Predictive modeling and the effect of process parameters on the hardness and bead characteristics for laser-cladded stainless steel

Alam, Mohammad K.; Urbanic, Jill; Nazemi, Navid; Edrisy, Afsaneh

doi:10.1007/s00170-017-0898-5

Cited by 37 publications

(12 citation statements)

References 25 publications

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“…The intent of RSM is to build a statistical prediction model based on experimental results. Afterwards, analysis of variance was utilized to analyze the model [ 17 ]. Multiple regression analysis was employed to build and analyze the model between the input parameters and the output.…”

Section: Methodsmentioning

confidence: 99%

Computational and Experimental Investigation of Micro-Hardness and Wear Resistance of Ni-Based Alloy and TiC Composite Coating Obtained by Laser Cladding

Lian

Zhang

et al. 2019

Materials

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The influence of processing parameters on the micro-hardness and wear resistance of a Ni-based alloy and titanium carbide (TiC) composite cladding layer was studied. Mathematical models were developed to predict the micro-hardness and wear resistance of the cladding layer by controlling the laser cladding processing parameters. Key processing parameters were the laser power, scanning speed, gas flow, and TiC powder ratio. The models were validated by analysis of variance and parameter optimization. Results show that the micro-hardness is positively correlated with laser power and TiC powder ratio, where the TiC powder ratio shows the most significant impact. The wear volume decreased with an increasing TiC powder ratio. The targets for the processing parameter optimization were set to 62 HRC for micro-hardness and a minimal volume wear. The difference between the model prediction value and experimental validation result for micro-hardness and wear volume were 1.87% and 6.33%, respectively. These models provide guidance to optimize the processing parameters to achieve a desired micro-hardness and maximize wear resistance in a composite cladding layer.

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Section: Methodsmentioning

confidence: 99%

Computational and Experimental Investigation of Micro-Hardness and Wear Resistance of Ni-Based Alloy and TiC Composite Coating Obtained by Laser Cladding

Lian

Zhang

et al. 2019

Materials

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“…Analysis of variance (ANOVA) was used to verify and analyze the fitted models. The significance level (α) was set at 0.05, which is a commonly used level in significance tests [9,20,21]. Experimental validation to verify that the optimized processing parameters and substrate size was conducted with targeting on the desired geometric characteristics.…”

Section: Methodsmentioning

confidence: 99%

Investigation of Geometric Characteristics in Curved Surface Laser Cladding with Curve Path

Lian

Zhang

et al. 2019

Metals

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Laser cladding on curved surfaces is essential in industrial applications for restoration and remanufacturing of high-value parts. This study investigated the influence of different factors on clad width, clad height, and dilution rate in curved surface laser cladding with curved path. Mathematical models were developed using central composite designs to predict these geometric characteristics by controlling laser power, scanning speed, gas flow, and altering the outside radius of the cylindrical substrate. Analysis of variance and response surface methodology indicated that clad width increased with increasing laser power and reducing scanning speed. Clad height positively correlated to laser power and negatively correlated to the outside radius of the cylindrical substrate. Increasing the laser power while decreasing the scanning speed led to an increase in dilution rate. Afterwards, the geometric characteristics of the clad were improved by optimizing these factors with the target to maximize clad width and height as well as to minimize dilution rate. The difference between model predictions and experimental validations for clad width, clad height, and dilution rate were 3.485%, 3.863%, and 6.566%, respectively. The predicted accuracy was verified with these models, and they were able to provide theoretical guidance to predict and control the geometric characteristics of curved surface laser cladding with a curved path.

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“…Each process parameter in laser cladding process including the power, travel speed, material feed rate, the contact tip to work piece distance, and the focal length has a distinct effect on the geometry and the mechanical properties of the bead [2][3] [4][5] [6]. Several experimental investigations have been found in the literature review to analyse the effect of process parameters on clad bead geometry and clad mechanical characteristics.…”

Section: Figure 1 Schematic Diagram Of Laser Cladding Processmentioning

confidence: 99%

Machine Learning Approaches for Predicting Geometric and Mechanical Characteristics for Single P420 Laser Beads Clad onto an AISI 1018 Substrate

Mohajernia

Mirazimzadeh

Pasha

et al. 2021

Preprint

Self Cite

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For effective bead deposition based additive manufacturing (AM) processes such as directed energy deposition, the final mechanical and physical properties should be predicted in tandem with the bead geometry characteristics. Experimental approaches to investigate the final geometry and the mechanical properties are costly, and simulation solutions are time-consuming. Alternative artificial intelligent (AI) systems are explored as they are a powerful approach to predict such properties. In the present study, the geometrical properties, as well as the mechanical properties (residual stress and hardness) for single bead clads are investigated. Experimental data is used to calibrate multi-physics finite element models, and both data sets are used to seed the AI models. The adaptive neuro-fuzzy inference system (ANFIS) and a feed-forward back-propagation artificial neural network (ANN) system are utilized to explore their effectiveness in the 1D (discrete values), 2D (cross sections), and 3D (complete bead) domains. The prediction results are evaluated using the mean relative error measure. The ANFIS predictions are more precise than those from the ANN for the 1D and 2D domains, but the ANN had less error for the 3D scenario. These models are capable of predicting the geometrical and the mechanical properties values very well, including capturing the mechanical properties in transient regions; however, this research should be extended for multi- bead scenarios before a conclusive ‘best approach’ strategy can be determined.

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Predictive modeling and the effect of process parameters on the hardness and bead characteristics for laser-cladded stainless steel

Cited by 37 publications

References 25 publications

Computational and Experimental Investigation of Micro-Hardness and Wear Resistance of Ni-Based Alloy and TiC Composite Coating Obtained by Laser Cladding

Computational and Experimental Investigation of Micro-Hardness and Wear Resistance of Ni-Based Alloy and TiC Composite Coating Obtained by Laser Cladding

Investigation of Geometric Characteristics in Curved Surface Laser Cladding with Curve Path

Machine Learning Approaches for Predicting Geometric and Mechanical Characteristics for Single P420 Laser Beads Clad onto an AISI 1018 Substrate

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