# Investigation and Optimization of Effects of 3D Printer Process Parameters on Performance Parameters

^{1}

^{2}

^{*}

^{†}

## Abstract

**:**

## 1. Introduction

- Analyze the regression analysis and the magnitude of the parametric influence on performance parameters using ANOVA;
- Investigate how changing LT, ID, and PS values affect ABS’s FS, TS, Ra, T, and E;
- We can achieve optimal performance by applying RSM’s multi-objective numerical optimization technique to the FFF 3DP’s parameters;
- Conduct trials and analyze the results using an SEM to verify the optimum sample preparation.

## 2. Materials and Methods

#### 2.1. Materials

#### 2.2. Response Surface Methodology

#### Measurement Procedure

## 3. Results and Discussion

#### 3.1. ANOVA for Performance Parameters

^{2}− 0.001 × ID

^{2}− 0.009 × PS

^{2}

^{2}+ 0.002 × ID

^{2}+ 0.002 × PS

^{2}

^{2}− 0.000 × ID

^{2}− 0.001 × PS

^{2}

^{2}+ 0.001 × ID

^{2}+ 0.009 × PS

^{2}

^{2}+ 0.000 × ID

^{2}+ 0.00003 × PS

^{2}

^{2}, the adjusted R

^{2}, and the anticipated R

^{2}, indicating a good correlation between the experimental and calculated values. A number greater than four is preferred. In this investigation, the appropriate precision for FS and TS is greater than 4, indicating a sufficient signal.

