An adaptive learning algorithm using an artificial neural network (ANN) has been proposed to predict the passive joint position of under-actuated robot manipulator. In this approach, a specific ANN model has been designed and trained to learn a desired set of joint angular positions for the passive joint from a given set of input torque and angular position for the active joint over a certain period of time. Trying to overcome the disadvantages of many used techniques in the literature, the ANNs have a significant advantage of being a model-free method. The learning algorithm can directly determine the position of its passive joint, and can, therefore, completely eliminate the need for any system modelling. Even though it is very difficult in practice, data used in this study were recorded experimentally from sensors fixed on robot's joints to overcome the effect of kinematics uncertainties present in the real world such as ill-defined linkage parameters and backlashes in gear trains. An ANN was trained using the experimentally obtained data and then used to predict the path of the passive joint that is positioned by the dynamic coupling of the active joint. The generality and efficiency of the proposed algorithm are demonstrated through simulations of an under-actuated robot manipulator; finally, the obtained results were successfully verified experimentally.

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The predicted results of the finite element (FE) model of an assembled structure with different types of joints are highly dependent on the mesh size of the FE model. The complexity of the FE model has forced engineers to seek the most efficient techniques for the selection of the appropriate mesh size specifically in obtaining accurate predicted results in normal modes analysis. This paper concerns the investigation into the effects of the mesh sizes and selection technique of the appropriate mesh size in the FE modelling and analysis of the assembled structure with bolted joints. The investigation was carried out by predicting the modal parameters of the FE models with the predefined range of mesh sizes. The predicted results of the FE models were compared with the measured counterparts obtained from the experimental modal analysis (EMA). The total error obtained from the comparison between FE and EMA was recorded. Evaluations were made by comparing the number of nodes and elements of the FE models, percentage of total error, computer processing unit (CPU) elapsed time and memory usage. The outcomes of the evaluations showed that there are significant effects of the mesh sizes on the accuracy, computing time and memory usage of the FE modal analysis of the assembled structure with bolted joints. This work also demonstrated an efficient technique for the selection of the appropriate mesh size in achieving a reliable, efficient and economic FE modelling and analysis of the assembled structure with bolted joints.

Since the level of vibration always depends on the natural frequencies of the system, it is important to know the modal parameters of such system to control failure and provide prevention actions. However, for many mechanical engineering machines or structures, there is a demand and necessity to determine real-life modal parameters using actual operating condition. This type of testing condition cannot be done in lab environment because most of the mechanical structure is big in size and heavy. Thus, the purpose of this paper is to study the natural frequencies of a steel plate by using Operational Modal Analysis (OMA) and Ibrahim Time Domain (ITD). Comparison of results between both approaches will be shown.