Environmental, Economical and Technological Analysis of MQL-Assisted Machining of Al-Mg-Zr Alloy Using PCD Tool
Abstract
:1. Introduction
- To determine the influence of cutting parameters on sustainable responses, viz. surface roughness and cutting temperature using ANOVA and the main effect of plots using the PCD tool;
- To achieve optimal cutting parameters with the Taguchi method and multi-objective optimization with desirability function analysis (DFA);
- To model cutting parameters with ANN and ANFIS;
- To analyze life cycle and sustainability aspects of MQL-assisted machining of Al-Mg-Zr alloy.
- Q1: Do cutting parameters significantly affect sustainable responses or not?
- Q2: Do the cutting parameters differ from optimal settings?
- Q3: Does a statistically significant interrelationship between parameters contribute to “sustainable machining”?
2. Materials and Methods
2.1. Workpiece Material and Dimensions
2.2. Cutting Inserts
2.3. Equipment and Instruments
2.4. Experimental Setup and Design of Experimentation
2.5. ANFIS and ANN Based Predictive Modelling
2.6. Data Collection for Sustainability Analysis
3. Results and Discussion
3.1. Effect of Process Paramters on Surface Roughness and Cutting Temprature
3.2. Analysis of Variance (ANOVA)
3.3. Optimization Using Desirability Function Analysis (DFA)
3.4. Proposed ANN Model
3.5. ANFIS Based Predictive Modeling
3.6. Three-Dimensional Surface Plots
4. Comparative Analysis
4.1. Comparison of Experimental Data with ANN and ANFIS Predicted Model
4.2. Life Cycle Assessment and Sustainability Analysis
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Element | Weight % | Atomic % |
---|---|---|
Mg | 34.81 | 51.67 |
O | 11.34 | 12.63 |
Al | 52.18 | 34.47 |
Zr | 1.67 | 1.22 |
Variable 1 | Variable 2 | Variable 3 | Response 1 | Response 2 | |
---|---|---|---|---|---|
Run | t: Depth of Cut (mm) | Vc: Cutting Speed (m/min) | S0: Feed Rate (mm/rev.) | Ra: Surface Roughness (µm) | θ: Cutting Temperature (°C) |
1 | 0.25 | 86 | 0.1 | 0.508 | 135.79 |
2 | 0.25 | 112 | 0.12 | 0.625 | 152.24 |
3 | 0.25 | 138 | 0.14 | 0.784 | 173.45 |
4 | 0.25 | 143 | 0.16 | 0.815 | 177.53 |
5 | 0.25 | 178 | 0.18 | 1.030 | 206.09 |
6 | 0.45 | 86 | 0.12 | 0.49 | 134.66 |
7 | 0.45 | 112 | 0.14 | 0.735 | 165.50 |
8 | 0.45 | 138 | 0.16 | 0.894 | 186.77 |
9 | 0.45 | 143 | 0.18 | 0.925 | 190.85 |
10 | 0.45 | 178 | 0.1 | 1.140 | 219.41 |
11 | 0.65 | 86 | 0.14 | 0.728 | 162.43 |
12 | 0.65 | 112 | 0.16 | 0.845 | 178.88 |
13 | 0.65 | 138 | 0.18 | 1.004 | 200.09 |
14 | 0.65 | 143 | 0.1 | 1.035 | 204.17 |
15 | 0.65 | 178 | 0.12 | 1.250 | 232.73 |
16 | 0.85 | 86 | 0.16 | 0.795 | 170.98 |
17 | 0.85 | 112 | 0.18 | 0.955 | 192.20 |
18 | 0.85 | 138 | 0.1 | 1.114 | 213.41 |
19 | 0.85 | 143 | 0.12 | 1.145 | 217.49 |
20 | 0.85 | 178 | 0.14 | 1.360 | 246.05 |
21 | 1.25 | 86 | 0.18 | 1.015 | 197.62 |
22 | 1.25 | 112 | 0.1 | 1.175 | 218.84 |
23 | 1.25 | 138 | 0.12 | 1.334 | 240.05 |
24 | 1.25 | 143 | 0.14 | 1.365 | 244.13 |
25 | 1.25 | 178 | 0.16 | 1.580 | 272.69 |
Variables | Units | Level 1 | Level 2 | Level 3 | Level 4 | Level 5 |
---|---|---|---|---|---|---|