#### 3.2. Printing Parametric Effects on Mechanical Properties

#### 3.3. Effect of Printing Parameters on Ra

#### 3.4. Eco-Friendly 3D Printing

## 4. Multi-Optimization Using Response Surface Methodology

#### Conformation Test

## 5. Conclusions and Prospects

- Inclusive multi-objective optimization of important performance parameters that are vital for industry (tensile strength (TS), flexural strength (FS), average surface roughness (Ra), print time (T), and energy consumption (E)) have been studied.
- Comprehensive investigations yielded contradictory optimized performance parameters, such as high FS and TS, low Ra value, and lowest T and E. The most crucial element in obtaining the desired Ra and T was LT (due to the staircase effect), whereas ID was the most crucial element in attaining the desired mechanical characteristics. PS also affected mechanical properties due to the polymer healing effects.
- Optimal printing settings combination for achieving FS, TS, Ra, T, and E for ABS were found at layer thickness LT = 0.27 mm, ID = 84%, and PS = 51.1 mm/s using the numerical multi-objective optimization. FS of 58.01 MPa, TS of 35.8 MPa, lowest Ra of 8.01 µm, lowest T of 58 min, and E of 0.21 kwh were attained using numerical multi-objective optimization. The variation percentage between the predicted and experimental values lies within 2.32%, 0.86%, 2.96%, 1.05%, and 4.55% for FS, TS, Ra, T, and E, respectively. Thus, the prediction implementation of the model is satisfactory.
- Reducing the T and E demonstrates that the FFF approach is feasible regarding power consumption, fuel efficiency, and controllable carbon emissions.
- The ABS mathematical models projected performance parameters findings and experimental results were all very close. When used for product quality testing, these data can be used as a guide to determine the best printing settings, saving time on trial and error.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Mushtaq, R.T.; Iqbal, A.; Wang, Y.; Cheok, Q.; Abbas, S. Parametric Effects of Fused Filament Fabrication Approach on Surface Roughness of Acrylonitrile Butadiene Styrene and Nylon-6 Polymer. Materials
**2022**, 15, 5206. [Google Scholar] [CrossRef] [PubMed] - Wang, Y.; Mushtaq, R.T.; Ahmed, A.; Ahmed, A.; Rehman, M.; Rehman, M.; Khan, A.M.; Sharma, S.; Ishfaq, D.K.; Ali, H.; et al. Additive Manufacturing Is Sustainable Technology: Citespace Based Bibliometric Investigations of Fused Deposition Modeling Approach. Rapid Prototyp. J.
**2022**, 28, 654–675. [Google Scholar] [CrossRef] - Shanmugam, R.; Ramoni, M.O.; Chandran, J.; Mohanavel, V.; Pugazhendhi, L. A Review on the Significant Classification of Additive Manufacturing. J. Phys. Conf. Ser.
**2021**, 2027, 12026. [Google Scholar] [CrossRef] - Rehman, M.; Yanen, W.; Mushtaq, R.T.; Ishfaq, K.; Zahoor, S.; Ahmed, A.; Kumar, M.S.; Gueyee, T.; Rahman, M.M.; Sultana, J. Additive Manufacturing for Biomedical Applications: A Review on Classification, Energy Consumption, and Its Appreciable Role since COVID-19 Pandemic. Prog. Addit. Manuf.
**2022**, 1–35. [Google Scholar] [CrossRef] - Ur Rehman, A.; Pitir, F.; Salamci, M.U. Full-Field Mapping and Flow Quantification of Melt Pool Dynamics in Laser Powder Bed Fusion of SS316L. Materials
**2021**, 14, 6264. [Google Scholar] [CrossRef] [PubMed] - Ur Rehman, A.; Mahmood, M.A.; Pitir, F.; Salamci, M.U.; Popescu, A.C.; Mihailescu, I.N. Keyhole Formation by Laser Drilling in Laser Powder Bed Fusion of Ti6Al4V Biomedical Alloy: Mesoscopic Computational Fluid Dynamics Simulation versus Mathematical Modelling Using Empirical Validation. Nanomaterials
**2021**, 11, 3284. [Google Scholar] [CrossRef] - Ali, Z.; Yan, Y.; Mei, H.; Cheng, L.; Zhang, L. Effect of infill density, build direction and heat treatment on the tensile mechanical properties of 3D-printed carbon-fiber nylon composites. Compos. Struct.
**2023**, 304, 116370. [Google Scholar] [CrossRef] - Ur Rehman, A.; Mahmood, M.A.; Ansari, P.; Pitir, F.; Salamci, M.U.; Popescu, A.C.; Mihailescu, I.N. Spatter Formation and Splashing Induced Defects in Laser-Based Powder Bed Fusion of AlSi10Mg Alloy: A Novel Hydrodynamics Modelling with Empirical Testing. Metals
**2021**, 11, 2023. [Google Scholar] [CrossRef] - Mohamed, O.A.; Masood, S.H.; Bhowmik, J.L. Optimization of Fused Deposition Modeling Process Parameters: A Review of Current Research and Future Prospects. Adv. Manuf.
