Intelligent 3-D sensing in automated manufacturing processes

Abstract

This paper focuses on the design of an intelligent, three-dimensional (3-D) sensing system applying artificial intelligence methodologies for quality assurance in automated manufacturing processes. An efficient 3-D object-oriented knowledge base and reasoning algorithm is developed. The knowledge base includes knowledge concerning the products, manufacturing processes, and inspection methods. The products knowledge base contains properties design and manufacturing. The manufacturing and inspection knowledge bases include various manufacturing techniques, criteria for detection and diagnosis of defects, and standards and limitations on various decision-making actions. A fast and reliable assurance of product quality may be achieved through fault detection and diagnosis, using symbolic knowledge processing combined with numerical analysis of data. Incorporated with the reasoning algorithms, the knowledge base assists in the design process anticipating manufacturing problems and assuring specified end product properties. The knowledge base is regularly updated using feedback of the inspection results. An inexpensive and accurate, non-contact 3-D range data measurement system is developed. In this system, multiple laser light stripes are projected onto the product and a single CCD camera is utilized to record the scene. The distortions in the projected line pattern are due to the orientation variations and surface curvature of the object. Utilizing a linear relation between the projected line distortion and surface depth, range data is recovered from a single camera image. The surface terrain information may be converted into the curvature, orientation, and depth of the shape to incorporate into the symbolic 3-D object-oriented knowledge base and reasoning algorithms.