.; Zheng, Y.F.; Xi, T.F. Effects of annealing treatment on microstructure and tensile behaviour of the Mg-Zn-Y-Nd alloy. J. Magnes. Alloys 2020, 8, 601–613. [Google Scholar] [CrossRef]
- Bermudo, C.; Andersson, T.; Svensson, D.; Trujillo, F.J.; Martín-Béjar, S.; Sevilla, L. Modeling of the fracture energy on the finite element simulation in Ti6Al4V alloy machining. Sci. Rep. 2021, 11, 18490. [Google Scholar] [CrossRef]
- Fernández, D.; Rodríguez-Prieto, A.; Camacho, A.M. Effect of Process Parameters and Definition of Favorable Conditions in Multi-material Extrusion of Bimetallic AZ31B-Ti6Al4V Billets. Appl. Sci. 2020, 10, 8048. [Google Scholar] [CrossRef]
- Fernández, D.; Rodríguez-Prieto, A.; Camacho, A.M. Selection of Die Material and Its Impact on the Multi-Material Extrusion of Bimetallic AZ31B-Ti6Al4V Components for Aeronautical Applications. Materials 2021, 14, 7568. [Google Scholar] [CrossRef]
- Negendanka, M.; Mueller, S.; Reimers, W. Co-extrusion of Mg–Al macrocomposites. J. Mater. Process. Technol. 2021, 212, 1954–1962. [Google Scholar] [CrossRef]
- Gall, S.; Müller, S.; Reimers, W. Aluminum coating of magnesium hollow profiles by using the co-extrusion process. Alum. Int. J. 2009, 85, 63–67. [Google Scholar]
- Rai, R.; Tiwari, M.K.; Ivanov, D.; Dolgui, A. Machine learning in manufacturing and industry 4.0 applications. Int. J. Prod. Res. 2021, 59, 4773–4778. [Google Scholar] [CrossRef]
- Dalzochio, J.; Kunst, R.; Pignaton, E.; Binotto, A.; Sanyal, S.; Favilla, J.; Barbosa, J. Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges. Comput. Ind. 2020, 123, 103298. [Google Scholar] [CrossRef]
- Vapnik, V. The Nature of Statistical Learning Theory; Springer: New York, NY, USA, 1995. [Google Scholar]
- Jović, S.; Radović, A.; Šarkoćević, Ž.; Petković, D.; Alizamir, M. Estimation of the laser cutting operating cost by support vector regression methodology. Appl. Phys. A 2016, 122, 798. [Google Scholar] [CrossRef]
- Rabiee, A.H.; Tahmasbi, V.; Qasemi, M. Experimental evaluation, modeling and sensitivity analysis of temperature and cutting force in bone micro-milling using support vector regression and EFAST methods. Eng. Appl. Artif. Intell. 2023, 120, 105874. [Google Scholar] [CrossRef]
- Xu, C.; Yao, S.; Wang, G.; Wang, Y.; Xu, J. A prediction model of drilling force in CFRP internal chip removal hole drilling based on support vector regression. Int. J. Adv. Manuf. Technol. 2021, 117, 1505–1516. [Google Scholar] [CrossRef]
- Benkedjouh, T.; Medjaher, K.; Zerhouni, N.; Rechak, S. Health assessment and life prediction of cutting tools based on support vector regression. J. Intell. Manuf. 2015, 26, 213–223. [Google Scholar] [CrossRef]
- Rebello, C.M.; Martins, M.A.F.; Santana, D.D.; Rodrigues, A.E.; Loureiro, J.M.; Ribeiro, A.M.; Nogueira, I.B.R. From a Pareto Front to Pareto Regions: A Novel Standpoint for Multiobjective Optimization. Mathematics 2021, 9, 3152. [Google Scholar] [CrossRef]
- Saaty, T.L. A scaling method for priorities in hierarchical structures. J. Math. Psychol. 1977, 15, 234–281. [Google Scholar] [CrossRef]
- Ghaleb, A.M.; Kaid, H.; Alsamhan, A.; Mian, S.H.; Hidri, L. Hindawi Assessment and Comparison of Various MCDM Approaches in the Selection of Manufacturing Process. Adv. Mater. Sci. Eng. 2020, 2020, 4039253. [Google Scholar] [CrossRef]
- Karbassi Yazdi, A.; Tan, Y.; Spulbar, C.; Birau, R.; Alfaro, J. An Approach for Supply Chain Management Contract Selection in the Oil and Gas Industry: Combination of Uncertainty and Multi-Criteria Decision-Making Methods. Mathematics 2022, 10, 3230. [Google Scholar] [CrossRef]
