The irregularity profiles of steel samples after vapour blasting were measured. A correlation analysis of profile parameters was then carried out. As the result, the following parameters were selected: Pq, Pt, PDq, Pp/Pt and Pku. Surface profiles after vapour blasting were modeled. The modeled surfaces were correctly matched to measured surfaces in 78% of all analyzed cases. The vapour blasting experiment was then carried out using an orthogonal selective research plan. The distance between the nozzle and sample d and the pressure of feed system p were input parameters; selected surface texture coefficients were output parameters. As the result of the experiment, regression equations connecting vapour blasting process parameters p and d with selected profile parameters were obtained. Finally, 2D profiles of steel samples were forecasted for various values of vapour blasting parameters. Proper matching accuracy of modeled to measured profiles was assured in 75% of analyzed cases.
The paper presents the least admissible dimensions of black lines of spatial object images, according to Saliszczew, adjusted to the needs of database generalization. It is pointed out, that the adjusted dimensions are in agreement with the cartographic norm included in the National Map Accuracy Standards , and their application to the generalization 1 will allow, for any map scale, the determination of the: • value of the scale-dependent parameter of the generalization process, without user action; • measure of recognizability of the shortest black line section on the map, what helps to obtain unique results of line generalization; • measure of recognizability of black lines in the image – using a standard (elementary triangle) – helpful in obtaining unique result of line simplification, and an assessment of the process; • recognizability distance between lines of close buildings, securing unique aggregation of them; • verification of spatial object image lines visualization. The new solutions were tested with the Douglas-Peucker (1973) generalization algorithm, modified by the author, which treats the minimal dimensions as geometric attributes, while object classes and their data hierarchy as descriptive attributes. This approach secures uniqueness of results on any level of generalization process, in which data of spatial objects in the DLM model are transformed to conform with the requirements for the DCM model data.