Additive manufacturing (AM), also known as 3D printing, is one of the most promising digital manufacturing technologies, thanks to its potential to produce highly complex geometries rapidly. AM has been promoted from a prototyping methodology to a serial production platform for which precise process monitoring and control strategies to guarantee the accuracy of products are required. This need has motivated practitioners to focus on designing process monitoring tools to improve the accuracy of produced geometries. In line with the emerging interest, in the current investigation, a novel strategy is proposed which uses functional representation of in-plane contours to come up with statistical boxplots with the goal of detecting outlying AM products. The method can be used for process monitoring during AM production to automatically detect defective products in an online fashion. To ensure the considered method has an acceptable potential, different complex 3D geometries are considered and undergo different types of stochastic perturbations to collect data for outlier detection. The results of the conducted simulation are very promising and reveal the reliability of the proposed method for detecting products with statistically significant deformation.
翻译:添加剂制造(AM)又称3D印刷,是最有希望的数字制造技术之一,因为它有可能迅速产生高度复杂的地理比例。AM已从原型方法推广到序列生产平台,为此需要精确的程序监测和控制战略,以保证产品的准确性。这需要促使从业人员侧重于设计过程监测工具,以提高所生产的地理比例的准确性。根据当前调查中新出现的兴趣,提出了一项新战略,利用机内轮廓的功能代表来生成统计插盒,目的是探测离AM产品。该方法可用于在AM生产期间进行过程监测,以便自动在线检测有缺陷的产品。为了确保所考虑的方法具有可接受的潜力,对不同的复杂三维地形进行了考虑,并进行了不同类型的随机干扰,以收集数据进行外部检测。进行模拟的结果非常有希望,并揭示了以具有统计意义的变形为目的的检测产品的拟议方法的可靠性。