**2015**, 3, 42–53. [Google Scholar] [CrossRef] - Li, H.; Wang, T.; Sun, J.; Yu, Z. The Effect of Process Parameters in Fused Deposition Modelling on Bonding Degree and Mechanical Properties. Rapid Prototyp. J.
**2018**, 24, 80–92. [Google Scholar] [CrossRef] - Singh, G.; Sharma, S.; Mittal, M.; Singh, G.; Singh, J.; Changhe, L.; Khan, A.M.; Dwivedi, S.P.; Mushtaq, R.T.; Singh, S. Impact of Post-Heat-Treatment on the Surface-Roughness, Residual Stresses, and Micromorphology Characteristics of Plasma-Sprayed Pure Hydroxyapatite and 7%-Aloxite Reinforced Hydroxyapatite Coatings Deposited on Titanium Alloy-Based Biomedical Implants. J. Mater. Res. Technol.
**2022**, 18, 1358–1380. [Google Scholar] [CrossRef] - Kumar, M.S.; Javidrad, H.R.; Shanmugam, R.; Ramoni, M.; Adediran, A.A.; Pruncu, C.I. Impact of Print Orientation on Morphological and Mechanical Properties of L-PBF Based AlSi7Mg Parts for Aerospace Applications. Silicon
**2022**, 14, 7083–7097. [Google Scholar] [CrossRef] - Crump, S.S. Fused Deposition Modeling (FDM): Putting Rapid Back into Prototyping. In Proceedings of the 2nd International Conference on Rapid Prototyping, Dayton, OH, USA, 23–26 June 1991; pp. 354–357. [Google Scholar]
- Turner, B.N.; Strong, R.; Gold, S.S. A Review of Melt Extrusion Additive Manufacturing Processes: I. Process Design and Modeling. Rapid Prototyp. J.
**2014**, 20, 192–204. [Google Scholar] [CrossRef] - Spreeman, M.E.; Stretz, H.A.; Dadmun, M.D. Role of Compatibilizer in 3D Printing of Polymer Blends. Addit. Manuf.
**2019**, 27, 267–277. [Google Scholar] [CrossRef] - Wang, Y.; Müller, W.-D.; Rumjahn, A.; Schwitalla, A. Parameters Influencing the Outcome of Additive Manufacturing of Tiny Medical Devices Based on PEEK. Materials
**2020**, 13, 466. [Google Scholar] [CrossRef] - Choong, Y.Y.C.; Tan, H.W.; Patel, D.C.; Choong, W.T.N.; Chen, C.-H.; Low, H.Y.; Tan, M.J.; Patel, C.D.; Chua, C.K. The Global Rise of 3D Printing during the COVID-19 Pandemic. Nat. Rev. Mater.
**2020**, 5, 637–639. [Google Scholar] [CrossRef] - Khan, S.B.; Irfan, S.; Lam, S.S.; Sun, X.; Chen, S. 3D printed nanofiltration membrane technology for waste water distillation. J. Water Process Eng.
**2022**, 49, 102958. [Google Scholar] [CrossRef] - Pramanik, D.; Mandal, A.; Kuar, A.S. An Experimental Investigation on Improvement of Surface Roughness of ABS on Fused Deposition Modelling Process. Mater. Today Proc.
**2019**, 26, 860–863. [Google Scholar] [CrossRef] - Jin, S.J.; Jeong, I.D.; Kim, J.H.; Kim, W.C. Accuracy (Trueness and Precision) of Dental Models Fabricated Using Additive Manufacturing Methods. Int. J. Comput. Dent.
**2018**, 21, 107–113. [Google Scholar] - Monzon, M.D.; Diaz, N.; Benitez, A.N.; Marrero, M.D.; Hernandez, P.M. Advantages of Fused Deposition Modeling for Making Electrically Conductive Plastic Patterns. In Proceedings of the 2010 International Conference on Manufacturing Automation, Hong Kong, China, 13–15 December 2010. [Google Scholar]
- Soriano-Heras, E.; Blaya-Haro, F.; Molino, C.; de Agustín del Burgo, J.M. Rapid Prototyping Prosthetic Hand Acting by a Low-Cost Shape-Memory-Alloy Actuator. J. Artif. Organs
**2018**, 21, 238–246. [Google Scholar] [CrossRef] - Rahim, T.N.A.T.; Abdullah, A.M.; Md Akil, H. Recent Developments in Fused Deposition Modeling-Based 3D Printing of Polymers and Their Composites. Polym. Rev.
**2019**, 59, 589–624. [Google Scholar] [CrossRef] - Shakor, P.; Nejadi, S.; Paul, G.; Sanjayan, J. A Novel Methodology of Powder-Based Cementitious Materials in 3D Inkjet Printing for Construction Applications. In Proceedings of the 6th International Conference on Durability of Concrete Structures, ICDCS 2018, Leeds, UK, 18–20 July 2018. [Google Scholar]
- Fischer, A.C.; Mäntysalo, M.; Niklaus, F. Inkjet Printing, Laser-Based Micromachining, and Micro–3D Printing Technologies for MEMS. In Handbook of Silicon Based MEMS Materials and Technologies; Elsevier: Amsterdam, The Netherlands, 2020; pp. 531–545. [Google Scholar]
- Ntousia, M.; Fudos, I. 3D Printing Technologies & Applications: An Overview. In Proceedings of the CAD 2020 Conference, Singapore, 24–26 June 2019. [Google Scholar]
- Samykano, M.; Selvamani, S.K.; Kadirgama, K.; Ngui, W.K.; Kanagaraj, G.; Sudhakar, K. Mechanical Property of FDM Printed ABS: Influence of Printing Parameters. Int. J. Adv. Manuf. Technol.
