Quality and Process Control of Nd: YAG - Laser Welding Using fuzzy Pattern Recognition Techniques for Multisensor Systems
Quality monitoring is necessary for a stable manufacturing, especially in the case of laser welding processes. During the project a system was developed which allows the analysis of in process data by fuzzy pattern recognition methods. This resulted in improved prediction of seam irregularities as compared with conventional methods. The work was carried out in the framework of the MAINE cooperative project funded by the Free State of Saxony.
Inspection and supervision by means of hierarchical fuzzy classifiers
Abstract The well-tried method of fuzzy classification is extended to hierarchical class structures to allow combinations of databased and rule-based class representations. An appropriate design method is suggested, the relationships to system theory and fuzzy logic are shown and software features and application examples are described. The main application fields are diagnostics and supervision of technical processes. Model design is based on disjunctive aggregation of atomic classes, obtained by fuzzification of vectors in the feature space (learning samples). Membership functions of all fuzzy sets are described by an uniform multidimensional parametric concept. Rule-based components are an appropriate formal tool to represent multimodal class models and to integrate explicit expert knowledge. Class models can be seen as components of several types of systems (e.g. fuzzy controller) which transform measured data into discrete fuzzy sets. For this reason the initiation of further development of new fuzzy methods is expected. Two application cases with use of a class structure and its effect are studied.
Recent progress in engineering near-infrared persistent luminescence nanoprobes for time-resolved biosensing/bioimaging
Persistent luminescence nanoprobes (PLNPs) can remain luminescent after ceasing excitation. Due to the ultra-long decay time of persistent luminescence (PersL), autofluorescence interference can be efficiently eliminated by collecting PersL signal after autofluorescence decays completely, thus the imaging contrast and sensing sensitivity can be significantly improved. Since near-infrared (NIR) light shows reduced scattering and absorption coefficient in penetrating biological organs or tissues, near-infrared persistent luminescence nanoprobes (NIR PLNPs) possess deep tissue penetration and offer a bright prospect in the areas of in vivo biosensing/bioimaging. In this review, we firstly summarize the design of different types of NIR PLNPs for biosensing/bioimaging, such as transition metal ions-doped NIR PLNPs, lanthanide ions-doped NIR PLNPs, organic molecules-based NIR PLNPs, and semiconducting polymer self-assembled NIR PLNPs. Notably, organic molecules-based NIR PLNPs and semiconductor self-assembled NIR PLNPs, for the first time, were introduced to the review of PLNPs. Secondly, the effects of different types of charge carriers on NIR PersL and luminescence decay of NIR PLNPs are significantly emphasized so as to build up an in-depth understanding of their luminescence mechanism. It includes the regulation of valence band and conduction band of different host materials, alteration of defect types, depth and concentration changes caused by ion doping, effective radiation transitions and energy transfer generated by different luminescence centers. Given the design and potential of NIR PLNPs as long-lived luminescent materials, the current challenges and future perspective in this rapidly growing field are also discussed.