A Framework to Optimize Laser Welding Process by Machine Learning in a SME Environment

Piat, Jean-Rémi; Dafflon, Baudouin; Bentaha, Mohand Lounes; Gerphagnon, Yannick; Moalla, Néjib

doi:10.1007/978-3-031-25182-5_42

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…In computer science, ML is used for various tasks, such as natural language processing [1][2][3][4][5], image recognition [6][7][8], or computer vision [9][10][11][12][13][14]. In engineering, ML is applied in the optimization and control of complex systems [15][16][17][18], the prediction of equipment failures [19][20][21], or the enhancement of manufacturing processes [22][23][24][25]. ML is also extensively used in materials science for materials discovery [26][27][28][29][30], property prediction [31][32][33][34], and accelerated materials design [35][36][37].…”

Section: Introductionmentioning

confidence: 99%

X-ray Diffraction Data Analysis by Machine Learning Methods—A Review

Surdu,

Győrgy

2023

Applied Sciences

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X-ray diffraction (XRD) is a proven, powerful technique for determining the phase composition, structure, and microstructural features of crystalline materials. The use of machine learning (ML) techniques applied to crystalline materials research has increased significantly over the last decade. This review presents a survey of the scientific literature on applications of ML to XRD data analysis. Publications suitable for inclusion in this review were identified using the “machine learning X-ray diffraction” search term, keeping only English-language publications in which ML was employed to analyze XRD data specifically. The selected publications covered a wide range of applications, including XRD classification and phase identification, lattice and quantitative phase analyses, and detection of defects and substituents, as well as microstructural material characterization. Current trends in the field suggest that future efforts pertaining to the application of ML techniques to XRD data analysis will address shortcomings of ML approaches related to data quality and availability, interpretability of the results and model generalizability and robustness. Additionally, future research will likely incorporate more domain knowledge and physical constraints, integrate with quantum physical methods, and apply techniques like real-time data analysis and high-throughput screening to accelerate the discovery of tailored novel materials.

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