Prediction and Optimization of the Design and Process Parameters of a Hybrid DED Product Using Artificial Intelligence
Abstract
:1. Introduction
2. Numerical Modelling and Validation
2.1. DED Process Design Constraints
2.2. Heat Source Modelling
2.3. CAE Modelling of Hybrid DED Product
2.4. Manufacturing and Physical Tests
2.5. Evaluation of the Results
3. Artificial Neural Networks and Optimization
Artificial Neural Network Application
Algorithm 1. Matlab codes for Artificial Neural Network. |
1·Input: Design Variables [DBW,DBH,DBD,BMT,PP,LP], Targets [TM, SEA, PF] 2·MSE_init = 100,000; 3·training_types = [“trainlm”,“trainscg”]; 4· for learning_rate = 0.01:0.01:1 5· for trainFcns = 1:2 6· create neural network = feedforward 7· train network 8· test network 9· if R_Test > 0.965 and MSE < MSE_init 10· MSE_init=MSE 11· save network 12· else 13· end 14· end 15· end 16·end |
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Nomenclature
ANN | Artificial Neural Network |
DED | Directed Energy Deposition |
DBW | DED Bead Width |
DBD | DED Bead Distance |
PP | Powder/Bead Pattern |
HS/LP | Heat Source/Laser Power |
SEA | Specific Energy Absorption |
Cons | Constraints |
LHS | Latin Hypercube Sampling |
AM | Additive Manufacturing |
TA | Topology Approach |
RSM | Responce Surface Method |
DOF | Degree of Freedom |
CFD | Computational Fluid Dynamics |
GA | Genetic Algorithm |
DBH | DED Bead Height |
BMT | Base Material Thickness |
PF | Peak Force |
TM | Total Mass |
MSE | Mean Squared Error |
Fig | Figure |
PBF | Powder Bed Fusion |
PCM | Phase Change Material |
LA | Lattice Approach |
DOE | Design of Experiment |
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Element | C | Co | Cr | Mn | Mo | N | Ni | P | S | Si |
---|---|---|---|---|---|---|---|---|---|---|
% Mass portion as fixed value | 0.018 | 0.19 | 16.63 | 1.57 | 2.05 | 0.0153 | 11.18 | 0.04 | 0.002 | 0.48 |
Heat Source Power (W) | Laser Scan Speed (mm/sn) | D (mm) | H (mm) | D + H (mm) | Dilution Rate |
---|---|---|---|---|---|
1500 | 12 | 3.368 | 5 | 8.368 | 0.402486 |
1500 | 8 | 3.723 | 5 | 8.723 | 0.426803 |
1300 | 12 | 3.178 | 5 | 8.178 | 0.388604 |
1300 | 8 | 3.589 | 5 | 8.589 | 0.41786 |
1100 | 12 | 3.006 | 5 | 8.006 | 0.375468 |
1100 | 8 | 3.496 | 5 | 8.496 | 0.411488 |
Patterns | Figures of Paterns |
---|---|
Semi Cross Left | |
Semi Cross Right | |
V Shape | |
Full Cross | |
S/N Shape | |
Element | Al | C | Fe | Mn | N | P | S | Si |
---|---|---|---|---|---|---|---|---|
% Mass Portion as fixed value | 0.05 | 0.17 | 98.127 | 1.6 | 0.005 | 0.017 | 0.011 | 0.02 |
Inputs | Outputs |
---|---|
DED Bead Width (DBW) | Total Mass |
DED Bead Height (DBH) | Added DED Mass |
DED Bead Distance (DBD) | Base Material Mass |
Base Material Thickness (BMT) | Specific Energy Absorption |
Bead Patterns (PP) | Peak Force |
Laser Power/Heat Source (LP/HS) | Energy Absorption Ratio |
Number of Parameters (P) | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
2 | 3 | 4 | 5 | 6 | *** | 29 | 30 | 31 | ||
Number of Levels | 2 | L4 | L4 | L8 | L8 | L8 | *** | L32 | L32 | L32 |
3 | L9 | L9 | L9 | L18 | L18 | *** | ||||
4 | L’16 | L’16 | L’16 | L16 | L’32 | *** | ||||
5 | L25 | L25 | L25 | L25 | L25 | *** |
NO | DBW (MM) | DBH (MM) | DBD (MM) | BMT (MM) | PP | LP/HS (W) |
---|---|---|---|---|---|---|
1 | 3 | 5 | 9 | 1.2 | 1 | 1000 |
2 | 3 | 7.5 | 18 | 1.4 | 2 | 1200 |
3 | 3 | 10 | 27 | 1.6 | 3 | 1300 |
4 | 3 | 12.5 | 36 | 1.8 | 4 | 1400 |
5 | 3 | 15 | 45 | 2 | 5 | 1500 |
