In this study, the effects of ten input factors (wheel speed, workpiece speed, feed, depth of cut, wheel diameter, wheel width, wheel abrasive material, wheel grain size, wheel hardness and wheel structure) on three response variables (dimensional deviation, surface roughness, and material removal rate) were investigated during cylindrical longitudinal grinding of tool steel. The experimental design was constructed using a custom design of experiments based on the I-optimality criterion, enabling efficient and statistically reliable evaluation of the effects of a large number of input factors. Based on the experimental results, regression analysis was performed and process regression models were developed. All input factors, as well as certain interaction effects, had a significant influence on dimensional deviation and surface roughness. Using the developed models, a multi-objective optimization was carried out to simultaneously improve dimensional accuracy, surface quality, and process productivity. Model validation and verification of the optimal solutions were conducted through additional confirmation experiments. The low confirmation errors obtained indicate the adequacy of the modelling and optimization procedures.

Purpose: To enhance enterprise efficiency, this study examines the pivotal role of operations management in optimizing the use of materials, technology, equipment, and personnel, especially within the transformative framework of Industry 4.0. Design/methodology/approach: This article investigates operational preparation in the context of Industry 4.0. Through questionnaires and interviews with stakeholders, problems related to operational preparation were identified. To address these problems, a launching model was developed for production systems. The model considers several parameters important for the production process, including business goals, customer needs, and environmental conditions that impact enterprise performance and profit. Findings: The model determines the importance of jobs to be performed in a certain order to optimize production sequence. The proposed parameters are included in a mathematical-algorithmic launching model based on categorical levels related to the production process, such as profit, delivery times, processing times, total number of technological operations, product types, materials, required quality, product complexity, and resource use. The model was developed based on observations and investigations conducted in Kosovo's enterprises on operations research, and it has the potential to significantly improve production efficiency and profitability in the Industry 4.0 era. Practical implications: The findings of this study have practical implications for operations management within the Industry 4.0 framework, proposing a launching model designed to optimize production processes and enhance efficiency. Originality/value: The originality and value of this research lie in the development of a mathematical-algorithmic launching model that addresses operational preparation in the Industry 4.0 context, taking into account various crucial parameters.

The article investigates the influence of geometric parameters of CVD cutting inserts on flank and crater wear during dry turning. Turning of AISI 316L stainless steel was performed with different values of approach angles, rake angles, clearance angles, inclination angles and corner radii. After dry turning, the flank and crater wear of the cutting inserts were measured. The effects of the input parameters on the output parameters were evaluated using ANOVA. The results of the experiments showed that the flank and crater wear increases with increasing approach angle, rake angle, clearance angle, inclination angle and decreasing corner radius. Based on the test results, statistical models were developed to predict the distribution of flank and crater wear using the response surface design. The absolute and percentage errors obtained show that the models developed can be successfully used to predict flank and crater wear.

The Lean Smart Maintenance concept has been developed to provide an optimal and targeted maintenance strategy. By implementing this concept, manufacturing companies can achieve an efficient maintenance program for their CNC machines, which is a crucial component in ensuring the continued operation and the overall success of the manufacturing process. Moreover, maintenance resources are minimized as maintenance measures are performed more effectively. Both planned and unplanned shutdowns are considered. In this paper, the focus is on a comprehensive consideration of maintenance in a SAP-based, medium-sized company with an inhomogeneous machine park and the digital networking of all departments. The data-based model follows a centralized approach from the maintenance technician’s mobile device, over the warehouse management of the maintenance to sales information with the use of advanced planning and scheduling software.

The study investigated the turning of C45 steel in a dry environment. The input parameters that were varied were cutting speed, feed, depth of cut, corner radius and insert type. The experimental investigations were carried out according to a custom experimental design using the D-optimality criterion. The measured output parameters were dimensional deviation, flank wear and surface roughness, while the material removal rate was calculated. A detailed analysis and evaluation of the effects of the input parameters on the output parameters was carried out. The model was diagnosed and appropriate regression equations were established. Based on the obtained regression equations, multi-objective optimisation was performed using particle swarm optimisation. The objective function was to simultaneously minimise dimensional deviation, flank wear and surface roughness and maximise material removal rate. The optimisation was carried out for different weighting coefficients of each output function for different production requirements. The obtained models and optimal values were verified by additional confirmation experiments.

