An Experimental and Simulation Study for Powder Injection Multitrack Laser Cladding of P420 Stainless Steel on AISI 1018 Steel for Selected Mechanical Properties

Nazemi, Navid; Urbanic, Jill

doi:10.1115/1.4037604

Cited by 12 publications

(3 citation statements)

References 31 publications

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“…Aluminum and its alloys are widely used in aerospace industry; they have been efficiently clad with other novel metals to improve their surface properties [67]. Stainless steel is used in automobile and household applications [68,69]. Titanium and its alloys are used in the medical sector [70].…”

Section: Lst Applicationsmentioning

confidence: 99%

Laser Surface Treatment

Siddiqui¹,

Dubey²

2020

Engineering Steels and High Entropy-Alloys

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Laser surface treatment (LST) utilizes intense thermal energy of laser beam for modification, alloying, and cladding surface of substrate materials. In LST very high cooling rates of 104-106°C/s can be achieved. Such high cooling rate arrests the possibilities of segregation in the case of multicomponent systems. Moreover, very narrow heat-affected zone (HAZ) and easy automation make it suitable for large-scale industrial production. LST depends on many process parameters such as laser power, scan speed, focal length, spot size, substrate temperature, and type of material. Selection of proper range of process parameters for good surface quality is essential. Pores and cracks may arise due to improper selection of parameters. Multilayered, high-entropy, thermal barrier coatings using LST with good bonding with substrates have been developed.

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Section: Lst Applicationsmentioning

confidence: 99%

Laser Surface Treatment

Siddiqui¹,

Dubey²

2020

Engineering Steels and High Entropy-Alloys

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“…Generally, there are three main methods to construct the relationship model between input machining parameters and output machining results: the finite element method, the phenomenology method, and the machine learning method, to investigate the relationship between process parameters and their responses in the laser cladding process [ 10 , 11 ]. The finite element method [ 12 , 13 ] normally constructs models through physical driving to make predictions. Kumar et al [ 14 ] proposed and solved a set of dimensionless transport equations to study the laser metal deposition process, and they calculated the deposition trajectory geometry, dilution ratio, and maximum molten pool temperature.…”

Section: Introductionmentioning

confidence: 99%

Prediction Method for High-Speed Laser Cladding Coating Quality Based on Random Forest and AdaBoost Regression Analysis

Xv,

Sun,

Zhang

2024

Materials

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The initial melting quality of a high-speed laser cladding layer has an important impact on its post-treatment and practical application. In this study, based on the repair of hydraulic support columns of coal mining machines, the influence of high-speed laser cladding process parameters on the quality of Fe-Cr-Ni alloy coatings was investigated to realize the accurate prediction of coating quality. The Taguchi orthogonal method was used to design the L25(56) test. The prediction models of the relationship between the cladding process and the coating quality were established using the Random Forest (RF) and AdaBoost (Adaptive Boosting, AB) algorithms, respectively. Then, the prediction accuracy of the two models was compared, and the process parameter features were screened for importance evaluation. The results show that the AB prediction model is more accurate than the RF prediction model and more sensitive to abnormal data. The importance evaluation based on the AdaBoost model shows that the scanning speed has a great influence on the height and surface roughness of the coating. On the other hand, the overlap rate is the most important factor in controlling the dilution ratio and near-surface grain size of high-speed laser melting coatings. In addition, the micro-hardness of the coating and the thermal effect of the substrate can be effectively enhanced by adjusting the laser power and scanning speed. Finally, it was verified that the AB prediction model could accurately estimate the quality indexes of the coating with a prediction error less than 6%. The results show that it is feasible to predict the quality of high-speed laser cladding with the AB algorithm. It provides a basis for the adjustment of process parameters in the subsequent quality control process of cladding.

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“…The behaviour of the liquid in the pool (its flow and temperature) has also been studied [16,17], with consequences concerning the metallurgical properties of the deposited layer (structure, morphology, etc.). Other works [18,19] concern the mechanical properties (such as thermal stresses, residual stresses, or hardness) of tracks. However, the list of the above papers is far from exhaustive.…”

Section: Introductionmentioning

confidence: 99%

Modelling the Laser Cladding of Geometrically More Complex Tracks and Its Experimental Verification

Doležel

Kotlan

Hamar

et al. 2021

Metals

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In this paper, a methodology for depositing wear- and corrosion-resistant layers of geometrically more complex tracks on metal substrates is presented. The corresponding mathematical model includes the temperature field produced by the laser beam, the field of residual mechanical stresses, and the efficiency of utilization of the delivered powder material. The computations are realized using the finite element method, with a substantial improvement in processing the time-variable geometry of the investigated system being found, based on the introduction of two specific matrices that characterize both the surface on which the tracks are cladded as well as the track itself. The proposed technique is illustrated by cladding an angled helix on a metal surface. Selected results are successfully verified by experiments.

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An Experimental and Simulation Study for Powder Injection Multitrack Laser Cladding of P420 Stainless Steel on AISI 1018 Steel for Selected Mechanical Properties

Cited by 12 publications

References 31 publications

Laser Surface Treatment

Laser Surface Treatment

Prediction Method for High-Speed Laser Cladding Coating Quality Based on Random Forest and AdaBoost Regression Analysis

Modelling the Laser Cladding of Geometrically More Complex Tracks and Its Experimental Verification

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