Depth of cut (t) | mm | 0.25 | 0.45 | 0.65 | 0.85 | 1.25 |
Cutting speed (Vc) | m/min | 86 | 112 | 138 | 143 | 178 |
Feed rate (S0) | mm/rev. | 0.1 | 0.12 | 0.14 | 0.16 | 0.18 |
Level | Depth of Cut | Cutting Speed | Feed Rate |
---|---|---|---|
1 | 0.7528 | 0.7074 | 0.9948 |
2 | 0.8372 | 0.8671 | 0.9692 |
3 | 0.9728 | 1.0268 | 0.9948 |
4 | 1.0743 | 1.0575 | 0.9863 |
5 | 1.2943 | 1.2724 | 0.9863 |
Delta | 0.5415 | 0.5650 | 0.0256 |
Rank | 2 | 1 | 3 |
Level | Depth of Cut | Cutting Speed | Feed Rate |
---|---|---|---|
1 | −44.47 | −44.01 | −45.81 |
2 | −44.97 | −45.11 | −45.59 |
3 | −45.77 | −46.09 | −45.79 |
4 | −46.30 | −46.26 | −45.77 |
5 | −47.36 | −47.40 | −45.90 |
Delta | 2.89 | 3.39 | 0.32 |
Rank | 2 | 1 | 3 |
Source | SS | df | MS | F-Value | p-Value | Contribution |
---|---|---|---|---|---|---|
Model | 1.80 | 3 | 0.5995 | 1152.52 | <0.0001 | significant |
t | 0.8956 | 1 | 0.8956 | 1721.90 | <0.0001 | 49.48% |
Vc | 0.9028 | 1 | 0.9028 | 1735.65 | <0.0001 | 49.87% |
S0 | 7.688× 10−9 | 1 | 7.688 × 10−9 | 0.0000 | 0.9970 | |
Residual | 0.0109 | 21 | 0.0005 | |||
Cor. Total | 1.81 | 24 |
Source | SS | df | MS | F-value | p-Value | Contribution |
---|---|---|---|---|---|---|
Model | 29,087.40 | 3 | 9695.80 | 1462.35 | <0.0001 | significant |
t | 13,136.15 | 1 | 13,136.15 | 1981.23 | <0.0001 | 44.94% |
Vc | 15,951.25 | 1 | 15,951.25 | 2405.82 | <0.0001 | 54.57% |
S0 | 0.0005 | 1 | 0.0005 | 0.0001 | 0.9932 | |
Residual | 139.24 | 21 | 6.63 | |||
Cor. Total | 29,226.64 | 24 |
Factor | Goal | Limit | Weight | Importance | ||
---|---|---|---|---|---|---|
Low | High | Low | High | |||
t | is in range | 0.25 | 1.25 | 1 | 1 | 3 |
Vc | minimize | 86 | 178 | 1 | 1 | 3 |
S0 | is in range | 0.1 | 0.18 | 1 | 1 | 3 |
Ra | minimize | 0.49 | 1.58042 | 1 | 1 | 4 |
θ | is in range | 134.66 | 272.698 | 1 | 1 | 4 |
No. | t | Vc | S0 | Ra | θ | Desirability | |
---|---|---|---|---|---|---|---|
1 | 0.305 | 86.000 | 0.180 | 0.496 | 134.660 | 0.997 | Selected |
2 | 0.264 | 89.307 | 0.180 | 0.494 | 134.660 | 0.983 | |
3 | 0.396 | 86.000 | 0.180 | 0.546 | 140.719 | 0.970 | |
4 | 0.414 | 86.000 | 0.180 | 0.555 | 141.906 | 0.965 | |
5 | 0.445 | 86.000 | 0.100 | 0.573 | 143.992 | 0.956 | |
6 | 0.504 | 86.596 | 0.100 | 0.609 | 148.422 | 0.933 | |
7 | 0.573 | 89.931 | 0.100 | 0.667 | 155.706 | 0.887 |
Learning Algorithm | No. of Neurons | Training Data | Testing Data | ||
---|---|---|---|---|---|
R2 | R2 | ||||
Ra | θ | Ra | θ | ||
LM | 9 | 0.99732 | 0.99847 | 0.99643 | 0.9964 |
LM | 12 | 0.99612 | 0.99727 | 0.9915 | 0.9927 |
LM | 15 | 0.99103 | 0.99218 | 0.9574 | 0.9584 |
LM | 20 | 0.98431 | 0.98546 | 0.96487 | 0.96597 |
LM | 24 | 0.96356 | 0.96471 | 0.97672 | 0.97773 |
CGP | 9 | 0.99242 | 0.99357 | 0.9943 | 0.9973 |
CGP | 12 | 0.99185 | 0.9928 | 0.9915 | 0.9927 |
CGP | 15 | 0.99151 | 0.99266 | 0.99388 | 0.9948 |
CGP | 20 | 0.99136 | 0.99251 | 0.99467 | 0.9956 |
CGP | 24 | 0.99145 | 0.99260 | 0.99521 | 0.9964 |
SCG | 9 | 0.99137 | 0.99252 | 0.99488 | 0.9974 |
SCG | 12 | 0.99156 | 0.99271 | 0.9944 | 0.9967 |
SCG | 15 | 0.99199 | 0.9929 | 0.99464 | 0.9976 |
SCG | 20 | 0.99161 | 0.99271 | 0.99352 | 0.9975 |
SCG | 24 | 0.99182 | 0.99286 | 0.99351 | 0.99658 |