**2019**, 102, 2779–2796. [Google Scholar] [CrossRef] - Lopez, D.M.B.; Ahmad, R. Tensile Mechanical Behaviour of Multi-Polymer Sandwich Structures via Fused Deposition Modelling. Polymers
**2020**, 12, 13. [Google Scholar] [CrossRef] - Mohamed, O.A.; Masood, S.H.; Bhowmik, J.L. Investigation on the Flexural Creep Stiffness Behavior of PC-ABS Material Processed by Fused Deposition Modeling Using Response Surface Definitive Screening Design. JOM
**2017**, 69, 498–505. [Google Scholar] [CrossRef] - Gautam, R.; Idapalapati, S.; Feih, S. Printing and Characterisation of Kagome Lattice Structures by Fused Deposition Modelling. Mater. Des.
**2018**, 137, 266–275. [Google Scholar] [CrossRef] - Ravi, A.K.; Deshpande, A.; Hsu, K.H. An In-Process Laser Localized Pre-Deposition Heating Approach to Inter-Layer Bond Strengthening in Extrusion Based Polymer Additive Manufacturing. J. Manuf. Process.
**2016**, 24, 179–185. [Google Scholar] [CrossRef] - Ancans, A.; Rozentals, A.; Nesenbergs, K.; Greitans, M. Inertial Sensors and Muscle Electrical Signals in Human-Computer Interaction. In Proceedings of the 2017 6th International Conference on Information and Communication Technology and Accessibility (ICTA), Muscat, Oman, 19–21 December 2017; pp. 1–6. [Google Scholar]
- Bruncko, M.; Anzel, I. Microstructure and Magnetic Properties of Polymer Bonded Magnets Produced by Additive Manufacturing Technologies. Prakt. Metallogr.-Pract. Metallogr.
**2019**, 56, 512–522. [Google Scholar] [CrossRef] - Guessasma, S.; Belhabib, S.; Nouri, H. Microstructure, Thermal and Mechanical Behavior of 3D Printed Acrylonitrile Styrene Acrylate. Macromol. Mater. Eng.
**2019**, 304, 11. [Google Scholar] [CrossRef] - Mwema, F.M.; Akinlabi, E.T. Basics of Fused Deposition Modelling (FDM). In Fused Deposition Modeling. SpringerBriefs in Applied Sciences and Technology; Springer: Cham, Switzerland, 2020; pp. 1–15. [Google Scholar] [CrossRef]
- Li, L.; Liu, W.; Wang, Y.; Zhao, Z. Mechanical performance and damage monitoring of CFRP thermoplastic laminates with an open hole repaired by 3D printed patches. Compos. Struct.
**2023**, 303, 116308. [Google Scholar] [CrossRef] - Larrañeta, E.; Dominguez-Robles, J.; Lamprou, D.A. Additive Manufacturing Can Assist in the Fight against COVID-19 and Other Pandemics and Impact on the Global Supply Chain. 3D Print Addit. Manuf.
**2020**, 7, 100–103. [Google Scholar] [CrossRef] - Singh, J. Influence of Process Parameters on Mechanical Strength, Build Time, and Material Consumption of 3D Printed Polylactic Acid Parts. Polym. Compos.
**2022**, 43, 5908–5928. [Google Scholar] [CrossRef] - Enemuoh, E.U.; Duginski, S.; Feyen, C.; Menta, V.G. Effect of Process Parameters on Energy Consumption, Physical, and Mechanical Properties of Fused Deposition Modeling. Polymers
**2021**, 13, 2506. [Google Scholar] [CrossRef] - Das, S.; Hollister, S.F.; Flanagan, C.; Adewunmi, A.; Bark, K.; Chen, C.; Ramaswamy, K.; Rose, D.; Widjaja, E. Freeform Fabrication of Nylon-6 Tissue Engineering Scaffolds. Rapid Prototyp. J.
**2003**, 9, 43–49. [Google Scholar] [CrossRef] - Jaiganesh, V.; Manivannan, S.; Manivannan, S. Numerical Analysis and Simulation of Nylon Composite Propeller for Aircraft. In Proceedings of the 12th Global Congress on Manufacturing and Management (GCMM—2014), Vellore, India, 8–10 December 2014; Xavior, M.A., Yarlagadda, P., Eds.; Elsevier Science BV: Amsterdam, The Netherlands, 2014; Volume 97, pp. 1079–1088. [Google Scholar]
- Kumar, R.; Singh, R.; Ahuja, I.P.S. Repair of Automotive Bumpers and Bars with Modified Friction Stir Welding. J. Cent. South Univ.
**2020**, 27, 2239–2248. [Google Scholar] [CrossRef] - Kechagias, J.D.; Vidakis, N.; Petousis, M.; Mountakis, N. A Multi-Parametric Process Evaluation of the Mechanical Response of PLA in FFF 3D Printing. Mater. Manuf. Process.
**2022**, 38, 941–953. [Google Scholar] [CrossRef] - Vidakis, N.; Petousis, M.; Kechagias, J.D. Parameter Effects and Process Modelling of Polyamide 12 3D-Printed Parts Strength and Toughness. Mater. Manuf. Process.
**2022**, 37, 1358–1369. [Google Scholar] [CrossRef] - Saharudin, M.S.; Hajnys, J.; Kozior, T.; Gogolewski, D.; Zmarzły, P. Quality of Surface Texture and Mechanical Properties of Pla and Pa-Based Material Reinforced with Carbon Fibers Manufactured by Fdm and Cff 3d Printing Technologies. Polymers
**2021**, 13, 1671. [Google Scholar] [CrossRef] - Vyavahare, S.; Kumar, S.; Panghal, D. Experimental Study of Surface Roughness, Dimensional Accuracy and Time of Fabrication of Parts Produced by Fused Deposition Modelling. Rapid Prototyp. J.
