Properties of the Surface Layer After Trochoidal Milling and Brushing: Experimental Study and Artificial Neural Network Simulation

Selected Properties of the Surface Layer of C45 Steel Samples after Slide Burnishing

This paper presents the experimental results of a study investigating the impact of the machining fluid type, the variable factor, used in slide burnishing on 2D and 3D surface roughness; surface topography; Abbott-Firestone curve shape; microhardness; and SFE (surface free energy). In the experiment, pre-ground, ringed samples of C45 steel were used. The results showed an over eight-fold decrease in the value of the Ra (arithmetical mean deviation) parameter and over a five-fold decrease in the Rt (total height of profile) parameter in relation to their values after grinding. The parameters Rpk (reduced peak height), Rk (core roughness depth), and Rvk (reduced valley depth) were also reduced. The Abbott-Firestone curve after slide burnishing changed its angle of inclination (it was more flattened), and the material ratio Smr increased. The reduction in the Rpk and Rk parameters and increased material ratio will most likely contribute to restoring the functionality of these surfaces (increased resistance to abrasive wear). After slide burnishing, the maximum 25% increase in microhardness was obtained compared to the value after grinding, while the layer thickness was 20 μm. The surface energy of elements subjected to slide burnishing using various machining fluids slightly increased, or its value was close to that of the ground surface. The most favourable properties of the surface layer in terms of mating between two elements were obtained for a part that was slide-burnished with a mixture of oil + polymethyl methacrylate (PMM) + molybdenum disulphide (MoS2).

Numerical Investigation of the Formation of a Failure Cone during the Pullout of an Undercutting Anchor

Previously published articles on anchors have mainly focused on determining the pullout force of the anchor (depending on the strength parameters of the concrete), the geometric parameters of the anchor head, and the effective anchor depth. The extent (volume) of the so-called failure cone has often addressed as a secondary matter, serving only to approximate the size of the zone of potential failure of the medium in which the anchor is installed. For the authors of these presented research results, from the perspective of evaluating the proposed stripping technology, an important aspect was the determination of the extent and volume of the stripping, as well as the determination of why the defragmentation of the cone of failure favors the removal of the stripping products. Therefore, it is reasonable to conduct research on the proposed topic. Thus far, the authors have shown that the ratio of the radius of the base of the destruction cone to the anchorage depth is significantly larger than in concrete (~1.5) and ranges from 3.9-4.2. The purpose of the presented research was to determine the influence of rock strength parameters on the mechanism of failure cone formation, including, in particular, the potential for defragmentation. The analysis was conducted with the finite element method (FEM) using the ABAQUS program. The scope of the analysis included two categories of rocks, i.e., those with low compressive strength (<100 MPa) and strong rocks (>100 MPa). Due to the limitations of the proposed stripping method, the analysis was conducted for an effective anchoring depth limited to 100 mm. It was shown that for anchorage depths <100 mm, for rocks with high compressive strength (above 100 MPa), there is a tendency to spontaneously generate radial cracks, leading to the fragmentation of the failure zone. The results of the numerical analysis were verified by field tests, yielding convergent results regarding the course of the de-fragmentation mechanism. In conclusion, it was found that in the case of gray sandstones, with strengths of 50-100 MPa, the uniform type of detachment (compact cone of detachment) dominates, but with a much larger radius of the base (a greater extent of detachment on the free surface).

Experimental Study and Artificial Neural Network Simulation of Cutting Forces and Delamination Analysis in GFRP Drilling

This paper reports the results of measurements of cutting forces and delamination in drilling of Glass-Fiber-Reinforced Polymer (GFRP) composites. Four different types of GFRP composites were tested, made by a different manufacturing method and had a different fiber type, weight fraction (wf) ratio, number of layers, but the same stacking sequence. GFRP samples were made using two technologies: a novel method based on the use of a specially designed pressing device and hand lay-up and vacuum bag technology process. The study was conducted with variable technological parameters: cutting speed v_c and feed per tooth f_z. The two-edge carbide diamond-coated drill produced by Seco Company was used in the experiments. Cutting-force components and delamination factor were measured in the experiments, and photos of the holes were taken to determine the delamination. In addition, modeling of cause-and-effect relationships between the technological drilling parameters v_c and f_z was simulated with the use of artificial neural network modeling. For all tested GFRP materials, an increase in f_z led to an increase in the amplitude of cutting-force component F_z. The lowest values of the amplitude of cutting-force component F_z were obtained with the lowest tested feed per tooth value of 0.04 mm/tooth for all tested materials. It was observed that materials produced with the use of the specially designed pressing device were characterized by lower values of the cutting-force component F_z. It was also found that the delamination factor increased with an increase in f_z for all tested GFRP materials. A comparison of the lowest and the highest values of f_z revealed that the lowest delamination factor increase was archived by the B1 material and amounted to about 12.5%. The error margin of the obtained numerical modeling results does not exceed 15%, so it can be concluded that artificial neural networks are a suitable tool for modeling cutting force amplitudes as a function of v_c and f_z. The study has shown that the use of the special pressing device during the manufacturing of composite materials has a positive effect on delamination.

