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Purpose: As it is already known, modern industry is based on CNC machines, robots, actuators etc. Most of this equipment has in its structure threaded spindles. Installation of the threaded spindles is the most demanding task in mechanical engineering. Methods: The bearings which are designed for this purpose have to meet many requirements regarding bigger number of revolutions, significant axial load, work accuracy, thermal stability etc. Results: In order to ensure thermal stability of this kind of bearings, it is necessary to predict values of the main operating parameters of the bearing: total friction torque as well as temperature. This paper presents prediction of the total friction torque and temperature by axial angular contact ball bearings designed for threaded spindles using ANN (Artificial Neural Network). Conclusion: As the main validating parameter was selected Mean Absolute Percentage Error (MAPE) which was in range 0,18- 1,32% for all ANNs.

For an efficient use of laser cutting technology, it is of great importance to analyze the impact of process parameters on different performance indicators, such as cut quality criteria, productivity criteria, costs as well as environmental performance criteria (energy and resource efficiency). Having this in mind, this study presents the experimental results of CO2laser fusion cutting of AISI 304 stainless steel using nitrogen, with the aim of developing a semi-empirical mathematical model for the estimation of process efficiency as an important indicator of the achievable energy transfer efficiency in the cutting process. The model was developed by relating the theoretical power needed to melt the volume per unit time and used laser power, where the change of kerf width was modeled using an empirical power model in terms of laser cutting parameters such as laser power, cutting speed, and focus position. The obtained results indicated the dominant effect of the focus position on the change in process efficiency, followed by the cutting speed and laser power. In addition, in order to maximize process efficiency and simultaneously ensure high cut quality without dross formation, a laser cutting optimization problem with constraints was formulated and solved. Also, a multi-objective optimization problem aimed at simultaneous optimization of process efficiency and material removal rate was formulated and solved, where the determined set of Pareto non-dominated solutions was analyzed by using the entropy method and multi-criteria decision analysis method, that is, the Technique for Order of Preference by Similarity to Ideal Solution. The optimization results revealed that in order to enhance process efficiency and material removal rate, while ensuring high cut quality without dross formation, focusing the laser beam deep into the bulk of material is needed with particular trade-offs between laser power and cutting speed levels at high pressure levels of nitrogen.

Taking full advantage of what laser cutting technology offers in terms of achieving superb quality cuts at low cost and high production rates requires the optimization of laser cutting parameters. This implies the need to formulate and solve different laser cutting optimization problems. In this article, an optimization model for CO2laser cutting of mild steel is developed. The laser cutting optimization problem was explicitly formulated as a single-objective optimization problem with five non-linear constraints of the equality, inequality and range type. The goal was to determine the laser cutting parameter values so as to maximize the material removal rate while simultaneously considering practical process constraints related to dross formation, kerf width, perpendicularity deviation, surface roughness and severance energy. Two crossed experimental designs of different resolutions were performed in order to define six mathematical models, which were used in the formulation of the optimization problem. For the purpose of optimization, the exhaustive iterative search algorithm was applied, since it determines solutions whose optimality is guaranteed in the given discrete space of input variable values. The practical usability of the developed laser cutting optimization model and the effectiveness of the applied optimization approach were proved while solving a real case study aimed at the optimization of laser cutting parameters for cutting parts for the furnace industry.

It is well known fact that materials play an important role in engineering design. Nowadays over a hundred thousand available materials can be distinguished with constant tendency for increasing the novel designed materials. Therefore material selection process becomes a complex and time consuming task. Selection of the most suitable material for a given application can be regarded as a multi-criteria decision making (MCDM) problem with conflicting and diverse objectives. New MCDM methods have been developed, and existing methods improved, showing that research in the decision-making is important and still valuable. This paper describes the use of recently developed MCDM methods, i.e. Complex Proportional Assessment (COPRAS) and Weighted Aggregated Sum Product Assessment (WASPAS) for selecting the most suitable hard coating material.

Multi-objective optimization of laser cutting for simultaneous improvement of performance characteristics is of great practical importance. In this study a range of value (ROV)-based Taguchi methodology is proposed for multi-objective optimization of laser cutting, i.e. surface roughness, kerf width and burr height in CO2 laser cutting of AISI 304 stainless steel. Laser cutting experiment was conducted based on Taguchi’s L27 experimental design by varying the laser power, cutting speed, assist gas pressure and focus position at three levels. In the proposed methodology based on the experimental data signal to noise ratios as per Taguchi’s method were calculated for each experimental trial upon which decision matrix was defined. Subsequently, multi-criteria decision making problem was solved by the ROV method. The proposed ROV-based Taguchi methodology has relatively simple computational procedure and can be easily applied by engineers for solving different multi-objective optimization problems that occur in real manufacturing environment.

Abrasive water jet machining is frequently used in industry. It is one of the most versatile processes in the world. The basic advantages of abrasive water jet machining is that no heat affected zones or mechanical stresses are left on an abrasive water jet cut surface, high flexibility and small cutting forces. Although this cutting technology includes many advantages, there are some drawbacks. For instance, abrasive water jet cutting can produce tapered edges on the kerf of workpiece being cut. This can limit the potential applications of abrasive water jet cutting, if further machining of the edges is needed to achieve the engineering tolerance required for the part. The machining parameters have a great influence on these phenomena. The aim of this paper is to investigate the cut quality of EN AW-6060 aluminium alloy sheets under abrasive water jets. The experimental results indicate that the feed rate (nozzle traverse speed) of the jet is a significant parameter on the surface morphology.

