This paper describes a fully automatic quality system for injection molding. The proposed system includes an
on-line measurement platform with a digital camera, a methodology for adaptive design of experiments (DOE),
statistical modeling, process monitoring, and a closed loop process control. The system has been tested in the
manufacturing of plastic parts for mobile phones.
This paper presents a hierarchical method, based on a deterministic variant of the self-organizing map, that provides an elegant solution for automated surface processing, e.g. for robot painting and sand-blasting. Given a set of data points in arbitrary order from the object surface, the proposed method is able to generate a path, where the robot hand position and its direction are optimized using separate criteria, and the tool path is smooth and covers the object uniformly. Input data may come from a laser measurement system, CAD model, digital camera, or from human assisted object digitizing system. The algorithm is reliable and easy to implement, and a good alternative for costly manual training of a robot.
Fault diagnostics of rotating machines requires the concept of novelty. For a set of similar new machines, coming form the assembly line, the typical features of vibration differ from one machine to another. Consequently, one must make a specific model for every machine and test if new, possibly harmful, vibrations will occur during the use of the machine. The classification system must discriminate between familiar and unfamiliar patterns with inclination to reject unseen patterns rather than accept badly distorted familiar ones. In this paper we define the problem and present a solution, based on a self-organizing map. It allows us to cluster different normal runtime characteristics of machines and classify new measurements. Detection of novelty is made by examining the difference between class features of old and new observations.
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