Automated assembly inspection using a multiscale algorithm trained on synthetic images

Includes bibliographical references.An important part of a robust automated assembly process is an accurate and efficient method for the inspection of finished assemblies. This paper presents a novel multiscale assembly inspection algorithm that is used to detect errors in an assembled product. The algorithm is trained on synthetic images generated using the CAD model of the different components of the assembly. The CAD model guides the inspection algorithm through its training stage by addressing the different types of variations that occur during manufacturing and assembly. Those variations are classified into those that can affect the functionality of the assembled product and those that are unrelated to its functionality. Using synthetic images in the training process adds to the versatility of the technique by removing the need to manufacture multiple prototypes and control the lighting conditions. Once trained on synthetic images, the algorithm can detect assembly errors by examining real images of the assembled product. The effectiveness of the system is illustrated on a typical mechanical assembly.This work was supported by National Science Foundation grant number CDR 8803017 to the Engineering Research Center for Intelligent Manufacturing Systems, National Science Foundation grant number MIP93-00560, an AT&T Bell Laboratories PhD Scholarship, and the NEC corporation

Camera and light placement for automated assembly inspection

Includes bibliographical references.Visual assembly inspection can provide a low cost, accurate, and efficient solution to the automated assembly inspection problem, which is a crucial component of any automated assembly manufacturing process. The performance of such an inspection system is heavily dependent on the placement of the camera and light source. This article presents new algorithms that use the CAD model of a finished assembly for placing the camera and light source to optimize the performance of an automated assembly inspection algorithm. This general-purpose algorithm utilizes the component material properties and the contact information from the CAD model of the assembly, along with standard computer graphics hardware and physically accurate lighting models, to determine the effects of camera and light source placement on the performance of an inspection algorithm. The effectiveness of the algorithms is illustrated on a typical mechanical assembly.This work was supported by National Science Foundation grant number CDR 8803017 to the Engineering Research Center for Intelligent Manufacturing Systems, National Science Foundation grant number MIP93-00560, an AT&T Bell Laboratories PhD Scholarship, and the NEC Corporation

Video and image systems engineering education for the 21st century

Includes bibliographical references.We are developing a new graduate program at Purdue in Video and Image Systems Engineering (VISE). The project is comprised of three parts: a new curriculum centered around a degree option in VISE to be earned as part of the Masters or Ph.D. degrees; a state-of-the-art lecture/laboratory facility for instruction, laboratory experiments, and project and homework activities in VISE courses; and enhancement of existing courses and development of new courses in the VISE area.Supported by an Image Systems Engineering Grant from Hewlett-Packard Company

CAD driven multiscale approach to automated inspection, A

Includes bibliographical references (page V-400).In this paper we develop a general multiscale stochastic object detection algorithm for use in an automated inspection application. Information from a CAD model is used to initialize the object model and guide the training phase of the algorithm. An object is represented as a stochastic tree, where each node of the tree is associated with one of the various object components used to locate and identify the part. During the training phase a number of model parameters are estimated from a set of training images, some of which are generated from the CAD model. The algorithm then uses a fast multiscale search strategy to locate and identify the subassemblies making up the object tree. We demonstrate the performance of the algorithm on a typical mechanical assembly.This work was supported by an AT&T Bell Laboratories PhD Scholarship, the NEC Corporation, National Science Foundation grant number MIP93-00560, and National Science Foundation grant number CDR 8803017 to the Engineering Research Center for Intelligent Manufacturing Systems

Multiscale assembly inspection algorithm, A

Includes bibliographical references.An important aspect of robust automated assembly is an accurate and efficient method for the inspection of finished assemblies. This novel algorithm is trained on synthetic images generated using the CAD model of the different components of the assembly. Once trained on synthetic images, the algorithm can detect assembly errors by examining real images of the assembled product.This work was supported by the NEC Corporation, National Science Foundation grant number CDR8803017 to the Engineering Research Center for Intelligent Manufacturing Systems, National Science Foundation grant number MIP93-00560, and an AT&T Bell Laboratories Ph.D. Scholarship