Investigation of Heat Annealing and Parametric Optimization for Drilling of Monel-400 Alloy
Abstract
:1. Introduction
2. Materials and Methods
3. Results and Discussion
3.1. Hardness Analysis
3.2. Tool Wear Analysis
3.3. ANOVA Analysis
3.4. Main Effect and Surface Plots for Fz and Ra of Monel-400 at Room Temperature
3.5. Optimization Results
4. Conclusions
- The cutting force (Fz) and surface roughness (Ra and Rt) can be reduced by 33%, 25%, and 31%, respectively after annealing at 700 °C compared to the results of drilled Monel-400 at room temperature.
- The maximum improvement can reach 42% of Fz, 35% of Ra, and 59% while annealing the Monel-400 at 1000 °C.
- A significant reduction in the tool wear for machining the annealed material can be obtained, which minimizes the tooling cost and overall machining cost.
- With an increase in feed rate and a drop in spindle speed, cutting force and surface roughness increase.
- In terms of cutting force, the spindle speed has the greatest impact, while the feed rate has the most impact on surface roughness.
- Multi-objective optimization method (MOGA) is accomplished to minimize both the cutting force (Fz) and surface roughness (Ra and Rt).
- The present research offers the optimal combination of drilling parameters to obtain high-quality and accurate holes in Monel-400 alloy. The optimal drilling parameters are cutting speed of 4250–4500 rpm and feed rate of 8–10 mm/min, yielding the key responses as Fz = 388–466 N, Ra = 0.0.17–0.19 μm, and Rt = 3–3.5 μm.
- The verification results show great agreement with the MOGA results. This means that the MOGA is a good tool for optimizing the drilling or any machining process parameters.
- In future work, more input parameters (different tool and coolant types) and more responses (physical properties, edge chipping, heat-affected zone, and dimensional accuracy of the drilled holes) will be included.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Thellaputta, G.R.; Chandra, P.S.; Rao, C. Machinability of Nickel Based Superalloys: A Review. Mater. Today Proc. 2017, 4, 3712–3721. [Google Scholar] [CrossRef]
- Arunachalam, R.; Mannan, M. Machinability of nickel-based high temperature alloys. Mach. Sci. Technol. 2000, 4, 127–168. [Google Scholar] [CrossRef]
- Monel Alloy 400. Available online: www.specialmetals.com (accessed on 29 August 2023).
- Shoemaker, L.E.; Smith, G.D. Nickel: A Century of Innovation A Century of Monel Metal: 1906–2006. J. Miner. Met. Mater. Soc. 2006, 58, 18–20. [Google Scholar] [CrossRef]
- Shihan, M.; Chandradass, J.; Kannan, T.T.M.; Sivagami, S.M. Proceedings Machining Feasibility and Sustainability Study on End Milling Process of Monel Alloy. Mater. Today Proc. 2021, 45, 7162–7165. [Google Scholar] [CrossRef]
- Rivero, A.; Aramendi, G.; Herranz, S.; López de Lacalle, L.N. An Experimental Investigation of the Effect of Coatings and Cutting Parameters on the Dry Drilling Performance of Aluminium Alloys. Int. J. Adv. Manuf. Technol. 2006, 28, 1–11. [Google Scholar] [CrossRef]
