Rolling Method Research Articles

Mechanical properties of three typical aluminum alloy strips prepared by twin-roll casting

Twin-roll casting (TRC) is a near-net shape processing technology that has found widespread application across various series of Al alloys. In this study, the process-structure-property relationships of 2099, 3003, and 6082 Al alloy strips prepared by TRC and traditional rolling (TR) methods were investigated. It shows that the tensile strength and fatigue strength of 2099-TRC are lower than 2099-TR, which is attributed to the reduced solid solubility of copper (Cu) elements resulting from the precipitation of Cu-rich micron phases and the presence of microstructure inhomogeneities. While both 3003-TRC and 3003-TR materials exhibit similar yield and tensile strength, the occurrence of casting micro-void defects in 3003-TRC leads to an accelerated accumulation of fatigue damage, consequently diminishing its fatigue strength. Conversely, 6082-TRC demonstrates an enhanced fatigue strength relative to 6082-TR. This improvement is primarily linked to the higher yield strength and comparable fatigue resistance observed in the former. In light of these findings, the TRC process holds promise for enhancing the fatigue performance of 6xxx Al alloy strips with good elongation. However, for 2xxx and 3xxx alloy strips with comparatively lower elongation, the TRC process enhancements may be necessary to realize similar benefits. The insights gleaned from this study could offer valuable reference and guidance for optimizing both the TRC process itself and the fatigue performance of Al alloy strips.

A novel method for online prediction of the remaining useful life of rolling bearings based on wavelet power spectrogram and Transformer structure

The vibration signal characteristics of rolling bearings are closely related to the performance decay process, predicting the remaining useful life (RUL) of rolling bearings by vibration signals can effectively prevent the occurrence of bearing failures. In this paper, a deep learning-based method for rolling bearing RUL prediction is proposed. The convolutional neural network (CNN), which is more effective in extracting local information, is combined with the Transformer structure, which is specialized in extracting global information, to deeply explore the complex mapping relationship between signal features in wavelet power spectrogram and bearing RUL. Meanwhile, the method of detecting the first prediction time of rolling bearings based on 3 criteria is improved. The proposed method is validated with the XJTU-SY rolling element bearing accelerated life test datasets, as well as compared with other methods to prove its superiority. The results show that the proposed method can effectively extract bearing degradation information and realize the accurate prediction of rolling bearing RUL. The performance-improved rolling bearing RUL prediction model is highly robust and generalizable, which applies to other mechanical parts performance prediction and can be realized for practical applications in industrial fields.

Fault anomaly detection method of aero-engine rolling bearing based on distillation learning

In this study, we address the issue of limited generalization capabilities in intelligent diagnosis models caused by the lack of high-quality fault data samples for aero-engine rolling bearings. We provide a fault anomaly detection technique based on distillation learning to address this issue. Two Vision Transformer (ViT) models are specifically used in the distillation learning process, one of which serves as the teacher network and the other as the student network. By using a small-scale student network model, the computational efficiency of the model is increased without sacrificing model accuracy. For feature-centered representation, new loss and anomaly score functions are created, and an enhanced Transformer encoder with the residual block is proposed. Then, a rolling bearing dynamics simulation method is used to obtain rich fault sample data, and the pre-training of the teacher network is completed. For anomaly detection, the training of the student network is completed based on the proposed loss function and the pre-trained teacher network, using only the vibration acceleration samples obtained from the normal state. Finally, the trained completed network and the designed anomaly score function are used to achieve the anomaly detection of rolling bearing faults. The experimental validation was carried out on two sets of test data and one set of real vibration data of a whole aero-engine, and the detection accuracy reached 100 %. The results show that the proposed method has a high capability of rolling bearing fault anomaly detection.

