No abstract

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.

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.

No abstract

In this paper, an assembly sequence planning system, based on artificial neural networks, is developed. The problem of artificial neural network itself is largely related to symmetry at every stage of its creation. A new modeling scheme, known as artificial neural networks, takes into account selected DFA (Design for Assembly) rating factors, which allow the evaluation of assembly sequences, what are the input data to the network learning and then estimate the assembly time. The input to the assembly neural network procedure is the sequences for assembling the parts, extended by the assembly’s connection graph that represents the parts and relations between these parts. The operation of a neural network is to predict the assembly time based on the training dataset and indicate it as an output value. The network inputs are data based on selected DFA factors influencing the assembly time. The proposed neural network model outperforms the available assembly sequence planning model in predicting the optimum assembly time for the mechanical parts. In the neural networks, the BFGS (the Broyden–Fletcher–Goldfarb–Shanno algorithm), steepest descent and gradient scaling algorithms are used. The network efficiency was checked from a set of 20,000 test networks with randomly selected parameters: activation functions (linear, logistic, tanh, exponential and sine), the number of hidden neurons, percentage set of training and test dataset. The novelty of the article is therefore the use of parts of the DFA methodology and the neural network to estimate assembly time, under specific production conditions. This approach allows, according to the authors, to estimate which mechanical assembly sequence is the most advantageous, because the simulation results suggest that the neural predictor can be used as a predictor for an assembly sequence planning system.

The aim of the study was to evaluate tribological properties of ultra-high-molecular-weight polyethylene (UHMWPE). This study concerns the areas of both mechanical and biomechanical application fields, including chain guides and knee or hip joint liner replacement. The presented paper describes conducted research on tribological properties of UHMWPE with tribological testing station UMT-2 Bruker. This testing station is capable of conducting tribological tests of ferrous, non-ferrous metals, plastics, ceramics, composites, and various types of coatings, under both "dry" and liquid and solid lubricating agents. Tests were performed under various conditions, differing in relative speed and the load of interacting frictional pair. Moreover, test duration and the performed number of cycles were considered.

In this paper thin-walled part deformation during finishing turning process caused by gripping force of hydraulic lathe chuck was investigated. Bearing ring was taken as an example of thin-walled part undergo finishing turning operation. Finite Element Method (FEM) was used to define the deformation of examined part. The aim of presented research was to compare the deformation of bearing ring caused by gripping force of hydraulic 3-jaw chuck and 6-jaw chuck for different values of total gripping force. The data obtained from conducted simulations allowed to evaluate the influence of gripping force on machining part deformation which is directly related with its geometrical accuracy.