In this paper, the results of microstructural analyses, including optical microscopy, scanning electron microscopy with energy dispersive spectroscopy and X-ray diffraction analysis, of the Ni-based self-fluxing alloys NiCrBSi, NiCrBSi–WC, and NiBSi–WC deposited on a previously quenched and tempered (QT) steel substrate 42CrMo4 by flame spraying with simultaneous fusing and plasma transferred arc (PTA) process are presented. The aforementioned microstructural analysis was carried out to determine the microstructural characteristics of the investigated coatings, especially at the coating/substrate interface, and the influences of the spraying and welding technology on the steel substrate. The analysis revealed a change in the microstructure of the coating/substrate interface. Specifically, the diffusion characteristics of certain chemical elements (carbon and iron) from the coating to the substrate and from the substrate to the coating were observed. Additionally, the analysis established the existence of new phases within the coating that arose as a result of the aforementioned diffusion and reaction with chemical elements from the coating. The diffusion of chemical elements was most pronounced in the area of the coating/substrate interface, while it decreased away from this area.

Additive manufacturing (AM), more specifically laser powder bed fusion (LPBF), has become increasingly important for the production of complex components. Despite recent improvements, issues with process parameter optimization, multi-material approaches, CAx chain, adaption for automated mass production, automated process planning, and quality control are still major concerns. So far, despite growing interest, the technology has not yet made the leap into everyday and large-scale use. The use of artificial intelligence offers opportunities to solve many of these problems and improve LPBF technology. In this paper, these topics are addressed to give the reader a holistic overview of the potential for optimization. The individual topics are not only explained and supported with example products from various industries but also evaluated in terms of cost-effectiveness and quality improvement. By evaluating the potentials, restrictions, and recommendations, a framework is created for further investigation and practical application of optimization approaches.

In this study, the modelling of arithmetical mean roughness after turning of C45 steel was performed. Four parameters of cutting tool geometry were varied, i.e.: corner radius r, approach angle κ, rake angle γ and inclination angle λ. After turning, the arithmetical mean roughness Ra was measured. The obtained values of Ra ranged from 0.13 μm to 4.39 μm. The results of the experiments showed that surface roughness improves with increasing corner radius, increasing approach angle, increasing rake angle, and decreasing inclination angle. Based on the experimental results, models were developed to predict the distribution of the arithmetical mean roughness using the response surface method (RSM), Gaussian process regression with two kernel functions, the sequential exponential function (GPR-SE) and Mattern (GPR-Mat), and decision tree regression (DTR). The maximum percentage errors of the developed models were 3.898 %, 1.192 %, 1.364 %, and 0.960 % for DTR, GPR-SE, GPR-Mat, and RSM, respectively. In the worst case, the maximum absolute errors were 0.106 μm, 0.017 μm, 0.019 μm, and 0.011 μm for DTR, GPR-SE, GPR-Mat, and RSM, respectively. The results and the obtained errors show that the developed models can be successfully used for surface roughness prediction.

Surface roughness is an important indicator of the quality of the machined surface. One of the methods that can be applied to improve surface roughness is ball burnishing. Ball burnishing is a finishing process in which a ball is rolled over the workpiece surface. Defining adequate input variables of the ball burnishing process to ensure obtaining the required surface roughness is a typical problem in scientific research. This paper presents the results of experiments to investigate ball burnishing of AISI 4130 alloy steel with a high-stiffness tool and a ceramic ball. The experiments were conducted following a randomized full factorial design for different levels of input variables. The input variables included the initial arithmetic mean roughness (the initial surface roughness), the depth of ball penetration, the burnishing feed, and the burnishing ball diameter, while the output variable was the arithmetic mean roughness after ball burnishing (the final surface roughness). The surface roughness modeling was performed based on the experimental results, using regression analysis (RA), artificial neural network (ANN), and support vector regression (SVR). The regression model displayed large prediction errors at low surface roughness values (below 1 μm), but it proved to be reliable for higher roughness values. The ANN and SVR models have excellently predicted roughness across a range of input variables. Mean percentage error (MPE) during the experimental training research was 29.727%, 0.995%, and 1.592%, and MPE in the confirmation experiments was 34.534%, 1.559%, and 2.164%, for RA, ANN, and SVR, respectively. Based on the obtained MPEs, it can be concluded that the application of ANN and SVR was adequate for modeling the ball burnishing process and prediction of the roughness of the treated surface in terms of the possibility of practical application in real industrial conditions.

