研究目的
To address the lack of process monitoring and control system in electron beam additive manufacturing (EBAM) by developing a digital electronic imaging system prototype and macroscopic process quality analysis software for in-process monitoring.
研究成果
The electronic imaging system prototype and macroscopic process quality analysis software demonstrated significant potential for in-process EBAM monitoring. The prototype achieved a maximum magnification of 95 and successfully generated images with different FOVs. The software verified the ability to monitor process quality on a layer-by-layer basis. Future work will explore the system's suitability under actual EBAM operating conditions.
研究不足
The study was conducted at room temperature, and challenges remain for in-process monitoring at elevated temperatures and dealing with metallization from metal powder vaporization. The prototype's spatial resolution is limited by the electron beam diameter.
1:Experimental Design and Method Selection
The study involved the development of a digital electronic imaging system prototype and software for monitoring the EBAM process. The prototype was designed to detect secondary electrons (SE) and backscattered electrons (BSE) from interactions between the electron beam and the processing area.
2:Sample Selection and Data Sources
Experiments were conducted on the top layer of an EBAM test build at room temperature. The test build was designed with nine imaging locations to represent individual components in a real EBAM build.
3:List of Experimental Equipment and Materials
The prototype consisted of an electron sensor, signal differential amplifier, data logger, image generation software, and a standard computer. The EBAM machine used was a commercial Arcam A1-GE Additive.
4:Experimental Procedures and Operational Workflow
Two sets of single-layer electronic imaging experiments were conducted: one to generate images with a range of magnifications and the other to generate images with different fields of view (FOV). The software was developed to analyze the quality of the EBAM process on a layer-by-layer basis.
5:Data Analysis Methods
Image noise was removed using a median filter, and contrast was enhanced through histogram equalization. The software compared reference images from the STL design with workpiece images to assess macroscopic process quality.
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