- Rodríguez-Barrero, S.; Fernández-Larrinoa, J.; Azkona, I.; López De Lacalle, L.N.; Polvorosa, R. Enhanced Perfor-mance of Nanostructured Coatings for Drilling by Droplet Elimination. Mater. Manuf. Process. 2016, 31, 593–602. [Google Scholar] [CrossRef]
- Kumar, V.; Kumar, V.; Jangra, K.K. An Experimental Analysis and Optimization of Machining Rate and Surface Characteristics in WEDM of Monel-400 Using RSM and Desirability Approach. J. Ind. Eng. Int. 2015, 11, 297–307. [Google Scholar] [CrossRef]
- Manikandan, K.; Ranjith, P.; Raj, D.; Palanikumar, K. Machinability Evaluation and Comparison of Incoloy 825, In-conel 603 XL, Monel K400 and Inconel 600 Super Alloys in Wire Electrical. Integr. Med. Res. 2020, 9, 12260–12272. [Google Scholar]
- Mahalingam, M.; Varahamoorthi, R. Materials Today: Proceedings Investigation on Tool Wear Rate of Brass Tool during Machining of Monel 400 Alloy Using Electric Discharge Machine. Mater. Today Proc. 2020, 26, 1213–1220. [Google Scholar] [CrossRef]
- Patil, D.H.; Mudigonda, S. Investigation on effect of grain orientation in photochemical machining of Monel 400. Mater. Manuf. Process. 2017, 32, 1831–1837. [Google Scholar] [CrossRef]
- Parida, A.K.; Maity, K. Comparison the machinability of Inconel 718, Inconel 625 and Monel 400 in hot turning operation. Eng. Sci. Technol. Int. J. 2018, 21, 364–370. [Google Scholar] [CrossRef]
- Sinha, A.; Rao, P.S.; Khan, M.Y.; Singh, G. A Comprehensive Study of Electrochemical Machining of Monel-400 Al-loy with Different Electrolytes: Research Challenges. Mater. Today Proc. 2023. [Google Scholar] [CrossRef]
- Nagarajan, V.; Solaiyappan, A.; Mahalingam, S.K.; Nagarajan, L.; Salunkhe, S.; Nasr, E.A.; Shanmugam, R.; Hussein, H.M.A.M. Meta-Heuristic Technique-Based Parametric Optimization for Electrochemical Machining of Monel 400 Alloys to Investigate the Material Removal Rate and the Sludge. Appl. Sci. 2022, 12, 2793. [Google Scholar] [CrossRef]
- Kukliński, M.; Przestacki, D.; Bartkowska, A.; Kieruj, P.; Radek, N. Conventional and laser-assisted machining of laser-borided Monel 400 alloy. Int. J. Adv. Manuf. Technol. 2023, 126, 5677–5687. [Google Scholar] [CrossRef]
- Bartkowska, A.; Bartkowski, D.; Przestacki, D.; Kukliński, M.; Miklaszewski, A.; Kieruj, P. Laser Processing of Diffusion Boronized Layer Produced on Monel® Alloy 400—Microstructure, Microhardness, Corrosion and Wear Re-sistance Tests. Materials 2021, 14, 7529. [Google Scholar] [CrossRef]
- Kumar, M.S.; Rajamani, D.; Nasr, E.A.; Balasubramanian, E.; Mohamed, H.; Astarita, A. A Hybrid Approach of Anfis—Artificial Bee Colony Algorithm for Intelligent Modeling and Optimization of Plasma Arc Cutting on MonelTM 400 Alloy. Materials 2021, 14, 6373. [Google Scholar] [CrossRef]
- Kukliński, M.; Bartkowska, A.; Przestacki, D. Microstructure and selected properties of Monel 400 alloy after laser heat treatment and laser boriding using diode laser. Int. J. Adv. Manuf. Technol. 2018, 98, 3005–3017. [Google Scholar] [CrossRef]
- Rajamani, D.; Ananthakumar, K.; Balasubramanian, E.; Paulo Davim, J. Experimental Investigation and Optimi-zation of PAC Parameters on Monel 400TM Superalloy. Mater. Manuf. Process. 2018, 33, 1864–1873. [Google Scholar] [CrossRef]
