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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.

The performances of high-speed machine tools depend not only on the speed, power, torque, dynamic and static stiffness, but also on the thermal behavior of the spindle. These parameters directly affect the productivity and quality of machining operations. This paper presents a 3-D finite element thermal model, which was based on the thermo mechanical bearing model and the numerical model of the spindle. Based on thermo mechanical analysis of bearings with angular contact, generated heat and thermal contact resistance are determined for each position of the ball. To provide the most accurate analysis possible in determining thermal contact resistance , bearings are divided into several zones based on the geometry of their cross-section. The aforementioned constraints have been applied to the 3-D FEM model which allowed for establishing temperature field distribution, and spindle thermal balance. In order to prove the efficacy of the proposed model, experimental measurements of spindle and bearing temperatures were done by using thermocouples and thermal imager.

In industry, the capability to predict the tool point frequency response function (FRF) is an essential matter in order to ensure the stability of cutting processes. Fast and accurate identification of contact parameters in spindle-holder-tool assemblies is very important issue in machining dynamics analysis. This work is an attempt to illustrate the utility of soft computing techniques in identification and prediction contact parameters of spindle-holder-tool assemblies. In this paper, three soft computing techniques, namely, genetic algorithm (GA), simulated annealing (SA), and particle swarm optimization (PSO) were used for identification of contact dynamics in spindle-holder-tool assemblies. In order to verify the proposed identification approaches, numerical and experimental analysis of the spindle-holder-tool assembly was carried out and the results are presented. Finally, a model based on the adaptive neural fuzzy inference system (ANFIS) was used to predict the dynamical contact parameters at the holder-tool interface of a spindle-holder-tool assembly. Accuracy and performance of the ANFIS model has been found to be satisfactory while validated with experimental results.