Abstract
The major challenge faced by a quality assurance (QA) system applied to small series production (SSP) is to guarantee the needed quality level already at the first run. Its called first time right on time. The SSP has some particular characteristics as the great diversity of product types and the continuous introduction of new products. Thus, the QA system has to adapt itself constantly to the new production conditions and support continuous process improvements. In this context, this paper presents the development of intelligent systems for QA in the diagnosis of SSP defects. The two systems developed are based on Artificial Intelligence techniques (Bayesian Networks, Expert Systems and Multiagent Systems) that analyze information from the metrological systems (such as a machine vision system) in the SSP line. The goal is to ensure that the cause of a defect will be fixed. The paper will present the context of the SSP, describe two solutions for quality assurance in SSP and will finish with the presentation of the results in the context of a SSP line for Printed Circuit Boards (PCB) mounting.
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