Aim. Temporomandibular disorders (TMD) refer to functional disorders of the masticatory system, temporomandibular joint (TMJ) and masticatory muscles. The main objective of this study was to determine whether and to what extent temporomandibular disorders (TMD) affect the maximum bite force (MBF). Methods. The present study included subjects with and without temporomandibular disorder. The presence of TMD was assessed by means of the Helkimo clinical dysfunction index analysis. We measured the maximum bite pressure (MBP) and occlusal contact area (OCA) by means of a Fuji Prescale Pressure measurement film. Based on the MBP and OCA values obtained, MBF values were determined. Results. The MBF values were significantly lower in patients with TMD compared to subjects without TMD (P<0.0005). MBF values demonstrate a trend, with a tendency towards a decrease in values with the increase in the severity of TMD (P <0.01). OCA was significantly lower in patients with TMD (P<0.05). There was no significant difference between controls and patients with TMD in terms of the MBP (P=0.135).
Conclusion.TMDs have a significant impact on MBF and masticatory muscle action potential. More research is needed to determine the impact of reduced maximum bite force on the functional efficiency of the masticatory system.
Surface quality is one of the most important indicators of the quality of machined parts. The analytical method of defining the arithmetic mean roughness is not applied in practice due to its complexity and empirical models are applied only for certain values of machining parameters. This paper presents the design and development of artificial neural networks (ANNs) for the prediction of the arithmetic mean roughness, which is one of the most common surface roughness parameters. The dataset used for ANN development were obtained experimentally by machining AA7075 aluminum alloy under various machining conditions. With four factors, each having three levels, the full factorial design considers a total of 81 experiments that have to be carried out. Using input factor-level settings and adopting the Taguchi method, the experiments were reduced from 81 runs to 27 runs through an orthogonal design. In this study we aimed to check how reliable the results of artificial neural networks were when obtained based on a small input-output dataset, as in the case of applying the Taguchi methodology of planning a four-factor and three-level experiment, in which 27 trials were conducted. Furthermore, this paper considers the optimization of machining parameters for minimizing surface roughness in machining AA7075 aluminum alloy. The results show that ANNs can be successfully trained with small data and used to predict the arithmetic mean roughness. The best results were achieved by backpropagation multilayer feedforward neural networks using the BR algorithm for training.
In the development of robots and machine tools, in addition to conventional and serial structures, parallel mechanism-based kinematic structures have been used over a longer period of time. Aside from a number of advantages, the irregular shape and relatively small dimensions of the workspace formed by parallel mechanisms rank among the major weaknesses of their application. Accordingly, this fact has to be taken into consideration in the process of designing parallel mechanism-based robots or machine tools. This paper describes the categorization of criteria for the conceptual design of parallel mechanism-based robots or machine tools, resulting from workspace analysis as well as the procedure of their defining. Furthermore, it also presents the designing methodology that was implemented into the program for the creation of a robot or machine tool space model and the optimization of the resulting solution. For verification of the criteria and the programme suite, three common (conceptually different) mechanisms with a similar mechanical structure and kinematic characteristics were used.
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