- Rodríguez-Prieto, A.; Camacho, A.M.; Sebastián, M.A. Multi-criteria materials selection for extreme operating conditions base on a multi-objective analysis of irradiation embrittlement and hot cracking prediction models. Int. J. Mech. Mater. Des. 2018, 14, 617–634. [Google Scholar] [CrossRef]
- Alrababah, S.A.A.; Gan, K.H. Effects of the Hybrid CRITIC–VIKOR Method on Product Aspect Ranking in Customer Reviews. Appl. Sci. 2023, 13, 9176. [Google Scholar] [CrossRef]
- Kao, C. Weight determination for consistently ranking alternatives in multiple criteria decision analysis. Appl. Math. Model. 2010, 34, 1779–1787. [Google Scholar] [CrossRef]
- Dev, S.; Aherwar, A.; Patnaik, A. Material Selection for Automotive Piston Component Using Entropy-VIKOR method. Silicon 2020, 12, 155–169. [Google Scholar] [CrossRef]
- Fernández, D.; Rodríguez-Prieto, Á.; Camacho, A.M. Optimal Parameters Selection in Advanced Multi-Metallic Co-Extrusion Based on Independent MCDM Analytical Approaches and Numerical Simulation. Mathematics 2022, 10, 4489. [Google Scholar] [CrossRef]
- Zavadskas, E.K.; Turskis, Z. A new additive ratio assessment (ARAS) method in multicriteria decision-making. Technol. Econ. Dev. Econ. 2010, 16, 159–172. [Google Scholar] [CrossRef]
- Behzadian, M.; Otaghsara, S.K.; Yazdani, M.; Ignatius, J. A state-of the-art survey of TOPSIS applications. Expert Syst. Appl. 2012, 39, 13051–13069. [Google Scholar] [CrossRef]
- Zavadskas, E.K.; Kaklauskas, A.; Peldschus, F.; Turskis, Z. Multi-attribute assessment of road design solutions by using the COPRAS method. Balt. J. Road Bridge Eng. 2007, 2, 195–203. [Google Scholar]
- Pant, S.; Kumar, A.; Ram, M.; Klochkov, Y.; Sharma, H.K. Consistency Indices in Analytic Hierarchy Process: A Review. Mathematics 2022, 10, 1206. [Google Scholar] [CrossRef]
- Narayanamoorthy, S.; Annapoorani, V.; Kalaiselvan, S.; Kang, D. Hybrid Hesitant Fuzzy Multi-Criteria Decision Making Method: A Symmetric Analysis of the Selection of the Best Water Distribution System. Symmetry 2020, 12, 2096. [Google Scholar] [CrossRef]
- Donachie, M.J. Titanium: A Technical Guide; ASM International: Novelty, OH, USA, 1988. [Google Scholar]
- Avedesiam, M.; Baker, H. ASM Speciality Handbook: Magnesium and Magnesium Alloys; ASM International: Novelty, OH, USA, 1999. [Google Scholar]
- Karmakar, D.; Muvvala, G.; Kumar, A. High-temperature abrasive wear characteristics of H13 steel modified by laser remelting and cladded with Stellite 6 and Stellite 6/30% WC. Surf. Coat. Technol. 2021, 422, 127498. [Google Scholar] [CrossRef]
- Li, D.; Zhu, Z.; Xiao, S.; Zhang, G.; Lu, Y. Plastic flow behavior based on thermal activation and dynamic constitutive equation of 25CrMo4 steel during impact compression. Mater. Sci. Eng. A 2017, 707, 459–465. [Google Scholar] [CrossRef]
- Bhandarkar, L.; Behera, M.; Mohanty, P.; Sarangi, S. Experimental investigation and multi-objective optimization of process parameters during machining of AISI 52100 using high performance coated tools. Measurement 2021, 172, 108842. [Google Scholar] [CrossRef]
- Bedekar, V.; Voothaluru, R.; Yu, D.; Wong, A.; Galindo-Nava, E.; Gorti, S.B.; An, K.; Hyde, R.S. Effect of nickel on the kinematic stability of retained austenite in carburized bearing steels—In-situ neutron diffraction and crystal plasticity modeling of uniaxial tension tests in AISI 8620, 4320 and 3310 steels. Int. J. Plast. 2020, 131, 102748. [Google Scholar] [CrossRef]