- Ananthakumar, K.; Rajamani, D.; Balasubramanian, E.; Davim, J.P. Measurement and Optimization of Mul-ti-Response Characteristics in Plasma Arc Cutting of Monel 400 TM Using RSM and TOPSIS. Measurement 2019, 135, 725–737. [Google Scholar] [CrossRef]
- Kumar, P.M.; Sivakumar, K.; Jayakumar, N. Multiobjective Optimization and Analysis of Copper–Titanium Diboride Electrode in EDM of Monel 400TM Alloy. Mater. Manuf. Process. 2018, 33, 1429–1437. [Google Scholar] [CrossRef]
- Qadri, S.I.A.; Harmain, G.A.; Wani, M.F. The effect of cutting speed and work piece hardness on turning performance of nickel based super Alloy-718 using ceramic cutting inserts. Eng. Res. Express 2019, 2, 025018. [Google Scholar] [CrossRef]
- Machno, M. Impact of Process Parameters on the Quality of Deep Holes Drilled in Inconel 718 Using EDD. Materials 2019, 12, 2298. [Google Scholar] [CrossRef] [PubMed]
- Baghlani, V.; Mehbudi, P.; Akbari, J.; Sohrabi, M. Ultrasonic Assisted Deep Drilling of Inconel 738LC Superalloy. Procedia CIRP 2013, 6, 571–576. [Google Scholar] [CrossRef]
- Polvorosa, R.; Suárez, A.; de Lacalle, L.N.L.; Cerrillo, I.; Wretland, A.; Veiga, F. Tool wear on nickel alloys with different coolant pressures: Comparison of Alloy 718 and Waspaloy. J. Manuf. Process. 2017, 26, 44–56. [Google Scholar] [CrossRef]
- Amigo, F.J.; Urbikain, G.; de Lacalle, L.N.L.; Fernández-Lucio, P.; Pereira, O.; Fernández-Valdivielso, A. On the effects of cutting-edge angle on high-feed turning of Inconel 718© superalloy. Int. J. Adv. Manuf. Technol. 2023, 125, 4237–4252. [Google Scholar] [CrossRef]
- Jayakumar, K.; Akash Koundinya, K.; Jayakumar, T.; Harshal, M.; Gopinath, G. Experimental Studies on the Ef-fect of Drilling Parameters on Monel Alloy. Mater. Sci. Forum 2020, 979, 137–141. [Google Scholar] [CrossRef]
- Tiwari, V.; Mishr, D.R. Multi Response Optimization of EDM Paramerers for Monel K-500. Int. J. Adv. Prod. Mech. Eng. 2016, 2, 53–60. [Google Scholar]
- Sonawane, S.A.; Kulkarni, M. Optimization of machining parameters of WEDM for Nimonic-75 alloy using principal component analysis integrated with Taguchi method. J. King Saud Univ. Eng. Sci. 2018, 30, 250–258, Erratum in J. King Saud Univ. Eng. Sci. 2020, 32, 561–562. [Google Scholar] [CrossRef]
- Korkmaz, M.E.; Verleysen, P.; Günay, M. Identification of Constitutive Model Parameters for Nimonic 80A Superalloy. Trans. Indian Inst. Met. 2018, 71, 2945–2952. [Google Scholar] [CrossRef]
- High Temp Metals. Available online: https://www.hightempmetals.com/techdata/hitempmonel400data.php (accessed on 29 August 2023).
- Ruchiyat, A.; Anhar, M.; Yusuf, Y.; Polonia, B.S. The Effect of Heating Temperature on the Hardness, Microstruc-ture and V-Bending Spring Back Results on Commercial Steel Plate. J. Appl. Eng. Technol. Sci. 2019, 1, 1–16. [Google Scholar]
- Sousa, V.F.C.; Castanheira, J.; Silva, F.J.G.; Fecheira, J.S.; Pinto, G.; Baptista, A. Wear Behavior of Uncoated and Coated Tools in Milling Operations of AMPCO (Cu-Be) Alloy. Appl. Sci. 2021, 11, 7762. [Google Scholar] [CrossRef]
