Additive Manufacturing ( IF 11.0 ) Pub Date : 2021-07-27 , DOI: 10.1016/j.addma.2021.102191 Marshall V. Johnson 1 , Kevin Garanger 2 , James O. Hardin 3 , J. Daniel Berrigan 3 , Eric Feron 2 , Surya R. Kalidindi 1
High mix, low volume processes such as additive manufacturing (AM) offer tremendous promise for increasing the customization in manufacturing but are hindered by the lack of efficient methods for identifying process parameters for complex new geometries exhibiting the desired performance. The search over the process space can be automated with analysis tools that can be applied in a time and resource efficient manner such that ambitious print designs are not dissuaded by the cost of process parameter discovery. In this work, we propose an image analysis tool that can classify spanning prints as one of five process-relevant archetypes, invariant of the span dimensions. We describe a modular design of the tool such that simple adjustments to image processing parameters allow for compatibility with different print processes and environments. Furthermore, we demonstrate how this tool may be incorporated into a fully automated workflow on multiple AM systems to facilitate rapid autonomous process parameter discovery and/or deeper scientific understanding.
中文翻译:
用于识别和校正增材制造过程中自支撑结构的通用人工智能工具
诸如增材制造 (AM) 之类的高混合、小批量工艺为增加制造中的定制化提供了巨大的希望,但由于缺乏有效的方法来识别具有所需性能的复杂新几何形状的工艺参数,因此受到阻碍。可以使用分析工具自动搜索过程空间,这些分析工具可以以时间和资源高效的方式应用,这样雄心勃勃的印刷设计不会被过程参数发现的成本所劝阻。在这项工作中,我们提出了一种图像分析工具,可以将跨度打印分类为五个与过程相关的原型之一,跨度尺寸不变。我们描述了该工具的模块化设计,以便对图像处理参数的简单调整允许与不同的打印过程和环境兼容。