A hybrid deep learning model of process-build interactions in additive manufacturing. (October 2020)

• Record Type: Journal Article
• Authors: Mojahed Yazdi, Reza; Imani, Farhad; Yang, Hui
• Is Part Of: Journal of manufacturing systems. Volume 57(2020)
• Page Start: 460
• Page End: 468
• Publication Date: 2020-10
• Subjects: Deep learning -- Wavelet analysis -- Texture analysis -- Additive manufacturing -- In-process Monitoring -- Laser powder bed fusion -- Data analysis -- Artificial intelligence; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2020.11.001
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: Hybrid input deep learning model is proposed for LBPF process monitoring. The underlying physics for identification of process deviations are discussed. The performance of model is validated for real-time application. Wavelet transform and texture analysis are employed to generate statistical features. Abstract: Laser powder bed fusion (LPBF) is a technique of additive manufacturing (AM) that is often used to construct a metal object layer-by-layer. The quality of AM builds depends to a great extent on the minimization of different defects such as porosity and cracks that could occur by process deviation during machine operation. Therefore, there is a need to develop new analytical methods and tools to equip the LPBF process with the inspection frameworks that assess the process condition and monitor the porosity defect in real-time. Advanced sensing is recently integrated with the AM machines to cope with process complexity and improve information visibility. This opportunity lays the foundation for online monitoring and assessment of the in-process build layer. This study presents the hybrid deep neural network structure with two types of input data to monitor the process parameters that result in porosity defect in cylinders' layers. Results demonstrate that statistical features extracted by wavelet transform and texture analysis along with original powder bed images, assist the model in reaching a robust performance. In order to illustrate the fidelity of the proposed model, the capability of the main pipeline is examined and compared with different machine learning models. Eventually, the proposed framework identified the process conditions with an F -score of 97.14%. This salient flaw detection ability is conducive to repair the defect in real-time and assure the quality of the final part before the completion of the process.