Effectiveness of instrument assisted soft tissue mobilization versus foam rolling on trigger point release in calf muscles

BackgroundInstrument assisted soft tissue mobilization and foam rolling are two techniques that have been proven effective in treating Myofascial Trigger Points, irrespective of the type of trigger point. However, little is known about the comparative effectiveness of Instrument assisted soft tissue mobilization and foam rolling. This study proposed to evaluate the effectiveness of either technique on plantar flexors trigger points, ankle dorsiflexion, and lower limb power present in the calf muscles in non-symptomatic patients. MethodForty-two subjects with bilateral calf muscle tightness, at least one trigger point in the calf muscle, and fulfilling the inclusion criteria were randomly assigned to either of the groups. Group A was treated for gastrocnemius and soleus trigger points using Instrument assisted soft tissue mobilization and Group B was treated using the Foam Rolling method. Treatment was given every alternate day, a total of 3 sessions. Subjects were evaluated on 1st and 3rd sessions for pre-post differences of ankle dorsiflexion Range of motion in weight bearing and non-weight bearing position, pressure pain threshold for gastrocnemius trigger point 1(G1), 2(G2), and soleus point 1(S1) on both sides, and lower limb power. ResultWithin group analyses, both groups had shown statistically significant results for all parameters except gastrocnemius trigger point 2 of foam rolling. For between group comparison foam rolling had a statistically significant result in non-weight bearing ankle dorsiflexion range of motion. ConclusionBoth Instrument assisted soft tissue mobilization and Foam rolling were equally effective for treating calf trigger points. But foam rolling was more effective in improving ankle dorsiflexion range of motion.

PRODUCTION AND CHARACTERIZATION OF 2024 ALUMINUM ALLOYS

This study aims to give theoretical information about the production and characterization of mechanical properties of 2024 aluminum alloys. AA2024 alloy; It consists of components such as aluminum, copper, magnesium and manganese, and the production of the alloy is usually carried out by different methods such as casting, rolling, extrusion and powder metallurgy. The choice between production methods should be made by considering the purpose and properties of the alloy. While the casting method is used for the production of large-sized parts, the rolling method is suitable for the production of thin sheets and strips, and the extrusion method is suitable for the production of profiles and pipes of various shapes. The powder metallurgy method, on the other hand, is carried out by shaping the powdered components of the alloy with a special pressing process and then sintering at high temperature. Alloys produced by this method exhibit high strength properties with their high density and homogeneous microstructures. The mechanical properties of AA2024 alloy are generally determined using various characterization methods such as tensile test, hardness measurement and fatigue life tests. The tensile test is used to determine the tensile strength, tensile elongation ratio and elastic tension properties of the alloy. The hardness measurement is used to determine the surface hardness of the alloy. Surface hardness can be performed using Brinell, Vickers and Rockwell hardness measurement methods. The fatigue life test is used to determine the fatigue strength of the alloy. In this test, the fatigue life of the specimen under repetitive loads is determined. The mechanical properties of AA2024 alloy may vary depending on production methods, alloy components, temperature and environmental conditions. Therefore, the mechanical properties of the alloy must be optimized for a particular application. 2024 aluminum alloys produced by powder metallurgy method are expected to exhibit high density, homogeneous microstructure and high strength properties as a result of hardness, tensile and fatigue tests.

Heterogeneous distribution of isothermal ω precipitates prevents brittle fracture in aged β-Ti alloys

Isothermal ω (ωiso) precipitates in β Ti-alloys can increase the yield strength of the material, but unfortunately at a dramatic cost of the ductility. The embrittlement is mainly attributed to the suppression of twinning-induced plasticity (TWIP) and transformation-induced plasticity (TRIP) effects, as well as the formation of dislocation channels associated with localized deformation. To address this issue, we propose a heterogenous microstructure in Ti-15Mo-3Al alloy using conventional rolling method, in which the heterogeneous distributions of both Mo element and ωisoprecipitates are realized after aging. Both the nano-hardness and elastic modulus of ωisorich (Mo-lean) regions are larger than that of ωisodepleted (Mo-rich) regions. A four-time increase in tensile ductility is achieved in the heterogeneous microstructure without a loss of tensile strength, compared with the homogeneous counterpart. The ductility improvement can be ascribed to the blocking effect of ωisodepleted regions against the propagation of dislocation channels which later evolve into micro-cracks.

