An approach towards energy and material efficient additive manufacturing: Multi-objective optimization of stellite-6 deposition on SS304

Singh, Anitesh Kumar; Sadhu, Abhijit; Das, Amit Kumar; Pratihar, Dilip Kumar; Choudhury, Asimava Roy

doi:10.1016/j.optlastec.2021.107799

Cited by 24 publications

(7 citation statements)

References 65 publications

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“…Figure 8b compares the measured and predicted values of the dilution rate. It can be seen that the measured values are more centrally distributed on both sides of the fitted straight line, which also suggests that the proposed model is highly accurate [10,26]. In summary, the insignificant factors in the model were removed to develop a quadratic regression model for the effect of process parameters on the dilution rate.…”

Section: Response Surface Analysis For the Dilution Ratementioning

confidence: 94%

“…In summary, the insignificant factors in the model were removed to develop a quadratic regression model for the effect of process parameters on the dilution rate. The final regression model for the dilution rate is as follows: η = 0.34 + 0.023A + 0.020B − 0.012C + 9.505 × 10 −3 × B 2 (10) where A, B, and C in Equation ( 10) can be found in Table 5. As can be seen from Table 5, the p-values of laser power, scanning speed, and powder feed rate are all less than 0.05, and the p-values of the first two parameters are less than 0.0001, indicating that the effects of these two parameters are most significant.…”

Section: Response Surface Analysis For the Dilution Ratementioning

confidence: 99%

“…Because the DED process is characterized by complex physicochemical behavior, it is crucial to select a suitable combination of process parameters to obtain a high-quality cladding layer [7,8]. In addition, DED is a complex nonlinear process covering the coupling of multiple process parameters, resulting in significant difficulties in qualitatively characterizing the mapping relationship between individual process parameters and the quality of the cladding layer [9,10].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multi-Objective Optimization of Process Parameters in Laser DED Ni-Based Powder on Steel Rail Using Response Surface Design

Li,

Yang,

Chen

et al. 2024

Coatings

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With the rise of global industrialization, the requirements for the operating speed and carrying capacity of high-speed trains are increasingly higher. Because the wear and tear of rails gradually increases during the running of high-speed trains, strengthening or repairing rail surfaces is of paramount significance. Laser-directed energy deposition (DED) exhibits significant advantages in improving surface hardness, corrosion resistance, and abrasion resistance. Because of the multiple interacting optimization objectives, the development of a multi-objective optimization method for process parameters is significant for improving DED deposition quality. Response surface design employs multivariate quadratic regression equations to fit the functional relationship between the factors and the responses, which can be employed to find the optimal process parameters and solve multivariate problems. This study develops a multi-objective optimization model with response surface design and 2D process mappings to visually analyze the effects of scanning speed, laser power, and powder feed rate on aspect ratio, dilution rate, and microhardness. The optimal combination of process parameters for Ni-based alloys on U71Mn rail is a laser power of 431 W, a scanning speed of 5.34 mm/s, and a powder feed rate of 1.03 r/min. In addition, a multi-physics field finite element model is developed to analyze the evolution mechanism of the microstructure from the bottom to the top of the single track. This study can provide theoretical and technical support for the surface strengthening or repair of U71Mn rail.

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Section: Response Surface Analysis For the Dilution Ratementioning

confidence: 94%

Section: Response Surface Analysis For the Dilution Ratementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Optimization of Process Parameters in Laser DED Ni-Based Powder on Steel Rail Using Response Surface Design

Li,

Yang,

Chen

et al. 2024

Coatings

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“…Singh et al investigated the directed energy deposition (DED) of the Co base alloy Stellite 6 and reduced the wear rate by 6.6 by hard-facing an austenitic substrate. [12] Rajeev et al examined the processing of the alloy Stellite 6 using…”

Section: Introductionmentioning

confidence: 99%

“…Singh et al investigated the directed energy deposition (DED) of the Co base alloy Stellite 6 and reduced the wear rate by 6.6 by hard‐facing an austenitic substrate. [ 12 ] Rajeev et al examined the processing of the alloy Stellite 6 using Arc‐DED. [ 13 ] They determined that the material properties are highly anisotropic and vary locally across the sample height, which they attributed to the alternating heat input due to the layered‐wise sample built‐up.…”

Section: Introductionmentioning

confidence: 99%

Laser Powder Bed Fusion of Hard‐Phase Containing Co‐Base Alloy

Vehrs,

Röttger,

Roj

et al. 2024

Adv Eng Mater

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This work considers the processing of the hard‐phase containing CoCrW‐alloy Celsit F using powder bed fusion‐laser beam/metal (PBF‐LB/M), whereby a suitable process window for producing dense parts having a low‐defect density is determined. Several cuboid specimens are manufactured by varying the exposure parameters (laser power, hatch distance, scanning speed). With the help of quantitative image analysis, the samples are evaluated regarding crack density and porosity, and optimal exposure parameters are derived. Dense samples with a low defect density can be produced if the energy density is 60–70 J mm−3 and the base‐plate is preheated to 800 °C. Choosing lower energy densities leads to increased pore formation, whereas higher energy densities result in increased ablation and cracking. With a view to a later application in which additively manufactured hot work tools are made of Celsit F, the possibility of introducing internal cavities as potential cooling channels is considered. It can be shown that the hardness sideways is lower than above and below the cavity due to a changed microstructure and locally different heat dissipation. The results of this work give a first guideline for the PBF‐LB/M‐processing of wear‐resistant CoCrW alloys with a high hard phase volume content to tools for hot working.

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Additive Manufacturing of Composite Materials and Functionally Graded Structures Using Archerfish Hunting Technique

Prakash,

Koushik,

Jha

et al. 2024

Lubrication Science

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This paper proposes an optimisation method for fabricating composite materials and functionally graded structures. Using the proposed method, 3D printing of copper (Cu)–polyethylene (PE) composite, Al2O3–ZrO2 ceramic composite and functionally graded CuO foams are utilised. This work aims to advance the capabilities of additive manufacturing by leveraging nature‐inspired approaches to create complex, tailored structures with enhanced performance across various industries. The major objective of the proposed method is to reduce the feed rate and increase the airflow rate and airflow temperature for the heat transfer process. Using the proposed technique in the advanced preparation conditions, Cu–PE composites with unreliable Cu substances are fabricated. The PE binder particle is melting as well as forming thick composites by means of soft surfaces. Using the proposed AHO approach, functionally graded materials with common distributions can be efficiently optimised. By then, the proposed model is implemented on the MATLAB platform, and its execution is calculated using the current procedures. The proposed technique displays superior outcomes in all existing methods like wild horse optimiser, particle swarm optimisation and heap‐based optimiser. The proposed method shows a throughput of 57 mm3. The existing method shows the throughput of 32, 27 and 45 mm3. The results show that the proposed method has higher throughput compared with existing methods.

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An approach towards energy and material efficient additive manufacturing: Multi-objective optimization of stellite-6 deposition on SS304

Cited by 24 publications

References 65 publications

Multi-Objective Optimization of Process Parameters in Laser DED Ni-Based Powder on Steel Rail Using Response Surface Design

Multi-Objective Optimization of Process Parameters in Laser DED Ni-Based Powder on Steel Rail Using Response Surface Design

Laser Powder Bed Fusion of Hard‐Phase Containing Co‐Base Alloy

Additive Manufacturing of Composite Materials and Functionally Graded Structures Using Archerfish Hunting Technique

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