The paper presents the results related to the development of a modular didactic laboratory set for the experimental study of friction and its implementation in engineering education. Successfully implemented in laboratory exercises and research, the modular set has been upgraded over time. A newly developed module is presented for investigating the kinetic coefficient of friction based on the differential equation of motion on an inclined plane. The authors deal with the most critical case - a free fall of a shell-shaped pin down the cylindrical guide when, theoretically, the friction force is equal to zero, while the air resistance force has a maximum value. Based on the time data recorded by inductive sensors, measurements were performed to determine the discrepancy between the theoretical and experimental acceleration values. After introducing the calculation model, the authors quantify very small energy "losses" and analyse the relationship between the unknown resistance force, caused primarily by the imperfections of the experiment, and the air resistance force.

This study investigated the AISI 1040 steel turning in dry environment with four cutting inserts of different corner radii coated by CVD method. Experimental investigations were performed for different levels of cutting speeds, feeds and depths of cut using a randomized full factorial design. Quality characteristics of the workpiece machined surface were measured (arithmetical mean roughness) as well as the cutting inserts tool life characteristics (average width of flank wear). Machining times and chip volume were calculated, and based on this, chip quantity in time (material removal rate). The response surface approach and analysis of variance were used to determine the effects of input process parameters on the response variables. Based on the derived regression models, multi-objective optimization of output process parameters was performed using genetic algorithm. The objective function was simultaneous minimization of flank wear, minimization of surface roughness and maximization of material removal rate. The parameters of the genetic algorithm (crossover ratio, crossover fraction, mutation rate, Pareto front population fraction) were varied to obtain the optimal values of the objective function. Additionally, a sensitivity analysis was performed, which showed that the selected values of genetic algorithm parameters gave the best (minimum) value of objective function. Instead of the usual approach of obtaining only one combination of optimal parameters as a final solution, the basic idea was to obtain multiple combinations of optimal input process parameters depending on the importance of each output process parameter, i.e. requirements of production. Accordingly, the results of multi-objective optimization showed that there are a large number of Pareto optimal solutions. To validate the optimal input and output process values, confirmation experiments were conducted for selected trials of Pareto optimal results obtained from multi-objective optimization. A mean error percentage of 1.478% and 1.146% for flank wear and arithmetical mean roughness, respectively, proves that the predicted optimum values are confirmed by experimental results.

Presented in this paper is the theoretical background for a novel method for determination of kinetic friction coefficient. The method is based on the equation of movement of a rigid body along an inclined plane and has not been previously discussed in literature. According to the theoretical results presented in this paper, mean kinetic friction coefficient can be determined based on the time it takes a rigid body to travel a distance along an inclined plane. Experimental results show that the mean kinetic friction coefficient increases with increasing mean sliding velocity, i.e. with decreasing sliding time. Increasing sliding velocity increases deviation of friction coefficients. This method also allows the determination of energy consumed due to friction along the travelled distance. Increasing mean kinetic friction coefficient and mean sliding velocity increases energy consumed due to friction. Practical calculation requires application of the distance law, which, in turn, requires accurate measurement of the sliding time and distance.

Using a "loop-the-loop" system, the authors developed a double-track apparatus and based their experimental research on theoretical foundations related to the motion on an inclined plane laid by Leonhard Euler. The method enabled the quantification of very small "energy losses". The paper presents the results of experimental research and the analysis of the changes in friction force and other causes of energy dissipation. In engineering education, the method contributes to a deep understanding of the concept of energy transformation – the appearance of "energy losses" in real tribomechanical systems.

