L. Cao, Wentao Guo, Binyan He, Weihong Li, Xufeng Huang, Y. Zhang, Wang Cai, Qi Zhou
{"title":"In-situ monitoring of the small changes in process parameters with multi-sensor fusion during LPBF","authors":"L. Cao, Wentao Guo, Binyan He, Weihong Li, Xufeng Huang, Y. Zhang, Wang Cai, Qi Zhou","doi":"10.1088/1361-6501/ad5ea5","DOIUrl":null,"url":null,"abstract":"\n The small changes in process parameters have significant influences on the stability of laser powder bed fusion (LPBF). Therefore, monitoring the small changes in process parameters is particularly important. This paper proposed a machine learning (ML)-based multi-sensor fusion approach to monitor the LPBF processing state by combining photodiode, acoustic, and visual signals. In order to extract the motion features of the melt pool more accurately and describe its transient changes, an ellipse adjustment algorithm is proposed to segment the melt pool images, eliminating the interference of spatters. The motion features combined with preprocessed acoustic signals and photodiode signals to identify melting states during small changes in process parameters. The proposed ML-based multi-sensor fusion approach achieves impressive prediction accuracies of 99.9% for identifying the fluctuations in the process parameters. The results demonstrate that the proposed method can accurately identify small changes in process parameters, which is of great significance for improving the process stability and providing reliable guidance in subsequent work.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Measurement Science and Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1088/1361-6501/ad5ea5","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
The small changes in process parameters have significant influences on the stability of laser powder bed fusion (LPBF). Therefore, monitoring the small changes in process parameters is particularly important. This paper proposed a machine learning (ML)-based multi-sensor fusion approach to monitor the LPBF processing state by combining photodiode, acoustic, and visual signals. In order to extract the motion features of the melt pool more accurately and describe its transient changes, an ellipse adjustment algorithm is proposed to segment the melt pool images, eliminating the interference of spatters. The motion features combined with preprocessed acoustic signals and photodiode signals to identify melting states during small changes in process parameters. The proposed ML-based multi-sensor fusion approach achieves impressive prediction accuracies of 99.9% for identifying the fluctuations in the process parameters. The results demonstrate that the proposed method can accurately identify small changes in process parameters, which is of great significance for improving the process stability and providing reliable guidance in subsequent work.
期刊介绍:
Measurement Science and Technology publishes articles on new measurement techniques and associated instrumentation. Papers that describe experiments must represent an advance in measurement science or measurement technique rather than the application of established experimental technique. Bearing in mind the multidisciplinary nature of the journal, authors must provide an introduction to their work that makes clear the novelty, significance, broader relevance of their work in a measurement context and relevance to the readership of Measurement Science and Technology. All submitted articles should contain consideration of the uncertainty, precision and/or accuracy of the measurements presented.
Subject coverage includes the theory, practice and application of measurement in physics, chemistry, engineering and the environmental and life sciences from inception to commercial exploitation. Publications in the journal should emphasize the novelty of reported methods, characterize them and demonstrate their performance using examples or applications.