Influence of Slide Burnishing Parameters on the Surface Layer Properties of Stainless Steel and Mean Positron Lifetime

This paper presents the results of an experimental study on the impact of slide burnishing on surface roughness parameters (Sa, Sz, Sp, Sv, Ssk, and Sku), topography, surface layer microhardness, residual stress, and mean positron lifetime (τ_mean). In the study, specimens of X6CrNiTi18 stainless steel were subjected to slide burnishing. The experimental variables were feed and slide burnishing force. The slide burnishing process led to changes in the surface structure and residual stress distribution and increased the surface layer microhardness. After slide burnishing, the analyzed roughness parameters decreased compared with their pre-treatment (grinding) values. The slide burnishing of X6CrNiTi18 steel specimens increased their degree of strengthening e from 8.77% to 42.74%, while the hardened layer thickness g_h increased after the treatment from about 10 µm to 100 µm. The maximum compressive residual stress was about 450 MPa, and the maximum depth of compressive residual stresses was g_σ = 1.1 mm. The positron mean lifetime τ_mean slightly yet systematically increased with the increase in burnishing force F, while an increase in feed led to changes of a different nature.

Analysis of the Effectiveness of Removing Surface Defects by Brushing

The paper presents the results of a study on the effectiveness of removing surface defects by brushing. Damage to machine components usually begins on their surface or in the surface layer area. This determines the development of methods, conditions, and process parameters that will positively affect the stereometric and physical properties of the surface layer. Experiments were conducted in which surface defects were generated on a specially designed test stand. By controlling the load and speed of the defect generator it was possible to affect the geometry, depth, and width of the surface defect. A FEM simulation of the brushing treatment was carried out in order to determine the effect of fibers passing through a surface defect in the form of a groove with a small depth and width. It was shown that for certain conditions of brushing treatment, surface defects could be removed effectively. Moreover, the microhardness of the surface layer after the brushing process was analyzed. Changes in microhardness due to brushing reached up to 50 μm for EN AW-2024 aluminum alloy and up to 150 μm for AZ91HP magnesium alloy. The results demonstrated that brushing was an effective method for strengthening the surface layer and that the value of strengthening in the area of defects depended on the effectiveness of their removal.

Lapping Quality Prediction of Ceramic Fiber Brush Based on Gaussian-Restricted Boltzmann Machine

Although ceramic fiber brushes have been widely used for deburring and surface finishing, the associated relationship between process parameters and lapping quality is still unclear. In order to optimize the lapping process of ceramic fiber brushes, this paper proposes a multi-layer neural network based on the Gaussian-restricted Boltzmann machine (GRBM), and verified its prediction effectiveness. Compared with a traditional back-propagation neural network, its prediction error was reduced from 7.6% to 4.5%, and the determination coefficient was increased from 0.96 to 0.98, respectively. The comparison results showed that the proposed model can better grasp the relationship between process parameters and machining quality, which can be used as a decision-making foundation for lapping-process optimization.

Study on the Surface Layer Properties and Fatigue Life of a Workpiece Machined by Centrifugal Shot Peening and Burnishing

This paper presents the results of research on the impact of finishing method on surface topography, surface roughness (parameters Ra, Rt, Rpk, Rk, Rvk), surface layer microhardness, residual stresses and fatigue life. Ring samples made of C45 steel were used to conduct the experiments. The following finishing machining methods were selected: slide burnishing, ball burnishing, centrifugal shot peening, centrifugal shot peening + slide burnishing and centrifugal shot peening + ball burnishing. In the first stage, the use of combined shot peening + burnishing enables microhardness to be increased on the surface layer, the values of residual stresses to be increased and the creation of characteristic machining traces on the surface, the so-called "dimples" (effect of centrifugal shot peening). On the other hand, burnishing (slide burnishing or ball burnishing) is aimed at smoothing the surface and providing favorable stereometric properties to the surface layer. It was noted that, after finishing, the surface roughness parameters decreased from 59% to 83% in relation to the reference surface. The exception is the centrifugal shot peening technology. The use of burnishing (slide or ball burnishing) after centrifugal shot peening reduces the surface roughness parameters by a maximum of 82% compared to the value after centrifugal shot peening. The highest increase in microhardness was obtained after centrifugal shot peening + slide burnishing (ΔHV = 105 HV 0.05), while the highest thickness g_h (g_h = 120 μm) was obtained after centrifugal shot peening + ball burnishing. The combination of centrifugal shot peening and ball burnishing results in the highest absolute value of compressive residual stresses σ_max = 602 MPa and depth g_σ = 0.41 mm). Application of an additional operation after centrifugal shot peening increases fatigue life from 27% to 49%. ANOVA analysis of variance confirms the significance of the processing effect of centrifugal shot peening combined with slide burnishing (CSP + SB) and centrifugal shot peening + ball burnishing (CSP + BB) on the analyzed dependent surface.

Research on Practical Intelligent Mode of Digital Image Economy Based on Improved Genetic Multilayer Neural Network

In the context of economic globalization and digitization, the current financial field is in an unprecedented complex situation. The methods and means to deal with this complexity are developing towards image intelligence. This paper takes financial prediction as the starting point, selects the artificial neural network in the intelligent algorithm and optimizes the algorithm, forecasts through the improved multilayer neural network, and compares it with the traditional neural network. Through comparison, it is found that the prediction success rate of the improved genetic multilayer neural network increases with the increase of the dimension of the input image data. This shows that, by adding more technical indicators as the input of the combined network, the prediction efficiency of the improved genetic multilayer neural network can be further improved and the advantage of computing speed can be maintained.