2022
DOI: 10.3390/mi13122191
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Fuzzy Control Modeling to Optimize the Hardness and Geometry of Laser Cladded Fe-Based MG Single Track on Stainless Steel Substrate Prepared at Different Surface Roughness

Abstract: Metallic glass (MG) is a promising coating material developed to enhance the surface hardness of metallic substrates, with laser cladding having become popular to develop such coatings. MGs properties are affected by the laser cladding variables (laser power, scanning speed, spot size). Meanwhile, the substrate surface roughness significantly affects the geometry and hardness of the laser-cladded MG. In this research, Fe-based MG was laser-cladded on substrates with different surface roughness. For this purpos… Show more

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Cited by 11 publications
(4 citation statements)
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“…Two crucial factors that are crucial in this situation are the “retained-strain”, and the desired “austenite grain-size”. The “cooling rate”, “composition”, and “deformation history” are instances of exterior factors that have an impact on both of these variables 40 42 .
Figure 10 SEM images of the cladding surface and the cladding layer of SS-304 coating with Fe20Co20Ni20Mn20Cu20 HEA.
…”
Section: Resultsmentioning
confidence: 99%
“…Two crucial factors that are crucial in this situation are the “retained-strain”, and the desired “austenite grain-size”. The “cooling rate”, “composition”, and “deformation history” are instances of exterior factors that have an impact on both of these variables 40 42 .
Figure 10 SEM images of the cladding surface and the cladding layer of SS-304 coating with Fe20Co20Ni20Mn20Cu20 HEA.
…”
Section: Resultsmentioning
confidence: 99%
“…It is suggested to use quadratic models for response parameters in all experimental conditions. The sequential sum of squares measures the contribution of terms of increasing complexity to the model (Kiranakumar et al, 2022;Lashin et al, 2022;Kumar et al, 2023c). This test selects the ultimate polynomial order with no aliased terms.…”
Section: Surface Roughnessmentioning
confidence: 99%
“…Fuzzy Logic Controllers use linguistic variables and a set of rules to make control decisions based on imprecise or uncertain information (Lee, 1990). Instead of relying on precise mathematical models, FLCs use linguistic terms like "high," "low," "medium," etc., to describe the control inputs and outputs (Lashin et al, 2022). FLCs are inherently adaptable and can handle complex and nonlinear systems more effectively than PID controllers, without the need for precise mathematical models.…”
Section: Introductionmentioning
confidence: 99%