Optimization of surface roughness and dimensional accuracy in LPBF additive manufacturing

Cao, Longchao; Li, Jingchang; Hu, Jiexiang; Liu, Huaping; Wu, Yuda; Zhou, Qi

doi:10.1016/j.optlastec.2021.107246

Cited by 114 publications

(36 citation statements)

References 27 publications

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“…The dot-line curves of PV and EV are related to the left ordinate, while the bars of RE are related to the right ordinate. Although the overall RE of R is larger than that of TEC and DWR, it is acceptable compared with the maximal relative error of the predictive model on surface roughness in the related literature [45] . In general, EM fits the three responses well and can be effective in the prediction of the fiber LST process.…”

Section: Validation Of Emmentioning

confidence: 86%

“…The R value of each experiment was measured by a confocal microscope. The one-dimensional formula of surface roughness for a surface with a profile length L can be expressed as where f(x) is the variation of local surface height at the point x compared with the average height of the whole profile with the assumption that the whole profile is even [45] . f m is the height of m positions along the profile length L, and the surface roughness can be formulated as…”

Section: Processing Qualitymentioning

confidence: 99%

“…Furthermore, in the validation of EM phase shown in Figure 6, additional experiments are used to validate the accuracy of EM. The accuracy threshold can refer to the relevant indicators in other literature: energy consumption [32] , cross-section geometry [48,49] , and surface roughness [45] . The maximum relative errors of 3%, 35%, and 35% are, respectively, set as the accuracy threshold of metamodels for TEC, R, and DWR in this work.…”

Section: Construction Of Emmentioning

confidence: 99%

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A prediction approach of fiber laser surface treatment using ensemble of metamodels considering energy consumption and processing quality

Wu¹,

Zhang²,

Jiang³

et al. 2022

Green Manuf Open

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Laser surface treatment (LST) is essential for advanced manufacturing but is extremely energy intensive. Being energy-aware is imperative as the industry pays increasing attention to energy management and environmental protection. However, existing literature mainly focuses on the laser-material interaction in LST, while few studies have considered energy consumption when investigating the processing quality. In this article, three metamodels (Kriging, RBF, and SVR) are integrated into an ensemble of metamodels (EM) by suitable weight coefficients, and the EM incorporates the predictive advantages of different metamodels. The EM establishes the relationship between laser process parameters (laser power, scan speed, and defocusing amount) and three outputs (total energy consumption, surface roughness, and depth-width ratio of LST track). The effectiveness of the presented prediction approach is validated by the leave-one-out method and additional experiments. Furthermore, the main influences of process parameters on the three outputs are studied. According to the technique for order preference by similarity to an ideal solution (TOPSIS), the optimal process parameter is Group No. 2, with the relative closeness of 78.04%, while the worst one is Group No. 13, with the relative closeness of 2.21%. The presented prediction approach can serve as a reliable foundation in the energy-aware application of laser processing.

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Section: Validation Of Emmentioning

confidence: 86%

Section: Processing Qualitymentioning

confidence: 99%

Section: Construction Of Emmentioning

confidence: 99%

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A prediction approach of fiber laser surface treatment using ensemble of metamodels considering energy consumption and processing quality

Wu¹,

Zhang²,

Jiang³

et al. 2022

Green Manuf Open

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“…Using a data-driven framework to handle the data transfer during the lifecycle of the metallic part would enable the smart transformation of metal AM processes [166]. Data-driven models to test manufacturability and test design for AM rules have been suggested by Ko et al [167], while data-driven models to predict surface roughness of printed parts were suggested by Cao et al [168]. Majeed et al [166] proposed a data-driven framework to handle real-time and non-real-time data for the product lifecycle of AM.…”

Section: Am Framework Developmentmentioning

confidence: 99%

Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review

et al. 2021

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One of the main issues hindering the adoption of parts produced using laser powder bed fusion (L-PBF) in safety-critical applications is the inconsistencies in quality levels. Furthermore, the complicated nature of the L-PBF process makes optimizing process parameters to reduce these defects experimentally challenging and computationally expensive. To address this issue, sensor-based monitoring of the L-PBF process has gained increasing attention in recent years. Moreover, integrating machine learning (ML) techniques to analyze the collected sensor data has significantly improved the defect detection process aiming to apply online control. This article provides a comprehensive review of the latest applications of ML for in situ monitoring and control of the L-PBF process. First, the main L-PBF process signatures are described, and the suitable sensor and specifications that can monitor each signature are reviewed. Next, the most common ML learning approaches and algorithms employed in L-PBFs are summarized. Then, an extensive comparison of the different ML algorithms used for defect detection in the L-PBF process is presented. The article then describes the ultimate goal of applying ML algorithms for in situ sensors, which is closing the loop and taking online corrective actions. Finally, some current challenges and ideas for future work are also described to provide a perspective on the future directions for research dealing with using ML applications for defect detection and control for the L-PBF processes.

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“…There are numerous AM process variables, e.g., beam parameters [ 1 , 2 , 3 ], scanning parameters (pattern, speed, power) [ 4 , 5 , 6 , 7 , 8 , 9 , 10 ], powder characteristics and distribution [ 11 , 12 , 13 ], which can induce internal defects and largely affect the surface roughness. For example, Yang et al.…”

Section: Introductionmentioning

confidence: 99%

Predictive model to design for high cycle fatigue of stainless steels produced by metal additive manufacturing

Serjouei

Afazov²

2022

Heliyon

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Optimization of surface roughness and dimensional accuracy in LPBF additive manufacturing

Cited by 114 publications

References 27 publications

A prediction approach of fiber laser surface treatment using ensemble of metamodels considering energy consumption and processing quality

A prediction approach of fiber laser surface treatment using ensemble of metamodels considering energy consumption and processing quality

Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review

Predictive model to design for high cycle fatigue of stainless steels produced by metal additive manufacturing

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