**2020**, 26, 1535–1554. [Google Scholar] [CrossRef] - Harris, M.; Potgieter, J.; Archer, R.; Arif, K.M. In-Process Thermal Treatment of Polylactic Acid in Fused Deposition Modelling. Mater. Manuf. Process.
**2019**, 34, 701–713. [Google Scholar] [CrossRef] - Dey, A.; Yodo, N. A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics. J. Manuf. Mater. Process.
**2019**, 3, 64. [Google Scholar] [CrossRef] - Chadha, C.; Crowe, K.A.; Carmen, C.L.; Patterson, A.E. Exploring an AM-Enabled Combination-of-Functions Approach for Modular Product Design. Designs
**2018**, 2, 37. [Google Scholar] [CrossRef] - Chadha, C.; Patterson, A.E.; Jasiuk, I. SFF 2021 Paper- Non-Review Submission Predict Adhesive Strength of Repair of Thermoplastic Component Based on Polymer Healing Theory. Rapid Prototyp. J.
**2021**, 23, 560–574. [Google Scholar] - Li, C.; Xiao, Q.; Tang, Y.; Li, L. A Method Integrating Taguchi, RSM and MOPSO to CNC Machining Parameters Optimization for Energy Saving. J. Clean. Prod.
**2016**, 135, 263–275. [Google Scholar] [CrossRef] - Mushtaq, R.T.; Wang, Y.; Rehman, M.; Khan, A.M.; Mia, M. State-Of-The-Art and Trends in CO2 Laser Cutting of Polymeric Materials—A Review. Materials
**2020**, 13, 3839. [Google Scholar] [CrossRef] [PubMed] - Kundra, T.K. Multi-Objective Optimisation of Fused Deposition Modelling PROCESS parameters Using RSM and Fuzzy Logic for Build Time and Support Material. Int. J. Rapid Manuf.
**2018**, 7, 25–42. [Google Scholar] [CrossRef] - Griffiths, C.A.; Howarth, J.; De Almeida-Rowbotham, G.; Rees, A. A Design of Experiments Approach to Optimise Tensile and Notched Bending Properties of Fused Deposition Modelling Parts. Proc. Inst. Mech. Eng. B J. Eng. Manuf.
**2016**, 230, 1502–1512. [Google Scholar] [CrossRef] - Saad, M.S.; Nor, A.M.; Baharudin, M.E.; Zakaria, M.Z.; Aiman, A.F. Optimization of Surface Roughness in FDM 3D Printer Using Response Surface Methodology, Particle Swarm Optimization, and Symbiotic Organism Search Algorithms. Int. J. Adv. Manuf. Technol.
**2019**, 105, 5121–5137. [Google Scholar] [CrossRef] - Selvamani, S.K.; Rajan, K.; Samykano, M.; Kumar, R.R.; Kadirgama, K.; Mohan, R.V. Investigation of Tensile Properties of PLA–Brass Composite Using FDM. Prog. Addit. Manuf.
**2022**, 7, 839–851. [Google Scholar] [CrossRef] - ISO 527-1; BSI Standards Publication Determination of Tensile Properties—ISO527 Part1. ISO: Geneva, Switzerland, 2012.
- ASTM D790-10; Standard Test Methods for Flexural Properties of Unreinforced and Reinforced Plastics and Electrical Insulating Materials; Annual Book of ASTM Standards. ASTM International: West Conshohocken, PA, USA, 2002; pp. 1–12. [CrossRef]
- Hamid, R.A.; Hamezah, F.H.; Abd Razak, J. Influence of Humidity on the Tensile Strength of 3D Printed PLA Filament. In Intelligent Manufacturing and Mechatronics. SympoSIMM 2021; Lecture Notes in Mechanical Engineering; Ali Mokhtar, M.N., Jamaludin, Z., Abdul Aziz, M.S., Maslan, M.N., Razak, J.A., Eds.; Springer Nature: Singapore, 2022; pp. 497–502. [Google Scholar]
- Teir, L.; Lindstedt, T.; Widmaier, T.; Hemming, B.; Brand, U.; Fahrbach, M.; Peiner, E.; Lassila, A. In-Line Measurement of the Surface Texture of Rolls Using Long Slender Piezoresistive Microprobes. Sensors
**2021**, 21, 5955. [Google Scholar] [CrossRef] - Luzanin, O.; Guduric, V.; Ristic, I.; Muhic, S. Investigating Impact of Five Build Parameters on the Maximum Flexural Force in FDM Specimens—A Definitive Screening Design Approach. Rapid Prototyp. J.
**2017**, 23, 1088–1098. [Google Scholar] [CrossRef] - De Kergariou, C.; Saidani-Scott, H.; Perriman, A.; Scarpa, F.; Le Duigou, A. The Influence of the Humidity on the Mechanical Properties of 3D Printed Continuous Flax Fibre Reinforced Poly(Lactic Acid) Composites. Compos. Part A Appl. Sci. Manuf.
**2022**, 155, 106805. [Google Scholar] [CrossRef] - Shirmohammadi, M.; Goushchi, S.J.; Keshtiban, P.M. Optimization of 3D Printing Process Parameters to Minimize Surface Roughness with Hybrid Artificial Neural Network Model and Particle Swarm Algorithm. Prog. Addit. Manuf.
**2021**, 6, 199–215. [Google Scholar] [CrossRef] - Kechagias, J.; Chaidas, D.; Vidakis, N.; Salonitis, K.; Vaxevanidis, N.M. Key Parameters Controlling Surface Quality and Dimensional Accuracy: A Critical Review of FFF Process. Mater. Manuf. Process.
**2022**, 37, 963–984. [Google Scholar] [CrossRef]

