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This research is focused on developing trajectory planning tools for the automotive painting industry. The geometric complexity of automotive surfaces and the complexity of the spray patterns produced by modern paint atomizers combine to make this a challenging and interesting problem. This paper documents our efforts to develop computationally tractable analytic deposition models for electrostatic rotating bell (ESRB) atomizers, which have recently become widely used in the automotive painting industry. The models presented in this paper account for both the effects of surface curvature as well as the deposition pattern of ESRB atomizers in a computationally tractable form, enabling the development of automated trajectory generation tools. We present experimental results used to develop and validate the models, and verify the interaction between the deposition pattern, the atomizer trajectory, and the surface curvature. Limitations of the deposition model with respect to predictions of paint deposition on highly curved surfaces are discussed.Note to Practitioners-The empirical paint deposition models developed herein, which are fit to experimental data, offer a significant improvement over models that are typically used in industrial robot simulations. The improved simulation results come without the computational cost and complexity of finite element methods. The models could be incorporated, as is, into existing industrial simulation tools, provided the users are cognizant of the model limitations with respect to highly curved surfaces. Although the models are based on readily available information, incorporating the models into existing robot simulation software would likely require support from the software vendor.

The paper reviews the development of loss reserving models over the past, classifying them according to an elementary taxonomy. The taxonomic components include (1) the algebraic structure of the model, (2) the form of its parameter estimation, (3) whether or not it is explicitly stochastic, and (4) whether or not its parameters evolve over time. Particular attention is given to one of the higher species of model, involving complex structure, optimal estimation, and evolutionary parameters. A generalisation of the Kalman filter is considered as a basis of adaptive loss reserving in this case. Real life numerical examples are provided. Some implications of this type of data analysis for loss reserving are discussed, with particular reference to the form of data set used. The use of triangular arrays is questioned, and alternatives examined. Again, real life numerical examples are provided.