Laser-induced breakdown spectroscopy and stoichiometry to identify various types of defects in metal-additive manufacturing parts

Lin, Jingjun; Yang, Jiangfei; Huang, Yutao; Lin, Xi; Che, Changjin

doi:10.1039/d3ja00060e

Cited by 4 publications

(1 citation statement)

References 21 publications

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“…The importance of the features recorded by the RF classifier showed that the spectral intensity of Fe i 414.234 nm and Al i 396.054 nm and the spectral kurtosis in the wavelength range of 484–490 nm and 508–518 nm were the most effective features for porosity identification. Yang et al 20,21 used LIBS technology combined with four classifiers, naive Bayes (NB), k -nearest neighbor, decision tree and RF, to classify and identify defective and non-defective samples of AM parts. The results showed that the RF model had the best recognition effect.…”

Section: Introductionmentioning

confidence: 99%

The semi-quantitative analysis of hole defects in metal additive manufacturing components using LIBS

Lin,

Hao,

Yang

et al. 2024

J. Anal. At. Spectrom.

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Section: Introductionmentioning

confidence: 99%

The semi-quantitative analysis of hole defects in metal additive manufacturing components using LIBS

Lin,

Hao,

Yang

et al. 2024

J. Anal. At. Spectrom.

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A practical path planning method for optimal repair paths between multiple small-size defects

Li,

Pan,

Yang

et al. 2024

RPJ

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Purpose This study aims to solve the problem of repair path planning between multiple small-size defects in the field of additive manufacturing (AM) repair by using Python-based ant colony algorithm (ACO). The optimal parameter combination scheme is obtained by discussing the influencing factors of parameters in the ACO. Design/methodology/approach The effects of the information heuristic factor α, the expected heuristic factor ß and the pheromone volatile factor ρ on the simulation results were investigated by designing a three-factor and three-level orthogonal experiment. The fast convergence of ACO in finding the optimal solution of multiple small-size defect repair path problem is proved by comparing the simulation results with those of genetic algorithm (GA) on the same data set. Findings The ACO can effectively solve the repair path planning problem between multiple small-size defects by optimizing the parameters. In the case of 50 defect locations, the simulation results of the ACO with optimized parameters are 159.8 iterations and 3,688 average path lengths, while the GA has 4,027.2 average path lengths under the same data set and the same number of iterations, and by comparison, it is proved that the ACO can find the optimal solution quickly in the small-size defects repair path planning problem, which greatly improves the efficiency of defect repair. Originality/value The parameter-optimized ACO can be quickly applied to the planning problem of repair paths between multiple small-size defects in the field of AM repair, which can better improve the defect repair efficiency and reduce the waste of resources.

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Research on Automotive Scrap Metal Classification Method Using Laser-Induced Breakdown Spectroscopy and Two-Step Clustering Algorithm

Lin,

Dai,

Che

et al. 2023

Preprint

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In the recycling of scrap metal, the establishment of the classification database of recyclables has the advantages of fast classification speed and high analysis accuracy. However, the classification and recycling of unknown samples become highly significant due to the extensive variety of standard metal samples and the challenges in obtaining them. In this study, a method for multi-element classification of automotive scrap metals in general environmental conditions was achieved by utilizing Laser-Induced Breakdown Spectroscopy (LIBS) and Two-Step Clustering Algorithm (K-means, Hierarchical Clustering). The two unsupervised learning algorithms were employed to cluster the LIBS spectral data of 60 automotive scrap metal samples rapidly and hierarchically. Three rare metal elements and three elements for distinguishing metal categories were selected to meet the recycling requirements. After applying the MSC (Multiplicative Scatter Correction) to the spectral data for calibration, the initial clustering clusters were determined using the DB index, CH index, and silhouette coefficient. Then, the Kruskal-Wallis test was conducted on each cluster to check the significance. And the clusters that failed the test were split and reclustered until all clusters met the significance criterion (α=0.05). The accuracy of the proposed method for classifying the collected automotive scrap metals reached 97.6%. This indicates the great potential of this method in the field of automotive scrap metal classification.

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Laser-induced breakdown spectroscopy and stoichiometry to identify various types of defects in metal-additive manufacturing parts

Abstract: Various defects of additive manufacturing (AM) components seriously impact their performance, which restricts the industry’s development process. Therefore, component defects should be classified quickly and treated in different ways. The...

Cited by 4 publications

References 21 publications

The semi-quantitative analysis of hole defects in metal additive manufacturing components using LIBS

The semi-quantitative analysis of hole defects in metal additive manufacturing components using LIBS

A practical path planning method for optimal repair paths between multiple small-size defects

Research on Automotive Scrap Metal Classification Method Using Laser-Induced Breakdown Spectroscopy and Two-Step Clustering Algorithm

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