Stoyan Dimitrov Slavov

The present research investigates the optimization of ball burnishing (BB) process parameters to create regular lubricating grooves on multilayer connecting rod liners to prevent engine seizure. The study utilized a Taguchi L9 fractional orthogonal array to evaluate the impact of ball diameter, deforming force, and feed rate on the resulting groove widths. Statistical analysis (ANOVA) revealed that ball diameter is the primary driver of groove width variation, exhibiting a non-linear parabolic relationship where the diameter serves as a stabilizing threshold. While deformation force showed a steady linear progression in widening traces, higher feed rates were found to restrict localized plastic flow, resulting in narrower groove widths. For the bimetallic structure (steel back with AlSn20Cu coating), the research recommends tailoring forces to the specific layer—forces for the anti-friction layer and for the substrate to avoid structural destruction. Profilometry confirmed that the height of edge inflows directly correlates with groove depth, ranging from 6 to 30 μm. The optimized non-linear regression model developed in this study achieved an exceptionally high coefficient of determination (R² = 99.84%), ensuring precise predictive accuracy. Overall, these findings provide a robust framework for researchers to enhance the durability of heavy-duty engine components through controlled surface topography.

This paper presents a novel approach using machine learning methods for the automated segmentation of acceleration signals measured during ball burnishing (BB) operations performed on a computer numerical controlled (CNC) milling machine. The study addresses the challenge of accurately finding actual feedrates in that finishing operation, which often deviate from programmed values due to various dynamic reasons. The method involves a two-stage process: first, an automatic signal segmentation algorithm employing Gaussian Mixture Modeling (GMM) and K-means clustering is applied to the ball burnishing (BB) process and acceleration data. Second, a Taguchi L9 experimental design is used to assess the influence of some regime parameters on the actual feedrate and the BB’s cycle duration. Results show successful segmentation of the toolpaths based on X-axis accelerations and deforming force data, with the Calinski–Harabasz Index confirming good cluster separability. Programmed feedrate and the number of toolpath points were identified as the most significant factors affecting the percentage delay between programmed and obtained feedrates. The main contribution is the development and testing of a new method for segmenting different toolpath states in ball burnishing operations, based on measured accelerations and momentary deforming force magnitudes. The present work offers valuable insights into autonomous monitoring and control in BB operations.

The present research is related to the application of artificial intelligence (AI) approaches for classifying surface textures, specifically regular reliefs patterns formed by ball burnishing operations. A two-stage methodology is employed, starting with the creation of regular reliefs (RRs) on test parts by ball burnishing, followed by 3D topography scanning with Alicona device and data preprocessing with Gwyddion, and Blender software, where the acquired 3D topographies are converted into a set of 2D images, using various virtual camera movements and lighting to simulate the symmetrical fluctuations around the tool-path of the real camera. Four pre-trained convolutional neural networks (DenseNet121, EfficientNetB0, MobileNetV2, and VGG16) are used as a base for transfer learning and tested for their generalization performance on different combinations of synthetic and real image datasets. The models were evaluated by using confusion matrices and four additional metrics. The results show that the pretrained VGG16 model generalizes the best regular reliefs textures (96%), in comparison with the other models, if it is subjected to transfer learning via feature extraction, using mixed dataset, which consist of 34,037 images in following proportions: non-textured synthetic (87%), textured synthetic (8%), and real captured (5%) images of such a regular relief.

In the present study, an approach for determining the different states of ball burnishing (BB) operations aimed at forming regular reliefs’ patterns on planar surfaces is introduced. The methodology involves acquiring multi-axis accelerometer data from CNC-driven milling machine to capture the dynamics of the BB tool and workpiece, mounted on the machine table. Following data acquisition from an AISI 304 stainless steel workpiece, which is subjected to BB treatments at different toolpaths and feed rates, the recorded signals are preprocessed through noise reduction techniques, DC component removal, and outlier correction. The refined data are then transformed using a root mean square (RMS) operation to simplify further analysis. A Gaussian Mixture Model (GMM) is subsequently employed to decompose the compressed RMS signal into distinct components corresponding to various operational states during BB. The experimental trials at feed rates of 500 and 1000 mm/min reveal that increased feed rates enhance the distinguishability of these states, thus leading to an augmented number of statistically significant components. The results obtained from the proposed GMM based algorithm applied on compressed RMS accelerations signals is compared with two other methods, i.e., Short-Time Fourier Transforms and Continuous Wavelet Transform. The results from the comparison show that the proposed GMM method has the advantage of segmenting three to five different states of the BB-process from nonstationary accelerations signals measured, while the other tested methods are capable only to distinguish the state of work of the deforming tool and state of its rapid (re-)positioning between the areas of working, when there is no contact between the BB-tool and workpiece.

