A novel bilateral stream neural network for melt pool monitoring during laser direct energy deposition

Article type: Research Article

Authors: Wang, Zhongan^a | Li, Honghai^a | Pang, Minghao^a | Wu, Yingna^{a; *} | Yang, Rui^a | Wu, Zhiwei^{b; *} | Cai, Guoshuang^b

Affiliations: [a] ShanghaiTech University, School of Creativity and Art, CASE | [b] Nanjing Huirui Photoelectric Technology Co., Ltd

Correspondence: [*] Corresponding authors. Yingna Wu. E-mail: [email protected] and Zhiwei Wu. E-mail: [email protected].

Abstract: Detection and classification methods for the melt pool state in laser direct energy deposition (L-DED) can significantly help predict defects and mechanical properties of L-DED metal parts. Although traditional machine learning algorithms based on physical modeling methods and convolutional neural networks have recently been introduced into melt pool state identification, these methods rely on complex artificially designed features or cannot simultaneously detect defects in multiple dimensions. In this paper, a novel bilateral stream neural network was designed for melt pool identification, which performs defect identification in two label dimensions simultaneously. Two sets of single-channel experiments were designed to collect the dataset captured by a high-speed camera. By cutting the metal parts and marking them with professional equipment operated by professionals, the dataset was labeled according to the bonding condition and dilution rate criteria. Without an additive model structure, the model achieved 95.2% accuracy in identifying defects in the bonding condition and 92.8% in determining deficiencies in the dilution rate. In order to explain the identification mechanism of the model, the CAM method was utilized for the visual display of the model recognition process, which provides a potential application solution for the online monitoring method of the L-DED.

Keywords: Laser direct energy deposition, melt pool state, bilateral stream neural network

DOI: 10.3233/JIFS-236589

Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7727-7738, 2024