BFG | 9 | 0.99208 | 0.99328 | 0.99638 | 0.9973 |
BFG | 12 | 0.99215 | 0.99415 | 0.99631 | 0.9971 |
BFG | 15 | 0.99187 | 0.99297 | 0.99563 | 0.9966 |
BFG | 20 | 0.99171 | 0.99276 | 0.99532 | 0.9962 |
BFG | 24 | 0.99181 | 0.9928 | 0.99373 | 0.9947 |
Run | Exp. Result | ANN Predicted Result | ANFIS Predicted Result | |||
---|---|---|---|---|---|---|
Ra | θ | Ra | θ | Ra | θ | |
(μm) | (°C) | (μm) | (°C) | (μm) | (°C) | |
1 | 0.508 | 135.79 | 0.49 | 132.34 | 0.508 | 133 |
2 | 0.625 | 152.24 | 0.611 | 151.34 | 0.625 | 152 |
3 | 0.784 | 173.45 | 0.654 | 170.89 | 0.735 | 173 |
4 | 0.815 | 177.53 | 0.8 | 174.23 | 0.816 | 175 |
5 | 1.030 | 206.09 | 1.025 | 197.66 | 1.03 | 206 |
6 | 0.49 | 134.66 | 0.523 | 133.62 | 0.49 | 135 |
7 | 0.735 | 165.50 | 0.687 | 156.26 | 0.735 | 162 |
8 | 0.894 | 186.77 | 0.882 | 181.88 | 0.872 | 184 |
9 | 0.925 | 190.85 | 0.911 | 188.22 | 0.926 | 187 |
10 | 1.140 | 219.41 | 1.09 | 211.9 | 1.14 | 217 |
11 | 0.728 | 162.43 | 0.644 | 151.44 | 0.728 | 154 |
12 | 0.845 | 178.88 | 0.836 | 169.13 | 0.845 | 175 |
13 | 1.004 | 200.09 | 0.956 | 195.1 | 0.96 | 200 |
14 | 1.035 | 204.17 | 1.043 | 200.76 | 1.04 | 202 |
15 | 1.250 | 232.73 | 1.2 | 225.43 | 1.25 | 227 |
16 | 0.795 | 170.98 | 0.77 | 166.98 | 0.796 | 171 |
17 | 0.955 | 192.20 | 0.946 | 183.32 | 0.955 | 192 |
18 | 1.114 | 213.41 | 1.034 | 206.66 | 1.11 | 213 |
19 | 1.145 | 217.49 | 1.126 | 209 | 1.15 | 217 |
20 | 1.360 | 246.05 | 1.33 | 237.44 | 1.36 | 246 |
21 | 1.015 | 197.62 | 0.936 | 188.74 | 1.004 | 195 |
22 | 1.175 | 218.84 | 1.12 | 211.76 | 1.155 | 214 |
23 | 1.334 | 240.05 | 1.304 | 222.13 | 1.33 | 240 |
24 | 1.365 | 244.13 | 1.311 | 237.56 | 1.34 | 244 |
25 | 1.580 | 272.69 | 1.56 | 264 | 1.58 | 273 |
MAPE | 3.95 | 3.45 | 1.072 | 1.172 |
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Karim, M.R.; Tariq, J.B.; Morshed, S.M.; Shawon, S.H.; Hasan, A.; Prakash, C.; Singh, S.; Kumar, R.; Nirsanametla, Y.; Pruncu, C.I. Environmental, Economical and Technological Analysis of MQL-Assisted Machining of Al-Mg-Zr Alloy Using PCD Tool. Sustainability 2021, 13, 7321. https://doi.org/10.3390/su13137321
Karim MR, Tariq JB, Morshed SM, Shawon SH, Hasan A, Prakash C, Singh S, Kumar R, Nirsanametla Y, Pruncu CI. Environmental, Economical and Technological Analysis of MQL-Assisted Machining of Al-Mg-Zr Alloy Using PCD Tool. Sustainability. 2021; 13(13):7321. https://doi.org/10.3390/su13137321
Chicago/Turabian StyleKarim, Md. Rezaul, Juairiya Binte Tariq, Shah Murtoza Morshed, Sabbir Hossain Shawon, Abir Hasan, Chander Prakash, Sunpreet Singh, Raman Kumar, Yadaiah Nirsanametla, and Catalin I. Pruncu. 2021. "Environmental, Economical and Technological Analysis of MQL-Assisted Machining of Al-Mg-Zr Alloy Using PCD Tool" Sustainability 13, no. 13: 7321. https://doi.org/10.3390/su13137321
APA StyleKarim, M. R., Tariq, J. B., Morshed, S. M., Shawon, S. H., Hasan, A., Prakash, C., Singh, S., Kumar, R., Nirsanametla, Y., & Pruncu, C. I. (2021). Environmental, Economical and Technological Analysis of MQL-Assisted Machining of Al-Mg-Zr Alloy Using PCD Tool. Sustainability, 13(13), 7321. https://doi.org/10.3390/su13137321