- Khanafer, K.; Eltaggaz, A.; Deiab, I.; Agarwal, H.; Abdul-Latif, A. Toward sustainable micro-drilling of Inconel 718 superalloy using MQL-Nanofluid. Int. J. Adv. Manuf. Technol. 2020, 107, 3459–3469. [Google Scholar] [CrossRef]
- Dhananchezian, M. Experimental investigation on dry turned Monel 400 alloy surface parameters with uncoated and coated tool. Mater. Today Proc. 2021, 46, 8303–8306. [Google Scholar] [CrossRef]
- Chintakindi, S.; Alsamhan, A.; Abidi, M.H.; Kumar, M.P. Annealing of Monel 400 Alloy Using Principal Component Analysis, Hyper-Parameter Optimization, Machine Learning Techniques, and Multi-Objective Particle Swarm Optimization. Int. J. Comput. Intell. Syst. 2022, 15, 18. [Google Scholar] [CrossRef]
- Abdo, B.M.A.; Anwar, S.; El-tamimi, A.M.; Nasr, E.A. Experimental Analysis on the Influence and Optimiza-tion of µ-RUM Parameters in Machining Alumina Bioceramic. Materials 2019, 12, 616. [Google Scholar] [CrossRef]
- Abdo, B.M.A.; El-Tamimi, A.M.; Anwar, S.; Umer, U.; Alahmari, A.M.; Ghaleb, M.A. Experimental Investigation and Multi-Objective Optimization of Nd:YAG Laser Micro-Channeling Process of Zirconia Dental Ceramic. Int. J. Adv. Manuf. Technol. 2018, 98, 2213–2230. [Google Scholar] [CrossRef]
- Guo, Y.; Wang, L.; Zhang, G.; Hou, P. Multi-response optimization of the electrical discharge machining of insulating zirconia. Mater. Manuf. Process. 2016, 32, 294–301. [Google Scholar] [CrossRef]
- Parida, A.K.; Maity, K. Modeling of machining parameters affecting flank wear and surface roughness in hot turning of Monel-400 using response surface methodology (RSM). Measurement 2019, 137, 375–381. [Google Scholar] [CrossRef]
Element | Nickel, Ni | Copper, Cu | Iron, Fe | Manganese, Mn | Silicon, Si | Carbon, C | Sulfur, S |
---|---|---|---|---|---|---|---|
Weight (%) | 64 | 28–34 | 2.5 | 2 | 0.5 | 0.3 | 0.024 |
Property (Metric) | Value | Unit |
---|---|---|
Density | 8.80 × 103 | kg/m3 |
Modulus of Elasticity | 179 | GPa |
Thermal Expansion | 13.9 × 10−6 | °C−1 |
Thermal Conductivity | 21.8 | W/(m · K) |
Yield Strength | 240 | MPa |
Elongation | 48 | % |
Liquids Temperature | 1350 | °C |
Solidus Temperature | 1300 | °C |
Drilling Tool | Property/Value |
---|---|
Cutting edge material | Carbide |
Coating/finish | TiAlN |
Diameter | 10 mm |
Cutting depth | 30 mm |
Grade | R458 |
Point angle | 140 Degree |
Rake angle | 30 Degree |
Relief angle | 8 Degree |
Factors | Levels | ||
---|---|---|---|
Speed (rpm) | 1500 | 3000 | 4500 |
Feed rate (mm/min) | 8 | 24 | 40 |
Fixed parameters | |||
Coolant | ECO COOL S-HL | ||
Tool type | Solid Carbide | ||
Tool Dia. | 10 mm |
Exp. # | Block | Speed | Feed |
---|---|---|---|
1 | 1 | 1500 | 8 |
2 | 1 | 1500 | 40 |
3 | 1 | 1500 | 24 |
4 | 1 | 4500 | 24 |
5 | 1 | 3000 | 40 |
6 | 1 | 3000 | 24 |
7 | 1 | 4500 | 40 |
8 | 1 | 4500 | 8 |
9 | 1 | 3000 | 8 |
10 | 2 | 4500 | 8 |
11 | 2 | 4500 | 24 |
12 | 2 | 4500 | 40 |
13 | 2 | 1500 | 24 |
14 | 2 | 1500 | 40 |
15 | 2 | 1500 | 8 |
16 | 2 | 3000 | 24 |
17 | 2 | 3000 | 40 |
18 | 2 | 3000 | 8 |
Exp. # | Input Parameters | Responses | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
At Room Temp. | At 700 °C | At 1000 °C | |||||||||
