Modern manufacturing has to deal with global competition, in which customers have high purchasing power. Production efficiency and rapid response to customer demand are dominant conditions for enterprises to stay successful. Reconfigurable Manufacturing Systems (RMSs) are designed to have a modular architecture in both mechanical design and control system. The architecture enables change of the machine structure quickly, by adding and removing parts of the system, and by changing the corresponding software programming. It can handle short times to market. This paper presents an ‘Index-Method’ to monitor the reconfiguration of RMS. The method is able to categorise the reconfiguration and related development in seven stages. It focusses specifically on the Independence Axiom. The main goal is to find all relevant parameters to cause interactions, and to decouple them. The solution, aiming to be scientifically vigorous and practically applicable, was applied to a true case; the development of a manufacturing system for an inkjet print head for industrial applications. The realisation of the system required the development of new process technology. The index-method may be considered successful. It has the ability to structure the configuration process of RMSs. The method harmonises well with the industry known V-model.
Light scattering is a fundamental property that can be exploited to create essential devices such as particle analysers. The most common particle size analyser relies on measuring the angle-dependent diffracted light from a sample illuminated by a laser beam. Compared to other non-light-based counterparts, such a laser diffraction scheme offers precision, but it does so at the expense of size, complexity and cost. In this paper, we introduce the concept of a new particle size analyser in a collimated beam configuration using a consumer electronic camera and machine learning. The key novelty is a small form factor angular spatial filter that allows for the collection of light scattered by the particles up to predefined discrete angles. The filter is combined with a light-emitting diode and a complementary metal-oxide-semiconductor image sensor array to acquire angularly resolved scattering images. From these images, a machine learning model predicts the volume median diameter of the particles. To validate the proposed device, glass beads with diameters ranging from 13 to 125 µm were measured in suspension at several concentrations. We were able to correct for multiple scattering effects and predict the particle size with mean absolute percentage errors of 5.09% and 2.5% for the cases without and with concentration as an input parameter, respectively. When only spherical particles were analysed, the former error was significantly reduced (0.72%). Given that it is compact (on the order of ten cm) and built with low-cost consumer electronics, the newly designed particle size analyser has significant potential for use outside a standard laboratory, for example, in online and in-line industrial process monitoring.
MULTIFILE