The influence of regular relief formation modes on the microhardness of the formed groove surface near the apex at the bottom of the groove has been studied. It has been established that the rate of plastic deformation, expressed as the feed rate of the deforming element, has a significant impact on the plastic deformation mechanism, and the microstructure of the formed subsurface layer, as well as on the microhardness of the groove surface. The influence of the type of partially regular reliefs on the degree of plastic deformation was also investigated. It was found that the third type of partially regular relief, which has the highest groove density, provides higher microhardness values than the first and second types. This is explained by the significantly greater density of these type of partially regular relief grooves, which exert a mutual strengthening effect on the surface during formation. The experimental study conducted enabled the derivation of regression equations describing the influence of the feed rate of the deforming element and the type of partially regular relief created on the surface microhardness beneath the lateral ridges and the bottoms of the plastically deformed traces.

This study explores the fatigue properties of EN AW-6082-T6 aluminum alloy in the gigacycle range (10⁶–10⁹ cycles), using ultrasonic resonance fatigue testing at 20 kHz in a push–pull mode with a symmetric load cycle (R = −1). A custom-built ultrasonic fatigue machine, developed at TU-Varna, comprising a generator, ultrasonic train (including a high-power transducer, booster, custom-made sonotrode, and specimen), monitoring, data logging systems, and an air-cooling capability, was used for the experiments conducted. A Bezier curve sonotrode, with an amplification ratio of 1:6, was designed and produced for the test. Hourglass-shaped specimens were designed on the base of the dynamic Young’s modulus E = 71.3 GPa, determined through the impulse resonance method (ASTM E1876-01), and validated with FEM analysis for resonance length and stress amplitude. The fatigue testing revealed a fatigue strength reduction of approximately 60 MPa between 10⁶ and 10⁹ cycles. The percentile of failure curves based on a Cactillo–Canteli model fits well with the experimental data and gives a fatigue limit at 10⁹ cycles σl = 104 MPa and “endurance strength” σw = 84 MPa. Surface crack initiation was consistently observed with predominately cleavage transgranular fractures in the fatigue zone. The present research highlights the utility of ultrasonic testing for examining fatigue behavior in the gigacycle regime.

Advanced in the present paper is an innovative approach for three-dimensional modeling of the regular relief topography formed via a ball burnishing process. The proposed methodology involves capturing a greyscale image of and profile measuring the surface topography in two perpendicular directions using a stylus method. A specially developed algorithm further identifies the best match between the measured profile segment and a row or column from the captured topography image by carrying out a signal correlation assessment based on an appropriate similarity metric. To ensure accurate scaling, the image pixel grey levels are scaled with a factor calculated as being the larger ratio between the ultimate heights of the measured profilograms and the more perfectly matched image row/column. Nine different similarity metrics were tested to determine the best performing model. The developed approach was evaluated for eight distinct types of fully and partially regular reliefs, and the results reveal that the best-scaled 3D topography models are produced for the fully regular reliefs with much greater heights. Following a thorough analysis of the results obtained, at the end of the paper, we draw some conclusions and discuss potential future work.

In the present work, functional properties of three different types of regular microreliefs, formed by ball burnishing process on flat surfaces are evaluated. For their estimation, heights and material ratio curve’s criteria were used, according to the international standard ISO 21920-2:2021. The influence of the regular relief’s type and ball burnishing process parameters on the surface functional properties were investigated using full factors experimental design. Based on the obtained results, statistical models were derived that describe the dependencies between the topography characteristics and the regular reliefs’ types and regime parameters of the ball burnishing process. Based on the obtained results, several conclusions have been formulated about the applicability of these standardized methods and criteria for evaluating the functional characteristics of the different types of regular reliefs at the end of the work.

The present work focusses on the research of the plastic deformation accumulated effect obtained after two different plastic deformation treatments, over the fatigue life of AISI 304 austenitic stainless steel. The research is focused on ball burnishing as a finishing process to form specific, so-called “regular micro-reliefs” (RMRs) on a pre-rolled stainless-steel sheet. RMRs are formed using a CNC (Computerized Numerically Controlled) milling machine and toolpaths with the shortest unfolded length, generated by an improved algorithm, based on the Euclidean Distance calculation. The effect of the predominant tool trajectory direction during the ball burnishing process (which can be coinciding or transverse with the rolling direction), the magnitude of applied deforming force, and feed-rate is subjected to evaluation using Bayesian rule analyses of experimentally obtained results for the fatigue life of AISI 304 steel. The obtained results give us reason to conclude that the fatigue life of researched steel is increased when directions of pre-rolled plastic deformation and the tool movement during ball burnishing are coincident. It also been found that the magnitude of deforming force has a stronger impact over the fatigue life, than the feed-rate of the ball tool.

The correlation between the service characteristics of the working surfaces of car parts belonging to the rotary body class, and quality parameters—in particular, the height-related roughness parameter Ra—was estimated. Low values of R_a were found to be unable to guarantee an optimal microrelief geometry and, accordingly, high-performance characteristics of the working surface. The oil-accumulation power of the parts was investigated as a primary characteristic of sliding friction using the group of Rk parameters in the Abbott–Firestone diagram, based on the profilogram of the test specimen’s surfaces. The oil-absorption power of the surfaces formed by different technological operations was compared with different microgeometric quality parameter values.