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Towards efficient powder quality control in additive manufacturing via an in situ capable device and methodology leveraging multispectral machine learning
Journal of Manufacturing Processes ( IF 6.2 ) Pub Date : 2024-04-05 , DOI: 10.1016/j.jmapro.2024.03.102
Clemens Maucher , Jonas Gerold , Hans-Christian Möhring

Additive manufacturing (AM) processes enables the fabrication of highly complex parts that cannot be manufactured using conventional manufacturing methods. Constant and specified material properties are of crucial importance for these highly optimized components. AM processes like laser-based powder bed fusion for metals or laser-based direct energy deposition use fine metallic powder as the feedstock material. Powder properties can be influenced due to, for example, reuse cycles or environmental influences. These changes have a significant impact on the properties of the components to be manufactured. It is therefore particularly important to monitor the quality of the powder used throughout the entire process. However common methods for powder qualification often are expensive, time consuming and cannot be applied to powders in situ the built process.

中文翻译:

通过利用多光谱机器学习的原位设备和方法,在增材制造中实现高效的粉末质量控制

增材制造 (AM) 工艺可以制造使用传统制造方法无法制造的高度复杂的零件。恒定且特定的材料特性对于这些高度优化的组件至关重要。基于激光的金属粉末床熔合或基于激光的直接能量沉积等增材制造工艺使用细金属粉末作为原料。粉末特性可能会因再利用循环或环境影响等因素而受到影响。这些变化对要制造的部件的性能产生重大影响。因此,监控整个过程中所用粉末的质量尤为重要。然而,粉末鉴定的常用方法通常昂贵、耗时,并且不能应用于现场构建过程中的粉末。
更新日期:2024-04-05
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