This paper presents research of the solid particle erosion of conventionally and duplex treated vanadium carbide (VC) coatings produced on the C45E steel substrate by the thermo-reactive diffusion (TRD) technique. The Response Surface Methodology (RSM) based on Central Composite Design (CCD) was used to evaluate the effect of moving speed and impact angle as input variables on the erosive wear mass loss as the response. It was established that moving speed of the samples and the impact angle of the abrasive particles are statistically significant factors, while their interaction does not have significant effect on the wear process. The mathematical model of the investigated wear process was derived as well.

The paper presents an approach to solving the problem of modelling and prediction of surface roughness in CNC turning process. In order to solve this problem an experiment was designed. Samples for experimental part of investigation were of dimensions 30 × 350 mm, and the sample material was GJS 500 - 7. Six cutting inserts were used for the designed experiment as well as variations of cutting speed, feed and depth of cut on CNC lathe DMG Moriseiki-CTX 310 Ecoline. After the conducted experiment, surface roughness of each sample was measured and a data set of 750 instances was formed. For data analysis, the Back-Propagation Neural Network (BPNN) algorithm was used. In modelling different BPNN architectures with characteristic features the results of RMS (Root Mean Square) error were controlled. Specially analysed were the RMS errors realised by different number of neurons in hidden layers. For the BPNN architecture with one hidden layer the architecture (4 – 8 - 1) was adopted with RMS error of 3,37%. In modelling the BPNN architecture with two hidden layers, a considerable amount of architectures was investigated. The adopted architecture with two hidden layers (4 - 2 - 10 - 1) generated the RMS error of 2,26%. The investigation was also directed at the size of the data set and controlling the level of RMS error.

Presented in this paper is a methodology for evaluation of dry turning process along with optimal machining parameters, which allows a compromise between technological, economical, and ecological requirements. Dry longitudinal turning of Ni-based superalloy - Inconel 601 workpiece using three turning inserts coated with TiAlN + AlCr2O3 by physical vapour deposition was investigated. The machining was performed at three different levels of cutting speed, feed, and depth of cut. During machining, power characteristics were measured in the form of current and interphase voltage. Machined parts and turning inserts were tested for surface roughness and tool life, respectively. Machining times, power and energy consumption were also calculated. Regression models were defined for arithmetical mean roughness, flank wear, and power. Negative environmental impact of the cutting process was monitored through midpoint and endpoint life cycle assessment (LCA). Finally, multi-objective optimization of output functions was performed. Optimization problem of minimizing the objective function was solved for eight output functions which characterize the process. The objective functions within this problem are: minimization of surface roughness, minimization of flank wear, minimization of cutting time, minimization of energy consumption, and minimization of four LCA indicators. The results indicate that dry turning of Ni-based superalloy - Inconel 601 requires higher turning inserts radii, higher cutting speeds, and smaller feeds and depths of cut. This allows acceptable flank wear of turning insert and required surface quality of workpiece, while drastically reducing cutting time, energy consumption and negative impact on the environment. The results also indicate that, within the set limitations and conditions of this experiment, increase of cutting speed can be a good strategy for reducing energy consumption and diminishing the impact of machining on the environment. (C) 2020 Elsevier Ltd. All rights reserved.

In investigating thermally sprayed Ni-based self-fluxing alloy coatings, widely applied under conditions of wear, corrosion, and high temperatures, designed experiments and statistical methods as a basis for modeling and optimization have become an important tool in making valid and comparable conclusions. Therefore, this paper gives an overview of investigating Ni-based self-fluxing alloy coatings deposited by thermal spraying by the use of designed experiments and statistical methods. The investigation includes the period of the last two decades and covers the treatments of flame spraying, high-velocity oxy/air fuel spraying, plasma spraying, plasma-transferred arc welding, and laser cladding. The main aim was to separate input variables, as well as measured responses, and to point out the importance of correct application of statistical design of experiment. After the review of the papers, it was concluded that investigators have used the process knowledge to analyze and interpret the results of the statistical analysis of experimental data, which is in fact the best way of using the design of experiment in every research. Nevertheless, more attention should be given to correct planning and conducting the experiments to derive the models suitable for the prediction of measured response and which could be an appropriate input for single- or multi-objective optimization.