Novel parameter estimation schemes in microsystems

This paper presents two novel estimation methods that are designed to enhance our ability of observing, positioning, and physically transforming the objects and/or biological structures in micromanipulation tasks. In order to effectively monitor and position the microobjects, an online calibration method with submicron precision via a recursive least square solution is presented. To provide the adequate information to manipulate the biological structures without damaging the cell or tissue during an injection, a nonlinear spring-mass-damper model is introduced and mechanical properties of a zebrafish embryo are obtained. These two methods are validated on a microassembly workstation and the results are evaluated quantitatively

Human variability, task complexity and motivation contribution in manufacturing

This paper is a preliminary study of the human contribution to variability in manufacturing industry and how motivation and learning play a key role in this contribution. The longer term aim is to incorporate this understanding in a methodology, using principles and guidelines, that aims to help in the design of intelligent automation that reduces product variability. This paper reports on the early stages that are concerned with understanding relationships between human-induced product variability, task complexity and human characteristics and capabilities. Two areas have been selected for initial study in manufacturing industry: (a) the relationship between manual task complexity and product variability and (b) the relationship between employee motivational factors and learning behaviours. The paper discusses the progress to date in conducting initial empirical studies and surveys in industry and draws tentative conclusions of the value of this knowledge to the overall objective of intelligent automation

Feature and viewpoint selection for industrial car assembly

Abstract. Quality assurance programs of today’s car manufacturers show increasing demand for automated visual inspection tasks. A typical example is just-in-time checking of assemblies along production lines. Since high throughput must be achieved, object recognition and pose estimation heavily rely on offline preprocessing stages of available CAD data. In this paper, we propose a complete, universal framework for CAD model feature extraction and entropy index based viewpoint selection that is developed in cooperation with a major german car manufacturer