Speed | Feed | Fz (N) | Ra (µm) | Rt (µm) | Fz (N) | Ra (µm) | Rt (µm) | Fz (N) | Ra (µm) | Rt (µm) | |
1 | 1500 | 8 | 876 | 0.242 | 2.3435 | 787 | 0.3225 | 2.21 | 683 | 0.2405 | 1.62 |
2 | 1500 | 40 | 1611 | 0.6385 | 6.1685 | 1367 | 0.546 | 5.10 | 1263 | 0.4945 | 4.35 |
3 | 1500 | 24 | 1355 | 0.455 | 2.6135 | 1282 | 0.42 | 1.90 | 1266 | 0.304 | 2.01 |
4 | 4500 | 24 | 545 | 0.422 | 3.4345 | 529 | 0.3695 | 4.50 | 474 | 0.338 | 3.24 |
5 | 3000 | 40 | 1266 | 0.53 | 3.1675 | 1260 | 0.509 | 2.35 | 1188 | 0.4105 | 1.63 |
6 | 3000 | 24 | 863 | 0.406 | 3.786 | 860 | 0.3495 | 4.61 | 854 | 0.333 | 2.94 |
7 | 4500 | 40 | 679 | 0.393 | 3.183 | 650 | 0.3532 | 3.02 | 608 | 0.3185 | 3.2 |
8 | 4500 | 8 | 395 | 0.268 | 3.4675 | 392 | 0.215 | 2.87 | 388 | 0.201 | 2.56 |
9 | 3000 | 8 | 957 | 0.201 | 0.998 | 847 | 0.211 | 1.02 | 794 | 0.2105 | 0.95 |
10 | 4500 | 8 | 412 | 0.2135 | 0.879 | 405 | 0.201 | 0.76 | 356 | 0.185 | 0.84 |
11 | 4500 | 24 | 1031 | 0.375 | 3.306 | 926 | 0.325 | 2.47 | 857 | 0.296 | 1.35 |
12 | 4500 | 40 | 842 | 0.403 | 4.0135 | 819 | 0.405 | 3.85 | 706 | 0.372 | 3.54 |
13 | 1500 | 24 | 1460 | 0.369 | 3.225 | 973 | 0.35 | 2.98 | 837 | 0.348 | 2.45 |
14 | 1500 | 40 | 1203 | 0.5405 | 3.993 | 903 | 0.561 | 4.50 | 773 | 0.513 | 3.51 |
15 | 1500 | 8 | 910 | 0.291 | 2.10 | 888 | 0.279 | 1.70 | 805 | 0.266 | 1.23 |
16 | 3000 | 24 | 591 | 0.376 | 3.768 | 606 | 0.349 | 4.95 | 589 | 0.256 | 4.21 |
17 | 3000 | 40 | 1002 | 0.4305 | 2.481 | 995 | 0.416 | 2.24 | 989 | 0.3395 | 2.54 |
18 | 3000 | 8 | 617 | 0.2195 | 1.069 | 542 | 0.166 | 1.05 | 482 | 0.211 | 1.02 |
Exp. # | % Improvement of 700 °C | % Improvement of 1000 °C | ||||
---|---|---|---|---|---|---|
Fz | Ra | Rt | Fz | Ra | Rt | |
1 | 10.15 | 24.96 | 5.70 | 22.03 | 25.43 | 30.87 |
2 | 15.14 | 14.49 | 17.32 | 21.60 | 22.55 | 29.48 |
3 | 5.38 | 7.69 | 27.30 | 6.57 | 33.19 | 23.09 |
4 | 2.93 | 12.44 | 31.02 | 13.03 | 19.91 | 5.66 |
5 | 0.47 | 3.96 | 25.81 | 6.16 | 22.55 | 48.54 |
6 | 0.34 | 13.92 | 21.76 | 1.04 | 17.98 | 22.35 |
7 | 4.27 | 10.13 | 5.12 | 10.46 | 18.96 | 0.53 |
8 | 0.75 | 19.78 | 17.23 | 1.77 | 35.07 | 26.17 |
9 | 11.49 | 4.98 | 2.20 | 17.03 | 4.73 | 4.81 |
10 | 1.69 | 5.85 | 13.54 | 13.59 | 13.35 | 4.44 |
11 | 10.18 | 13.33 | 25.29 | 16.88 | 21.07 | 59.17 |
12 | 2.73 | 0.50 | 4.07 | 16.15 | 7.69 | 11.80 |
13 | 33.35 | 5.15 | 7.60 | 42.67 | 5.69 | 24.03 |
14 | 9.97 | 3.79 | 12.70 | 22.93 | 5.09 | 12.10 |
15 | 2.41 | 4.12 | 19.05 | 11.54 | 8.59 | 41.43 |
16 | 2.53 | 7.18 | 31.37 | 0.34 | 31.91 | 11.73 |
17 | 0.69 | 3.37 | 9.71 | 1.30 | 21.14 | 2.38 |
18 | 12.15 | 24.37 | 1.78 | 21.88 | 3.87 | 4.58 |
Region | # of Drilled Holes | Tool Flank Wear (mm) | ||
---|---|---|---|---|
@ Room Temp | @ 700 °C | @ 1000 °C | ||
A | 6 | 0.144 | 0.116 | 0.000 |
A | 12 | 0.185 | 0.172 | 0.000 |
A | 18 | 0.253 | 0.195 | 0.152 |
B | 6 | 0.0 | 0.000 | 0.000 |
B | 12 | 0.114 | 0.051 | 0.000 |
B | 18 | 0.235 | 0.124 | 0.113 |
Source | DF | Adj SS | Adj MS | F-Value | p-Value |
---|---|---|---|---|---|
Speed | 2 | 921,275 | 460,637 | 8.64 | 0.001 |
Feed | 2 | 451,486 | 225,743 | 4.23 | 0.046 |
Speed × Feed | 4 | 223,121 | 55,780 | 1.05 | 0.441 |