- Peat, T.; Galloway, A.; Toumpis, A.; Steel, R.; Zhu, W.; Iqbal, N. Enhanced erosion performance of cold spray co-deposited AISI316 MMCs modified by friction stir processing. Mater. Des. 2017, 120, 22–35. [Google Scholar] [CrossRef]
- Davis, J.R. ASM Speciality Handbook—Stainless Steels; ASM International: Novelty, OH, USA, 1999. [Google Scholar]
- Scientific Forming Technologies. DEFORM v11.2 User’s Manual; Scientific Forming Technologies Corporation: Columbus, OH, USA, 2017. [Google Scholar]
- Li, W.; Zhao, G.; Ma, X.; Gao, J. Flow Stress Characteristics of AZ31B Magnesium Alloy Sheet at Elevated Temperatures. Int. J. Appl. Phys. Math. 2012, 2, 83–88. [Google Scholar] [CrossRef]
- Wang, F.; Zhao, J.; Zhu, N.; Li, Z. A comparative study on Johnson—Cook constitutive modelling for Ti6Al4V alloy using automated ball indentation (ABI) technique. J. Alloys Compd. 2015, 633, 220–228. [Google Scholar] [CrossRef]
- Zhang, C.; Zhao, G.; Li, T.; Guan, Y.; Chen, H.; Li, P. An Investigation of Die Wear Behavior during Aluminum Alloy 7075 Tube Extrusion. J. Tribol. 2012, 135, 011602. [Google Scholar] [CrossRef]
- Li, T.; Zhao, G.; Zhang, C.; Guan, Y.; Sun, X.; Li, H. Effect of Process Parameters on Die Wear Behavior of Aluminum Alloy Rod Extrusion. Mater. Manuf. Process. 2013, 28, 312–318. [Google Scholar] [CrossRef]
- Lepadatu, D.; Hambli, R.; Kobi, A.; Barreau, A. Statistical investigation of die wear in metal extrusion processes. Int. J. Adv. Manuf. Technol. 2005, 28, 272–278. [Google Scholar] [CrossRef]
- García-Domínguez, A.; Claver, J.; Camacho, A.M.; Sebastián, M.A. Comparative analysis of extrusion processes by finite element analysis. Procedia Eng. 2015, 100, 74–83. [Google Scholar] [CrossRef]
- Gisbert, C.; Bernal, C.; Camacho, A.M. Improved analytical model for the calculation of forging forces during compression of bimetallic axial assemblies. Procedia Eng. 2015, 132, 298–305. [Google Scholar] [CrossRef]
- Safari, M.; Rabiee, A.H.; Joudaki, J. Developing a Support Vector Regression (SVR) Model for Prediction of Main and Lateral Bending Angles in Laser Tube Bending Process. Materials 2023, 16, 3251. [Google Scholar] [CrossRef]
- Opricovic, S.; Tzeng, G.H. Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. Eur. J. Oper. Res. 2004, 156, 445–455. [Google Scholar] [CrossRef]
- Sasanka, C.T.; Ravindra, K. Implementation of VIKOR Method for Selection of Magnesium Alloy to Suit Automotive Applications. Int. J. Adv. Sci. Technol. 2015, 83, 49–58. [Google Scholar] [CrossRef]
- Raschka, S.; Mirjalili, V. Python Machine Learning. Second Edition; Packt Publishing: Birminghan, UK, 2017. [Google Scholar]
Property | Ti6Al4V | AZ31B |
---|---|---|
Density (g/cm3) | 4.46 | 1.74 |
Tensile strength (MPa) | 895 | 260 |
Yield strength (MPa) | 828 | 200 |
Elastic modulus (GPa) | 110 | 44.80 |
Poisson’s ratio | 0.31 | 0.35 |
Ti (wt.%) | Al (wt.%) | V (wt.%) | Fe (wt.%) | C (wt.%) | O (wt.%) | N (wt.%) | H (wt.%) |
---|---|---|---|---|---|---|---|
Bal. | 5.5–6.5 | 3.5–4.5 | 0.25 | 0.08 | 0.13 | 0.040 | 0.012 |
Mg (wt.%) | Al (wt.%) | Zn (wt.%) | Mn (wt.%) | Si (wt.%) | Cu (wt.%) | Ca (wt.%) | Fe (wt.%) | Ni (wt.%) |
---|---|---|---|---|---|---|---|---|
97 | 2.5–3.5 | 0.6–1.4 | 0.20 | 0.1 | 0.05 | 0.04 | 0.005 | 0.005 |
Material | C (wt.%) | Mn (wt.%) | Si (wt.%) | Cr (wt.%) | Mo (wt.%) | Ni (wt.%) |
---|---|---|---|---|---|---|
