Obtaining a dynamic model of the robotic manipulator is a complex task. With the growing application of machine learning (ML) approaches in modern robotics, a question arises of using ML for dynamic modeling. Still, due to the large amounts of data necessary for this approach, data collection may be time and resource-intensive. For this reason, this paper aims to research the possibility of synthetic dataset creation by using pre-existing dynamic models to test the possibilities of both applications of such synthetic datasets, as well as modeling the dynamics of an industrial manipulator using ML. Authors generate the dataset consisting of 20,000 data points and train seven separate multilayer perceptron (MLP) artificial neural networks (ANN)—one for each joint of the manipulator and one for the total torque—using randomized search (RS) for hyperparameter tuning. Additional MLP is trained for the total torsion of the entire manipulator using the same approach. Each model is evaluated using the coefficient of determination ($R^2$) and mean absolute percentage error (MAPE), with 10-fold cross-validation applied. With these settings, all individual joint torque models achieved $R^2$ scores higher than 0.9, with the models for first four joints achieving scores above 0.95. Furthermore, all models for all individual joints achieve MAPE lower than 2%. The model for the total torque of all joints of the robotic manipulator achieves weaker regression scores, with the $R^2$ score of 0.89 and MAPE slightly higher than 2%. The results show that the torsion models of each individual joint, and of the entire manipulator, can be regressed using the described method, with satisfactory accuracy.

Laser powder bed fusion (LPBF) process has a great ability to produce complex AlSi10Mg 3D components with uncommon degrees of freedom for broad span of applications in different industries. Presence of the microstructural imperfections such as porosity, dependent on the parameters of the process can be detrimental to the printed products for different engineering applications. Parameters of the process and different post-processing heat and surface treatments are recognized for decrease of the occurrence of microstructural defects and for the improvement of the mechanical properties. The influence of laser power by applying four different laser speeds at one layer thickness with a constant hatch distance, on the microstructure and microhardness of the AlSi10Mg alloy was examined in this paper. The goal of the research was to determine whether increasing the laser speed will have a significant impact on the change in microstructure and the appearance of porosity in the tested samples.

This paper analyses the application of a genetic algorithm (GA) for the purpose of designing the control system with separately excited DC motor controlled according to the rotor angle. The presented research is based on the utilization of a mathematical model designed with separate electrical and mechanical sub-systems. Such an approach allows fine-tuning of PID controllers by using an evolutionary procedure, mainly GA. For purpose of PID tuning, the new fitness function which combines several step response parameters with the aim of forming a unique surface which is then minimized with a genetic algorithm. From the results, it can be seen that the elitism-based algorithm achieved better results compared to the eligibility-based selection. Such an algorithm achieved a fitness value of 0.999982 resulting in a steady-state error of 0.000584 rad. The obtained results indicate the possibility of applying a GA in the parameterization of the PID controller for DC motor control.

Predicting the quality of the robot end-effector grasp quality during an industrial robot manipulator operation can be an extremely complex task. As is often the case with such complex tasks, Artificial Intelligence methods may be applied to attempt...

Electropolishing at high current densities without agitation of the electrolyte results in a pitting phenomenon that produces dimple-like surface features. Although pitting is unfavorable in the electropolishing process, its effect on surface modification, such as surface texturing, has not been thoroughly investigated. Surface topography and chemical composition analyses of electropolished steel revealed surface pits and an oxide surface layer, indicating the presence of surface texture and coating. The resulting surface is characterized by negative skewness and high kurtosis values. The tribological behavior of the electropolished steel-bronze pair is investigated by evaluating coefficients of friction and bronze wear using sliding tests conducted in mixed and boundary lubrication regimes. The results are compared to those of the ground steel-bronze pair. In the mixed and upper range of the boundary lubrication regime, coefficients of friction reduction up to 30% and shorter running-in phases are observed for electropolished steel (electropolished steel μ_avg = 0.019 vs. ground steel μ_avg = 0.028). In contrast, the coefficient of friction increased in the lower range of boundary lubrication regime by 50% (electropolished steel μ_avg = 0.098 vs. ground steel μ_avg = 0.065). Electropolishing, as a cost- and time-effective method applicable to complex geometries, presents an alternative method for achieving surface modifications aimed at friction reduction and improved tribological behavior for non-conformal contacts in the boundary and mixed lubrication regimes.