Optimization of cutting parameters in finish turning of medical stainless steel 316LVM with coated carbide tools using Taguchi method is proposed in this paper. Four cutting parameters namely, insert radius, depth of cut, feed and cutting speed are optimized with considerations of surface roughness as performance characteristic. The effects of cutting parameters on the surface roughness were experimentally investigated. Experimentation was conducted as per Taguchi's orthogonal array. Four cutting parameters with three levels are arranged in L27 orthogonal array. The orthogonal array, measured values of surface roughness, signal-to-noise ratios and analysis of variance are employed to study the surface roughness. Based on the analysis, the optimal cutting parameter settings were determined. Through the confirmation test with optimal cutting parameter settings the effectiveness of the optimization approach are validated. The obtained results have shown that Taguchi method is suitable for optimizing the cutting parameter levels with the minimum number of experiments.

Machining of aluminum and its alloys requires the use of cutting tools with special geometry and material. Since there exists a number of cutting tools for aluminum machining, each with unique characteristics, selection of the most appropriate cutting tool for a given application is very complex task which can be viewed as a multi-criteria decision making (MCDM) problem. This paper is focused on multi-criteria analysis of VCGT cutting inserts for aluminum alloys turning by applying recently developed MCDM method, i.e. weighted aggregated sum product assessment (WASPAS) method. The MCDM model was defined using the available catalogue data from cutting tool manufacturers.

Stainless steels are one of the most important engineering materials widely used in the industry. This paper presents multi-objective optimization of CO2 laser cutting of stainless steel considering different cut quality characteristics and material removal rate (MRR). Laser cutting experiment trials were conducted based on Taguchis L27 experimental design by varying the laser power, cutting speed, assist gas pressure and focus position at three levels. Using obtained experimental data, six mathematical models for the prediction of surface roughness, kerf width, kerf taper angle, width of heat affected zone, dross height and MRR were developed using artificial neural network (ANN). The developed mathematical models were taken as objective functions for the multi-objective optimization using genetic algorithm based on Pareto concept. As a result of multi-objective optimization, five 2-D Pareto fronts were generated covering all combinations of cut quality characteristics and MRR. It was observed that the mathematical relationships in the Pareto fronts between MRR and cut quality characteristics are in some cases linear and in another nonlinear.

In abrasive water jet cutting, the cut quality is of great importance. In this paper, artificial intelligence model was developed for the prediction of cut quality in abrasive water jet cutting of aluminum alloy. To this aim, artificial neural network (ANN) model was developed in terms of workpiece material thickness, traverse rate and abrasive flow rate. Three-layered feedforward ANN model having four hidden neurons trained with backpropagation algorithm with momentum was used for modeling purposes. The mathematical model showed high prediction accuracy with average absolute percentage error of about 3 %. Using the developed ANN model, 3-D graphs, showing the interaction effects of the traverse rate and abrasive flow rate for three different thicknesses, were given. It was showed that ANNs may be used as a good alternative in analyzing the effects of abrasive water jet cutting parameters on the cut quality characteristics.

Determination of optimal laser cutting parameter settings for obtaining high cut quality in CO2laser cutting process is of great importance. In this paper an attempt has been made to apply different optimization methods for determining of optimal values of laser power, cutting speed, assist gas pressure and focus position with the purpose of improving the cut quality characteristics obtained in the CO2laser cutting of stainless steel. The laser cutting experiment was planned and conducted according to the Taguchi’s L27orthogonal array and the experimental data were used for developing mathematical models for surface roughness, kerf width and width of heat affected zone based on artificial neural networks (ANNs). Mathematical models of the cut quality characteristics were developed using single hidden layer ANN trained with Levenberg–Marquardt algorithm. This paper compares the quality of solutions obtained when optimizing ANN models using the real coded genetic algorithm (RCGA), simulated annealing (SA) and recently developed improved harmony search algorithm (IHSA). The computer code was written in MATLAB to integrate the ANN-based process models and the RCGA, SA and IHSA algorithms. For the purpose of comparison, some performance criteria were used. The merits and the limitations of the selected optimization methods were discussed.

The application of artificial neural networks (ANNs) for modelling laser cutting is broad and ever increasing. The practical application of ANNs is mostly dependent on the success of the training process which is a complex task. Considering the disadvantages of backpropagation (BP) such as the convergence to local minima and slow convergence, this paper aims at investigating the possibilities of using novel meta-heuristic algorithms such as improved harmony search algorithm (IHSA) and cuckoo search algorithm (CSA) for training ANNs in modelling laser cutting. The validity and efficiency of the algorithms were verified by comparing the results with ANN model trained with real coded genetic algorithm (RCGA) which’s superiority over BP has been well-documented. Statistical methods of the correlation coefficient and absolute percentage error indicate that the search space exploration capability of the IHSA and CSA are comparable to RCGA. It was shown that all three algorithms could be efficiently used for training of ANNs in modelling laser cutting.