AISI316 | 0.08 | 2 | 0.75 | 16–18 | 2–3 | 10–14 |
H13 | 0.32–0.45 | 0.2–0.5 | 0.8–1.20 | 4.75–5.50 | 1.10–1.75 | 0.30 max |
25CrMo4 | 0.22–0.29 | 0.60–0.90 | 0.10–0.40 | 0.90–1.20 | 0.15–0.30 | - |
AISI52100 | 0.1 | 0.45 | 0.26 | 1.51 | 0.06 | 3.39 |
AISI3310 | 0.99 | 0.39 | 0.16 | 1.4 | - | 1.4 |
Property | AISI316 | H13 | 25CrMo4 | AISI52100 | AISI3310 |
---|---|---|---|---|---|
Density (g/cm3) | 8.03 | 7.78 | 7.85 | 7.83 | 7.81 |
Tensile strength (MPa) | 550 | 1990 | 670 | 992 | 1866 |
Yield strength (MPa) | 240 | 1650 | 435 | 579 | 1800 |
Elastic modulus (GPa) | 210 | 210 | 205 | 200 | 210 |
Poisson’s ratio | 0.3 | 0.3 | 0.3 | 0.3 | 0.3 |
Material | Parameters |
---|---|
AISI316 | Extrusion ratio, friction, ram speed, core diameter, billet height, die semi-angle, and temperature. |
H13 | Friction, extrusion ratio, core diameter, billet height, die semi-angle, ram speed, and temperature. |
25CrMo4 | Friction, ram speed, billet height, core diameter, die semi-angle, temperature, and extrusion ratio. |
AISI52100 | Friction, core diameter, die semi-angle, extrusion ratio, billet height, ram speed, and temperature. |
AISI3310 | Ram speed, core diameter, friction, extrusion ratio, die semi-angle, billet height, and temperature. |
Material | R2 |
---|---|
AISI316 | 0.91461 |
H13 | 0.92245 |
25CrMo4 | 0.70708 |
AISI52100 | 0.86922 |
AISI3310 | 0.91966 |
Material | Ranking |
---|---|
AISI316 | 2 |
H13 | 2 |
25CrMo4 | 5 |
AISI52100 | 3 |
AISI3310 | 1 |
Material | Parameters |
---|---|
AISI316 | Friction, ram speed, and temperature. |
H13 | Friction, ram speed, and temperature. |
25CrMo4 | Temperature, friction, and ram speed. |
AISI52100 | Friction, temperature, and ram speed. |
AISI3310 | Temperature, ram speed, and friction. |
Material | R2 |
---|---|
AISI316 | 0.75695 |
H13 | 0.74873 |
25CrMo4 | 0.63223 |
AISI52100 | 0.80571 |
AISI3310 | 0.65881 |
Material | Ranking |
---|---|
AISI316 | 2 |
H13 | 3 |
25CrMo4 | N/A |
AISI52100 | N/A |
AISI3310 | 1 |
Material | Ranking |
---|---|
AISI316 | 2 |
H13 | 3 |
25CrMo4 | 5 |
AISI52100 | 4 |
AISI3310 | 1 |
W. 1(%) | W. 2(%) | W. 3(%) | W. 4(%) | W. 5(%) | W. 6(%) | W. 7(%) | W. 8(%) | W. 9(%) | W. 10(%) | W. 11(%) | W. 12(%) | W. 13(%) | W. 14(%) | W. 15(%) | W. 16(%) | W. 17(%) | W. 18(%) |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0.40 | 0.22 | 0.62 | 0 | 0.03 | 0.02 | 2.66 | 0.10 | 0.11 | 6.16 | 3.88 | 7.41 | 37.56 | 4.30 | 13.69 | 3.56 | 3.61 | 15.68 |
Material | Ranking |
---|---|
AISI316 | 2 |
H13 | 3 |
25CrMo4 | 4 |
AISI52100 | 5 |
AISI3310 | 1 |
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Fernández, D.; Rodríguez-Prieto, Á.; Camacho, A.M. Data-Analytics-Driven Selection of Die Material in Multi-Material Co-Extrusion of Ti-Mg Alloys. Mathematics 2024, 12, 813. https://doi.org/10.3390/math12060813
Fernández D, Rodríguez-Prieto Á, Camacho AM. Data-Analytics-Driven Selection of Die Material in Multi-Material Co-Extrusion of Ti-Mg Alloys. Mathematics. 2024; 12(6):813. https://doi.org/10.3390/math12060813
Chicago/Turabian StyleFernández, Daniel, Álvaro Rodríguez-Prieto, and Ana María Camacho. 2024. "Data-Analytics-Driven Selection of Die Material in Multi-Material Co-Extrusion of Ti-Mg Alloys" Mathematics 12, no. 6: 813. https://doi.org/10.3390/math12060813
APA StyleFernández, D., Rodríguez-Prieto, Á., & Camacho, A. M. (2024). Data-Analytics-Driven Selection of Die Material in Multi-Material Co-Extrusion of Ti-Mg Alloys. Mathematics, 12(6), 813. https://doi.org/10.3390/math12060813