Source | DF | Adj SS | Adj MS | F-Value | p-Value |
---|---|---|---|---|---|
Speed | 2 | 0.019997 | 0.009998 | 6.91 | 0.018 |
Feed | 2 | 0.192893 | 0.096447 | 66.61 | 0.000 |
Speed × Feed | 4 | 0.020543 | 0.005136 | 3.55 | 0.060 |
Source | DF | Adj SS | Adj MS | F-Value | p-Value |
---|---|---|---|---|---|
Speed | 1 | 4.3128 | 4.31280 | 9.46 | 0.008 |
Feed | 1 | 2.1624 | 2.16240 | 4.74 | 0.047 |
Speed × Feed | 1 | 0.0006 | 0.00063 | 0.00 | 0.971 |
Objectives | Minimize Fz Minimize Ra Minimize Rt |
Constraints | Fz ≤ 1000 N Ra ≤ 0.5 µm Rt ≤ 6.0 µm |
Evolutionary Algorithms | MOGA-II |
---|---|
Seed | 50 |
Prediction Model | Radial Basis Function |
Validation | 20% |
Radial function | Hardy’s MultiQuadrics |
Max. no. of optimization | 10 |
Parameters of MOGA | |
Parameter | value |
Number of evaluation | 5000 |
Generations numbers | 100 |
Cross-over probability | 0.5 |
Selection probability | 0.05 |
Mutation ratio | 0.05 |
Probability of mutation | 0.1 |
Random generator seed | 1 |
Point # | Inputs | Outputs | |||
---|---|---|---|---|---|
Feed Rate (mm/min) | Speed (rpm) | Fz (N) | Ra (μm) | Rt (μm) | |
1 | 10 | 4400 | 466.04 | 0.19 | 3.32 |
2 | 9 | 4500 | 417.58 | 0.18 | 3.44 |
3 | 10 | 4450 | 456.58 | 0.19 | 3.37 |
4 | 10 | 4500 | 449.22 | 0.19 | 3.41 |
5 | 9 | 4400 | 438.02 | 0.18 | 3.34 |
6 | 9 | 4350 | 451.48 | 0.19 | 3.27 |
7 | 8 | 4450 | 401.51 | 0.18 | 3.41 |
8 | 8 | 4250 | 462.91 | 0.18 | 3.10 |
9 | 8 | 4400 | 417.08 | 0.18 | 3.33 |
10 | 8 | 4200 | 477.78 | 0.18 | 3.02 |
Point # | Inputs | Outputs | % Error | |||||
---|---|---|---|---|---|---|---|---|
Feed Rate (mm/min) | Speed (rpm) | Fz (N) | Ra (μm) | Rt (μm) | Fz | Ra | Rt | |
1 | 10 | 4400 | 482.60 | 0.211 | 3.17 | 3.43 | 9.95 | 4.73 |
2 | 9 | 4500 | 450.20 | 0.195 | 3.82 | 7.25 | 7.69 | 9.95 |
3 | 10 | 4450 | 420.30 | 0.208 | 3.48 | 8.63 | 8.56 | 3.16 |
6 | 9 | 4350 | 418.60 | 0.205 | 2.99 | 7.85 | 7.32 | 9.36 |
7 | 8 | 4450 | 439.51 | 0.184 | 3.21 | 8.65 | 2.17 | 6.23 |
10 | 8 | 4200 | 501.20 | 0.172 | 2.85 | 4.67 | 4.65 | 5.96 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Abdo, B.M.A.; Almuzaiqer, R.; Noman, M.A.; Chintakindi, S. Investigation of Heat Annealing and Parametric Optimization for Drilling of Monel-400 Alloy. J. Manuf. Mater. Process. 2023, 7, 170. https://doi.org/10.3390/jmmp7050170
Abdo BMA, Almuzaiqer R, Noman MA, Chintakindi S. Investigation of Heat Annealing and Parametric Optimization for Drilling of Monel-400 Alloy. Journal of Manufacturing and Materials Processing. 2023; 7(5):170. https://doi.org/10.3390/jmmp7050170
Chicago/Turabian StyleAbdo, Basem M. A., Redhwan Almuzaiqer, Mohammed A. Noman, and Sanjay Chintakindi. 2023. "Investigation of Heat Annealing and Parametric Optimization for Drilling of Monel-400 Alloy" Journal of Manufacturing and Materials Processing 7, no. 5: 170. https://doi.org/10.3390/jmmp7050170
APA StyleAbdo, B. M. A., Almuzaiqer, R., Noman, M. A., & Chintakindi, S. (2023). Investigation of Heat Annealing and Parametric Optimization for Drilling of Monel-400 Alloy. Journal of Manufacturing and Materials Processing, 7(5), 170. https://doi.org/10.3390/jmmp7050170