**Figure 1.**Schematic of FFF-based 3D printer (figure reprinted from [16] under the license CC-BY 4.0).

**Figure 5.**The effect of printing parameters on ABS: (

**a**) FS vs. LT, (

**b**) ID vs. FS, (

**c**) PS vs. FS, (

**d**) LT vs. TS, (

**e**) ID vs. TS, and (

**f**) PS vs. TS. Red represents center point. Black is the average data line and green lines show the min and max of data.

**Figure 6.**SEM images of TS and FS samples: (

**a**) FS-Exp 4 (ID = 100%, PS = 75 mm/s, LT = 0.22 mm), (

**b**) FS-Exp number 14 (ID = 20%, LT = 0.3 mm, PS = 75 mm/s), (

**c**) Stress strain curve for Exp 4 and 14, (

**d**) TS-Exp 4 (ID = 100%, PS = 61 mm/s, LT = 0.22 mm), (

**e**) TS-Exp 5 (ID = 52% PS = 61 mm/s, LT = 0.22), (

**f**) Stress strain curve for Exp 4 and 5, and (

**g**) FS results for ABS (

**h**) TS results for ABS.

**Figure 7.**2D contour plots show the effect of the printing parameters and interaction on FS and TS; (

**a**) effect of LT Vs. ID on FS, (

**b**) effect of LT Vs. ID on TS.

**Figure 8.**The effect of printing parameters on the Ra of the ABS polymer; (

**a**) effect of LT on Ra, (

**b**) effect of ID on Ra; (

**c**) effect of PS on Ra. Red represents center point. Black is the average data line and green lines show the min and max of data.

