This paper presents the development and evaluation of neural network models using a small input–output dataset to predict the thermal behavior of a high-speed motorized spindles. Different neural multi-output regression models were developed and evaluated using Keras, one of the most popular deep learning frameworks at the moment. ANN was developed and evaluated considering the following: the influence of the topology (number of hidden layers and neurons within), the learning parameter, and validation techniques. The neural network was simulated using a dataset that was completely unknown to the network. The ANN model was used for analyzing the effect of working conditions on the thermal behavior of the motorized grinder spindle. The prediction accuracy of the ANN model for the spindle thermal behavior ranged from 95% to 98%. The results show that the ANN model with small datasets can accurately predict the temperature of the spindle under different working conditions. In addition, the analysis showed a very strong effect of type coolant on spindle unit temperature, particularly for intensive cooling with water.

As manufacturing technologies advance, the integration of artificial neural networks in machining high-hardness materials and optimization of multi-objective parameters is becoming increasingly prevalent. By employing modeling and optimization strategies during the machining of such materials, manufacturers can improve surface roughness and tool life while minimizing cutting time, tool vibrations, and cutting forces. In this paper, the aim was to analyze the impact of input parameters (cutting speed, feed rate, depth of cut, and insert radius) on surface roughness and cutting forces during the machining of 90MnCrV7 using feed-forward neural network models and SHAP analysis. Afterward, multi-criteria optimization was applied to determine the optimal parameter levels to achieve minimum surface roughness and cutting forces using the modified PSI-TOPSIS method. According to the SHAP analysis, the insert radius has the most significant impact on the surface roughness and passive force, while in the multi-criteria analysis, according to ANOVA results, the insert radius has the most significant impact on all considered outputs. The results show that an insert radius of 0.8 mm, a cutting speed of 260 m/min, a feed rate of 0.08 mm, and a depth of cut of 0.5 mm are the optimal combination of input parameters.

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.