6 | 4 | 5 | 18 | 1.6 | 4 | 1500 |
7 | 4 | 7.5 | 27 | 1.8 | 5 | 1000 |
8 | 4 | 10 | 36 | 2 | 1 | 1200 |
9 | 4 | 12.5 | 45 | 1.2 | 2 | 1300 |
10 | 4 | 15 | 9 | 1.4 | 3 | 1400 |
11 | 5 | 5 | 27 | 2 | 2 | 1400 |
12 | 5 | 7.5 | 36 | 1.2 | 3 | 1500 |
13 | 5 | 10 | 45 | 1.4 | 4 | 1000 |
14 | 5 | 12.5 | 9 | 1.6 | 5 | 1200 |
15 | 5 | 15 | 18 | 1.8 | 1 | 1300 |
16 | 6 | 5 | 36 | 1.4 | 5 | 1300 |
17 | 6 | 7.5 | 45 | 1.6 | 1 | 1400 |
18 | 6 | 10 | 9 | 1.8 | 2 | 1500 |
19 | 6 | 12.5 | 18 | 2 | 3 | 1000 |
20 | 6 | 15 | 27 | 1.2 | 4 | 1200 |
21 | 7 | 5 | 45 | 1.8 | 3 | 1200 |
22 | 7 | 7.5 | 9 | 2 | 4 | 1300 |
23 | 7 | 10 | 18 | 1.2 | 5 | 1400 |
24 | 7 | 12.5 | 27 | 1.4 | 1 | 1500 |
25 | 7 | 15 | 36 | 1.6 | 2 | 1000 |
NO | DBW (MM) | DBH (MM) | DBD (MM) | BMT (MM) | PP | LP/HS (W) |
---|---|---|---|---|---|---|
1 | 6.91 | 5.37 | 36.81 | 1.1 | 4 | 1053 |
2 | 6.45 | 13.98 | 15.3 | 1.69 | 2 | 1266 |
3 | 3.36 | 10.09 | 10.97 | 1.51 | 5 | 1151 |
4 | 5.54 | 14.2 | 40.58 | 1.8 | 2 | 1362 |
5 | 4.53 | 6.25 | 27.71 | 1.33 | 5 | 1333 |
6 | 6.18 | 12.19 | 30.65 | 1.42 | 4 | 1217 |
7 | 4.71 | 11.62 | 26.89 | 1.95 | 3 | 1038 |
8 | 5.26 | 9.38 | 21.47 | 1.21 | 1 | 1485 |
9 | 3.54 | 8.43 | 19.49 | 1.01 | 1 | 1116 |
10 | 4.05 | 7.47 | 42.11 | 1.84 | 3 | 1424 |
11 | 3.08 | 11.65 | 37.19 | 1.53 | 1 | 1327.64 |
DBW | DBH | DBD | BMT | PP | LP/HS |
---|---|---|---|---|---|
3.53066 | 15 | 43.9946 | 1.2 | 5 | 1500 |
DOE RSM Prediction | CAE/CAD Results | Correlation (% error) | |
---|---|---|---|
FORCE (kN) | 7.115 | 7.951 | 11.75% |
ENERGY (Joule) | 3.889 | 2.877 | −26.15% |
TOTAL MASS (kg) | 0.464 | 0.5636 | 21.46% |
Energy (kJ) | Total Mass (kg) | Force (kN) | |
---|---|---|---|
Type | Feed-forward | Feed-forward | Feed-forward |
Training function | Scaled Conjugate Gradient | Levenberg–Marquardt | Levenberg–Marquardt |
Learning rate | 0.11 | 0.34 | 0.92 |
Number of hidden layer | 1 | 1 | 1 |
Number of neuron | 9 | 4 | 10 |
Number of epochs | 41 | 15 | 7 |
DBW | DBH | DBD | BMT | PP | LP/HS |
---|---|---|---|---|---|
4.3542 | 5.8669 | 45 | 1.6724 | 1 | 1342.10 |
ANN Prediction (ANN + GA) | CAE/CAD Results | Correlation (% Error) | |
---|---|---|---|
FORCE (kN) | 9.009 | 10.70 | 18.86% |
ENERGY (Joule) | 4.794 | 4.232 | −11.72% |
TOTAL MASS (kg) | 0.601 | 0.6055 | 0.75% |
DOE RSM Error | ANN + GA Error | % Improvement or Deterioration with ANN + GA | |
---|---|---|---|
FORCE (kN) | 11.75% | 18.86% | −60.51 |
ENERGY (Joule) | −26.51% | −11.72% | +55.79 |
TOTAL MASS (kg) | 21.46% | 0.75% | +96.50 |
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Çallı, M.; Albak, E.İ.; Öztürk, F. Prediction and Optimization of the Design and Process Parameters of a Hybrid DED Product Using Artificial Intelligence. Appl. Sci. 2022, 12, 5027. https://doi.org/10.3390/app12105027
Çallı M, Albak Eİ, Öztürk F. Prediction and Optimization of the Design and Process Parameters of a Hybrid DED Product Using Artificial Intelligence. Applied Sciences. 2022; 12(10):5027. https://doi.org/10.3390/app12105027
Chicago/Turabian StyleÇallı, Metin, Emre İsa Albak, and Ferruh Öztürk. 2022. "Prediction and Optimization of the Design and Process Parameters of a Hybrid DED Product Using Artificial Intelligence" Applied Sciences 12, no. 10: 5027. https://doi.org/10.3390/app12105027
APA StyleÇallı, M., Albak, E. İ., & Öztürk, F. (2022). Prediction and Optimization of the Design and Process Parameters of a Hybrid DED Product Using Artificial Intelligence. Applied Sciences, 12(10), 5027. https://doi.org/10.3390/app12105027