In case of complex parts machining or multidirectional machining in multi-part fixtures the error compensation in multi-dimensional decision space poses a difficult problem. The article focuses on the limitation of defective products by means of systematic increase of the remaining error budget due to correction of the setup data. A vectorial equation for machine tool space description is presented. The development of geometric dimensioning and tolerancing scheme to the levels connected with the setup data is proposed. The optimization algorithm used here is based on the paradigm particle swarm optimization (PSO), but it includes a few significant modifications inspired by the growth of the coral reef thus the name of the method-coral reefs inspired particle swarm optimization (CRIPSO). CRIPSO has been compared with three other popular metaheuristics: classic PSO, genetic algorithm, and cuckoo optimization algorithm. There is a practical example in this article.

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In the article, a method of recognizing the position, location and orientation of irregular machine parts with a complex outline of the external contour is suggested. Recognition is performed on the basis of a raster image that has undergone preliminary processing in a vision system. The developed method is based on matching the finite set of points retrieved from the external contour of the classified object. The originality of the approach consists of the form of the object pattern taken into account in determining the degree of similarity to reference objects. The similarity index was defined in the category of fuzzy sets. A cellular automaton has been proposed to generate the outline of the perimeter quickly while simultaneously smoothing it out. The process chain necessary for the recognition function to be implemented has been presented. The developed method has been illustrated with an example related to recognizing the position, location and orientation of a rear wheel pin. The results were compared to the classic Blair-Bliss, Danielsson and Haralick shape coefficients. The sensitivity of the developed method to a change in the scale and rotating the object as well as errors in outlining its edge has been tested. The obtained results confirmed the advantageous features of the developed method, both in the aspect of the recognition quality and the practically viable time of waiting for the results of processing.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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The presented paper contains the results of research aimed at developing optimal strategies for controlling the feed rate in the friction drilling process. In particular, the use of linear variable feed rate for individual drilling stages and adaptive feed rate control have been tested. The experiments were carried out with the use of a CNC machine tool equipped with an axial force and torque sensor. Correlation between thrust force and torque was shown, respectively, in relation to the feed drive load and the drive of machine tool spindle. Based on this, a feed rate sensorless control strategy was created to protect against excessive and long-term overload both of the tool and the drives. The following assessment criteria were considered: drilling cycle time, maximum values of thrust and torque, maximum values of feed drive load and drive of machine tool spindle, maximum power and energy effect in the form of work necessary to perform during the drilling process and forming the hole flange. The obtained test results, made for low-carbon steel with a tungsten carbide tool, indicate the advantage of the approach based on the linear variable feed rate and adaptive control over the traditional drilling process based on the step change of the feed rate, according to the recommendations given by the tool manufacturers.