**Figure 9.**2D contour plots show the parameters’ effect and their interaction parameters on Ra; (

**a**) effect of LT Vs. ID on Ra, (

**b**) effect of LT Vs. PS on Ra.

**Figure 10.**SEM analysis of Ra-ABS and experimental results; (

**a**) SEM image of exp 16 (0.34 mm LT), (

**b**) SEM image of exp 2 (LT = 0.14 mm, PS = 47 mm/s, ID 20%), and (

**c**) graphical representation of each experimental performance parameter value of Ra.

**Figure 12.**Effect of printing parameters on (

**a**–

**c**) for T and (

**d**–

**f**) for E. Red represents center point. Black is the average data line and green lines show the min and max of data.

**Figure 13.**2D contour plots showing the effect of parameters and their interaction parameters on T and E; (

**a**) effect of LT Vs. ID on T, (

**b**) effect of LT Vs. PS on T, (

**c**) effect of LT Vs. ID on E, (

**d**) effect of LT Vs. PS on E.

**Figure 14.**Graphical representation of the mechanical performance parameters values for every experiment: (

**a**) for T-ABS and (

**b**) for E-ABS.

**Figure 15.**Optimization conditions and final performance parameters as the result of optimization: (

**a**–

**c**) for the parameters and (

**d**–

**h**) for the responses.

**Figure 16.**Numerical optimization plots show the region of optimal FFF parameters: (

**a**) desirability, (

**b**) FS optimization, (

**c**) TS optimization, (

**d**) Ra optimization, (

**e**) T optimization, and (

**f**) E optimization.

Specification | Details |
---|---|

Layer Thickness | 0.05–0.4 mm |

Nozzle Diameter | Standard 0.4 mm (can be changed to 0.3/0.2 mm) |

Filaments | 1.75 mm PLA, ABS, PA6, TPU, Copper, Wood, Carbon Fiber |

Print Speed | Normal: 60 mm/s, high: 100 mm/s |

Printing Method | TF card/Online/Offline |

Software Supporting | PROE, Solidworks, UG, 3D Max, Rhino 3D design |

File Format | STL/OBJ/G-Code |

Layers Software | Cura/Repetier-Host |

Printing Size | 300 × 225 × 320 mm |

Print Temperature | Up to 270 °C |

Power supply | 230V |

Bed Temperature | Up to 120 °C |

Properties | Values | Unit |
---|---|---|

Density | 1.04 | g/cm^{3} |

Flexural modulus | 2000–3000 | MPa |

TS | 30–40 | MPa |

Impact strength | Good | |

Heat resistance | 95–105 °C | °C |

Exp # | LT (mm) | ID (%) | PS (mm/s) |
---|---|---|---|

1 | 0.22 | 52 | 61 |

2 | 0.14 | 20 | 47 |

3 | 0.14 | 84 | 47 |

4 | 0.22 | 100 | 61 |

5 | 0.22 | 52 | 61 |

6 | 0.22 | 52 | 61 |

7 | 0.22 | 52 | 61 |

8 | 0.22 | 52 | 82 |

9 | 0.22 | 52 | 61 |

10 | 0.22 | 4 | 61 |

11 | 0.34 | 52 | 61 |

12 | 0.3 | 84 | 47 |

13 | 0.22 | 52 | 40 |

14 | 0.3 | 20 | 75 |

15 | 0.14 | 84 | 75 |

16 | 0.3 | 84 | 75 |

17 | 0.22 | 52 | 61 |

18 | 0.1 | 52 | 61 |

19 | 0.14 | 20 | 75 |

20 | 0.3 | 20 | 47 |

**Table 4.**Performance parameters, mean, standard deviation, and ANOVA models were used for the investigation.

Performance Parameter | Name | Units | Observations | Min | Max | Mean | Std. Dev. | Ratio | Model |
---|---|---|---|---|---|---|---|---|---|

