Santecchia Eleonora, Mengucci Paolo, Gatto Andrea, Bassoli Elena, Defanti Silvio, Barucca Gianni
Consorzio Interuniversitario Nazionale per la Scienza e Tecnologia dei Materiali (INSTM-UdR Ancona), Via Brecce Bianche 12, 60131 Ancona, Italy.
Dipartimento SIMAU, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy.
Materials (Basel). 2019 Jul 24;12(15):2342. doi: 10.3390/ma12152342.
Metal additive manufacturing is now taking the lead over traditional manufacturing techniques in applications such as aerospace and biomedicine, which are characterized by low production volumes and high levels of customization. While fulfilling these requirements is the strength of metal additive manufacturing, respecting the tight tolerances typical of the mentioned applications is a harder task to accomplish. Powder bed fusion (PBF) is a class of additive manufacturing in which layers of metal powder are fused on top of each other by a high-energy beam (laser or electron beam) according to a computer-aided design (CAD) model. The quality of raw powders for PBF affects the mechanical properties of additively manufactured parts strongly, and therefore it is crucial to avoid the presence of any source of contamination, particularly cross-contamination. In this study, the identification and quantification of cross-contamination in powders of Ti-6Al-4V and maraging steel was performed using scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS) techniques. Experimental results showed an overall good reliability of the developed method, opening the way for applications in machine learning environments.
金属增材制造目前在航空航天和生物医学等应用领域领先于传统制造技术,这些领域的特点是产量低且定制程度高。虽然满足这些要求是金属增材制造的优势,但要满足上述应用中典型的严格公差则是一项更艰巨的任务。粉末床熔融(PBF)是一类增材制造技术,其中金属粉末层根据计算机辅助设计(CAD)模型通过高能束(激光或电子束)逐层熔融在一起。用于PBF的原始粉末质量会强烈影响增材制造零件的机械性能,因此避免任何污染源的存在至关重要,尤其是交叉污染。在本研究中,使用扫描电子显微镜(SEM)和能量色散光谱(EDS)技术对Ti-6Al-4V和马氏体时效钢粉末中的交叉污染进行了识别和量化。实验结果表明所开发方法总体具有良好的可靠性,为机器学习环境中的应用开辟了道路。