Correlations of Melt Pool Geometry and Process Parameters During Laser Metal Deposition by Coaxial Process Monitoring

Ocylok, Sörn; Alexeev, Eugen; Mann, Stefan; Weisheit, Andreas; Wissenbach, Konrad; Kelbassa, Ingomar

doi:10.1016/j.phpro.2014.08.167

Cited by 97 publications

(38 citation statements)

References 7 publications

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“…Single weld tracks are placed next to each other in order to form a single layer with thickness varying from 0.1 mm to several millimeters depending on the process parameters (velocity, powder feed rate, and laser power) [ 1 , 7 ]. In order to coat wide surfaces on 3D complex geometries, side overlapping of the individual laser traces is required.…”

Section: Introductionmentioning

confidence: 99%

Laser Direct Metal Deposition of 2024 Al Alloy: Trace Geometry Prediction via Machine Learning

2018

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Laser direct metal deposition is an advanced additive manufacturing technology suitably applicable in maintenance, repair, and overhaul of high-cost products, allowing for minimal distortion of the workpiece, reduced heat affected zones, and superior surface quality. Special interest is growing for the repair and coating of 2024 aluminum alloy parts, extensively utilized for a wide range of applications in the automotive, military, and aerospace sectors due to its excellent plasticity, corrosion resistance, electric conductivity, and strength-to-weight ratio. A critical issue in the laser direct metal deposition process is related to the geometrical parameters of the cross-section of the deposited metal trace that should be controlled to meet the part specifications. In this research, a machine learning approach based on artificial neural networks is developed to find the correlation between the laser metal deposition process parameters and the output geometrical parameters of the deposited metal trace produced by laser direct metal deposition on 5-mm-thick 2024 aluminum alloy plates. The results show that the neural network-based machine learning paradigm is able to accurately estimate the appropriate process parameters required to obtain a specified geometry for the deposited metal trace.

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Section: Introductionmentioning

confidence: 99%

Laser Direct Metal Deposition of 2024 Al Alloy: Trace Geometry Prediction via Machine Learning

2018

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“…The energy absorption in the substrate leads to a net heat increase, causing the surface to melt and therefore increase its absorption coefficient [20]. In the end, if too high energy densities are used for the employed powder flow rate, a re-melting of the substrate is obtained, which causes an inappropriate relationship between powder flow and laser power, making the heat-affected zone of the substrate bigger than the clad width [21]. When comparing the obtained results for equal laser power and powder flow values (Figure 3), the height of the clads is higher for the minimum value of process speed (13 mm/s) than those made with the maximum value (27 mm/s).…”

Section: Individual Clad Experimentsmentioning

confidence: 99%

Analysis of the Process Parameter Influence in Laser Cladding of 316L Stainless Steel

Alvarez

Montealegre²,

Pulido-Jiménez

et al. 2018

JMMP

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Laser Cladding is one of the leading processes within Additive Manufacturing technologies, which has concentrated a considerable amount of effort on its development. In regard to the latter, the current study aims to summarize the influence of the most relevant process parameters in the laser cladding processing of single and compound volumes (solid forms) made from AISI 316L stainless steel powders and using a coaxial nozzle for their deposition. Process speed, applied laser power and powder flow are considered to be the main variables affecting the laser cladding in single clads, whereas overlap percentage and overlapping strategy also become relevant when dealing with multiple clads. By setting appropriate values for each process parameter, the main goal of this paper is to develop a processing window in which a good metallurgical bond between the delivered powder and the substrate is obtained, trying simultaneously to maintain processing times at their lowest value possible. Conventional metallography techniques were performed on the cross sections of the laser tracks to measure the effective dimensions of clads, height and width, as well as the resulting dilution value. Besides the influence of the overlap between contiguous clads and layers, physical defects such as porosity and cracks were also evaluated. Optimum process parameters to maximize productivity were defined as 13 mm/s, 2500 W, 30% of overlap and a 25 g/min powder feed rate.

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“…The energy absorption in the substrate leads to a net heat increase, causing the surface to melt and therefore increase its absorption coefficient [20]. In the end, this results in remelting of the substrate, making the heat affected zone surrounding the clad to be bigger than with a more appropriate relationship between powder flow and laser power [21].…”

Section: Individual Clad Experimentsmentioning

confidence: 99%

Analysis of the Process Parameter Influence in Laser Cladding of 316L Stainless Steel

Alvarez¹,

Montealegre²,

Pulido-Jiménez³

et al. 2018

Preprint

View full text Add to dashboard Cite

Laser Cladding is one of the leading processes within Additive Manufacturing technologies, a fact which has concentrated an important amount of effort on its development. In regard to the latter, the current study aims to summarize the influence of the most relevant process parameters in the laser cladding processing of single and compound volumes (solid forms) made from AISI 316L stainless steel powders and using a coaxial nozzle for deposition. Process speed, applied laser power and powder flow are considered to be the main variables affecting laser cladding in single clads, meanwhile overlap percentage and overlapping strategy become also relevant when dealing with multiple clads. By means of setting appropriate values of each process parameter, the main goal of this paper is to develop a processing window in which a good metallurgical bond between the delivered powder and substrate is obtained, trying simultaneously to maintain processing times in their lowest value possible. Conventional metallography techniques were performed on the cross sections of the laser tracks to measure the effective dimensions of clads for dilution analysis, height and width for the values of overlap between contiguous clads and layers, and also to analyze them for physical defects such as porosity and cracks. The resulting solid piece was 8 mm high at 800 mm/min.

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Correlations of Melt Pool Geometry and Process Parameters During Laser Metal Deposition by Coaxial Process Monitoring

Cited by 97 publications

References 7 publications

Laser Direct Metal Deposition of 2024 Al Alloy: Trace Geometry Prediction via Machine Learning

Laser Direct Metal Deposition of 2024 Al Alloy: Trace Geometry Prediction via Machine Learning

Analysis of the Process Parameter Influence in Laser Cladding of 316L Stainless Steel

Analysis of the Process Parameter Influence in Laser Cladding of 316L Stainless Steel

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