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The proposed model of the neural network describes the task of planning the assembly sequence on the basis of predicting the optimal assembly time of mechanical parts. In the proposed neural approach, the k-means clustering algorithm is used. In order to find the most effective network, 10,000 network models were made using various training methods, including the steepest descent method, the conjugate gradients method, and Broyden–Fletcher–Goldfarb–Shanno algorithm. Changes to network parameters also included the following activation functions: linear, logistic, tanh, exponential, and sine. The simulation results suggest that the neural predictor would be used as a predictor for the assembly sequence planning system. This paper discusses a new modeling scheme known as artificial neural networks, taking into account selected criteria for the evaluation of assembly sequences based on data that can be automatically downloaded from CAx systems.

The aim of this study is first to determine the effect of the discharge energy on the surface microgeometry of aluminum samples created by electrical discharge machining (EDM). Secondly, an additional purpose is to demonstrate the differences between the geometric multiscale methods: length-, area-scale, and curvature. Eleven samples were manufactured using discharge energies ranging from 0.486 mJ to 1389.18 mJ and, subsequently, measured with focus variation microscopy. Standard ISO and multiscale parameters were calculated and used for surface discrimination and regression analysis. The results of linear, logarithmic, and exponential regression analyses revealed a strong correlation (R2 > 0.9) between the geometrical features of the surface topography and the discharge energy. The approach presented in this paper shows that it is possible to shape surface microgeometry by changing the energy of electrical discharges, and these dependencies are visible in various scales of observation. The similarities of the results produced by curvature and length-scale methods were observed, despite the significant differences in the essence of those methods.

A B S T R A C TArticle presents the methods of detecting defects within material with the use of active infrared thermovision. During the study ABS and PVC samples were used inside which internal structure defects and defects of glue conjunction between components were modeled. During combining composite materials with the use of glue joints, there is a problem with homogenous distribution of the glue layer on the surface of an element, which results in the creation of defects in joint structure and the decline of active surface of adhesion forces on the combined materials. It is then necessary to control the quality of the conjunction between the glued surfaces. The use of non-contact diagnostic methods allows to analyze a larger surface which conditions in more efficient quality control process. In the study, external heat excitation was used -optical excitation with periodic variable signal (LockIn method) and unit step excitation (Pulse method). The methods of analysis of the obtained thermograms are presented.

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