Rezaei Mohammad Reza, Houshmand Mahmoud, Fatahi Valilai Omid
Department of Industrial Engineering, Sharif University of Technology, Tehran, Tehran, Iran.
Department of Mathematics & Logistics, Jacobs University Bremen, Bremen, Bremen, Germany.
PeerJ Comput Sci. 2021 Aug 10;7:e629. doi: 10.7717/peerj-cs.629. eCollection 2021.
Additive manufacturing, artificial intelligence and cloud manufacturing are three pillars of the emerging digitized industrial revolution, considered in industry 4.0. The literature shows that in industry 4.0, intelligent cloud based additive manufacturing plays a crucial role. Considering this, few studies have accomplished an integration of the intelligent additive manufacturing and the service oriented manufacturing paradigms. This is due to the lack of prerequisite frameworks to enable this integration. These frameworks should create an autonomous platform for cloud based service composition for additive manufacturing based on customer demands. One of the most important requirements of customer processing in autonomous manufacturing platforms is the interpretation of the product shape; as a result, accurate and automated shape interpretation plays an important role in this integration. Unfortunately despite this fact, accurate shape interpretation has not been a subject of research studies in the additive manufacturing, except limited studies aiming machine level production process. This paper has proposed a framework to interpret shapes, or their informative two dimensional pictures, automatically by decomposing them into simpler shapes which can be categorized easily based on provided training data. To do this, two algorithms which apply a Recurrent Neural Network and a two dimensional Convolutional Neural Network as decomposition and recognition tools respectively are proposed. These two algorithms are integrated and case studies are designed to demonstrate the capabilities of the proposed platform. The results suggest that considering the complex objects which can be decomposed with planes perpendicular to one axis of Cartesian coordination system and parallel withother two, the decomposition algorithm can even give results using an informative 2D image of the object.
增材制造、人工智能和云制造是工业4.0中新兴数字化工业革命的三大支柱。文献表明，在工业4.0中，基于智能云的增材制造发挥着关键作用。考虑到这一点，很少有研究实现智能增材制造与面向服务的制造范式的整合。这是由于缺乏实现这种整合的先决框架。这些框架应该基于客户需求为增材制造创建一个用于基于云的服务组合的自主平台。自主制造平台中客户处理的最重要要求之一是对产品形状的解读；因此，准确且自动化的形状解读在这种整合中起着重要作用。不幸的是，尽管如此，除了针对机器级生产过程的有限研究外，准确的形状解读尚未成为增材制造研究的主题。本文提出了一个框架，通过将形状或其信息丰富的二维图片分解为更简单的形状来自动解读它们，这些简单形状可以根据提供的训练数据轻松分类。为此，提出了两种算法，分别应用递归神经网络和二维卷积神经网络作为分解和识别工具。将这两种算法进行了整合，并设计了案例研究来展示所提出平台的能力。结果表明，考虑到可以用垂直于笛卡尔坐标系的一个轴且平行于其他两个轴的平面分解的复杂物体，分解算法甚至可以使用物体的信息丰富的二维图像给出结果。