1 | FS | MPa | 20 | 39.47 | 61.81 | 50.07 | 6.75 | 1.57 | Quadratic |

2 | TS | MPa | 20 | 26.14 | 40.02 | 32.12 | 3.85 | 1.53 | Quadratic |

3 | Ra | µm | 20 | 3.77 | 10.18 | 7.62 | 1.69 | 2.70 | Quadratic |

4 | T | min | 20 | 36 | 106 | 60.80 | 19.69 | 2.94 | Quadratic |

5 | E | kwh | 20 | 0.14 | 0.41 | 0.2340 | 0.0754 | 2.93 | Quadratic |

FS | TS | Ra | T | E |
---|---|---|---|---|

0.197525 | 1.5 | 0.017725 | 2.7 | 0.0105 |

2.0625 | 1.4105 | 0.252 | 4.1 | 0.0155 |

2.3 | 1.8995 | 0.1885 | 5.3 | 0.0205 |

3.0905 | 2.001 | 0.3825 | 3.6 | 0.014 |

2.736 | 1.554 | 0.3845 | 2.7 | 0.0105 |

2.695 | 1.552 | 0.3985 | 2.7 | 0.0105 |

2.68 | 1.4285 | 0.3985 | 2.7 | 0.0105 |

2.382 | 1.5435 | 0.46 | 2.35 | 0.009 |

2.6985 | 1.554 | 0.3935 | 2.7 | 0.0105 |

2.005 | 1.3995 | 0.3985 | 2.2 | 0.0085 |

2.5345 | 1.6995 | 0.483 | 2.05 | 0.008 |

2.899 | 1.781 | 0.3895 | 2.75 | 0.0105 |

2.573 | 1.565 | 0.2925 | 3.6 | 0.014 |

1.9735 | 1.558 | 0.507 | 1.8 | 0.007 |

2.776 | 1.8675 | 0.315 | 4.1 | 0.0155 |

2.8505 | 1.9055 | 0.509 | 2.1 | 0.008 |

2.7155 | 1.584 | 0.3985 | 2.7 | 0.0105 |

2.0665 | 1.4095 | 0.2575 | 5.2 | 0.02 |

2.0655 | 1.307 | 0.373 | 3.05 | 0.0115 |

2.265 | 1.527 | 0.4355 | 2.4 | 0.009 |

Performance Parameter | R^{2} | Adj-R^{2} | Pre-R^{2} | Precision | F-Value | Lack-of-Fit | Model p-Value |
---|---|---|---|---|---|---|---|

FS | 97.68 | 95.60 | 81.97 | 23.09 | 46.09 | 0.004 | <0.0001 |

TS | 95.82 | 92.05 | 82.45 | 19.59 | 25.45 | 0.556 | <0.0001 |

Ra | 98.78 | 97.67 | 90.86 | 34.22 | 89.64 | 0.01 | <0.0001 |

T | 99.43 | 98.93 | 95.82 | 71.63 | 196.35 | <0.0001 | |

E | 99.27 | 98.61 | 94.64 | 44.33 | 150.26 | <0.0001 |

**Table 7.**Predicted process parameters, performance parameters, experimental performance parameters, and error by RSM optimization.

Predicted Process Parameters | Predicted Performance Parameters | Experimental Performance Parameters | Error % | ||||||
---|---|---|---|---|---|---|---|---|---|

Name | Unit | Value | Name | Unit | Value | Name | Unit | Value | Value |

LT | mm | 0.27 | FS | MPa | 59.39 | FS | MPa | 58.01 | 2.32 |

ID | % | 84 | TS | MPa | 36.11 | TS | MPa | 35.8 | 0.86 |

PS | mm/s | 51.1 | Ra | μm | 7.78 | Ra | μm | 8.01 | 2.96 |

T | min | 57.4 | T | min | 58 | 1.05 | |||

E | kwh | 0.22 | E | kwh | 0.21 | 4.55 |

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## Share and Cite

**MDPI and ACS Style**

Mushtaq, R.T.; Iqbal, A.; Wang, Y.; Rehman, M.; Petra, M.I.
Investigation and Optimization of Effects of 3D Printer Process Parameters on Performance Parameters. *Materials* **2023**, *16*, 3392.
https://doi.org/10.3390/ma16093392

**AMA Style**

Mushtaq RT, Iqbal A, Wang Y, Rehman M, Petra MI.
Investigation and Optimization of Effects of 3D Printer Process Parameters on Performance Parameters. *Materials*. 2023; 16(9):3392.
https://doi.org/10.3390/ma16093392

**Chicago/Turabian Style**

Mushtaq, Ray Tahir, Asif Iqbal, Yanen Wang, Mudassar Rehman, and Mohd Iskandar Petra.
2023. "Investigation and Optimization of Effects of 3D Printer Process Parameters on Performance Parameters" *Materials* 16, no. 9: 3392.
https://doi.org/10.3390/ma16093392