Prediction Model for Tourism Object Ticket Determination in Bangkalan, Madura, Indonesia

One of the regencies in Madura, namely Bangkalan, with its local wisdom and beautiful landscapes has the potential to become a tourism center. However, there may be a decrease in the number of visits caused by some factors. The research used the time series method to build a prediction model for tourist attraction entrance tickets. The model development aimed to estimate the number of tourist attraction visits in the future. The right model was needed to get the best prediction results. Least square, Holt-Winter, Seasonal Autoregressive Integrated Moving Average (SARIMA), and Rolling were chosen as the models. Data collection related to the number of tourist objects was carried out directly at the Tourism Office to obtain valid data. Using data on visitors to tourist attractions in Bangkalan Regency from 2015 to 2019, the results of measuring errors using Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) are obtained. The error measurement results show that the Holt-Winter model has the lowest error rate of 5% and RMSE of 307,1198. Based on these calculations, the Holt-Winter model is the best model for determining tourist attraction entrance tickets. The ranking of the error measurement results from the highest to the lowest are Holt-Winter, Rolling, SARIMA, and Least Square methods.

Effect of Heat Treatment on Interface Behavior and Fatigue Property of 316L/2Cr13 Multilayered Steel

In this study, multilayered stainless steel (MLS) composed of the austenitic stainless steel 316 L and the martensitic stainless steel 2Cr13 is prepared by the cumulative rolling method. Specifically, changes in interface and fatigue properties before and after the heat treatment are verified by scanning electron microscope, tensile test, and fatigue test. In the results, it is revealed that common grains appear at the interface after the heat treatment. Although the overall mechanical properties are improved, the interfacial bonding strength is increased and the plasticity is greatly improved after heat treatment, the fatigue properties are greatly decreased simultaneously. The main cause is that during the rolling process, large deformation leads to grain fragmentation as well as the generation of a large number of subgrains, resulting in higher dislocation density and increased deformation resistance. Therefore, the fatigue performance of MLS in as‐rolled state is better than that of MLS in the heat‐treatment state. In addition, fatigue fracture analysis indicates that cracks usually initiate at the 2Cr13 layer or at the interface between the 316 L layer and the 2Cr13 layer. Ultimately, in this study, the quadratic relationship between tensile strength and fatigue limit is analyzed through fatigue ratio to predict fatigue limit.

Effect of Core Density on the Three-Point Bending Performance of Aluminum Foam Sandwich Panels.

Using the powder-metallurgy rolling method, aluminum foam sandwich (AFS) panels with a metallurgical bond between the foam core and the panel can be produced. In this study, by manipulating the foaming temperature and duration, AFS panels were fabricated with varying core densities and thicknesses, all maintaining a panel thickness close to 1 mm. Through the three-point bending test, this research deeply delved into how core density influences the mechanical behaviors of these AFS panels. It became evident that a rise in core density positively affects the bending strength and failure load of the panels but inversely impacts their total energy absorption efficiency. Differing core densities brought about distinct failure patterns: low-density samples primarily showed panel indentation and core shear failures, whereas those of high density demonstrated panel yield and fractures. Furthermore, the research offers predictions on the initial failure loads for different failure modes and introduces a comprehensively designed failure diagram, laying a foundational theory for the production of AFS panels.

Optimizing strength and fatigue crack propagation resistance of in-situ TiB2/Al-Cu-Mg composite sheet

The industrial-grade TiB2/Al-Cu-Mg composite sheet was fabricated via casting and rolling methods. Cold-rolling coupled with low-temperature aging treatment was used to regulate the grain size, dislocation density, precipitates and TiB2 particle distribution, to optimize the strength and fatigue crack propagation (FCP) resistance of the sheet. The results show that pre-rolling deformation combined with low-temperature long-term aging is beneficial for obtaining fine grains, high-density dislocations, high-density nano precipitates and uniform particle distribution, all of which are conducive to improving strength. The TiB2/Al-Cu-Mg composite sheet can achieve remarkable ultimate tensile strength over 600 MPa regardless of its excellent elongation (12 %). Furthermore, uniformly distributed TiB2 particles and high-density nano precipitates are beneficial for offsetting the negative effect of high-density dislocations on FCP. Finally, the small pre-rolling deformation (5 %) combined with low-temperature long-term aging achieves excellent strength and FCP resistance combination.

Quality support of conical pipe threads rolled on pipes with control of kinematic technological links

The formation of conical threads on oilfield pipes by the plastic deformation method is studied. A tool and a method for rolling of conical threads is developed to ensure high performance of oilfield pipes. Keywords: pipe, conical thread, rolling, head, kinematic link. nariman.rasulov@aztu.edu.az, ugurlu.nadirov@aztu.edu.az

A life prediction method of rolling bearing based on signal reconstruction and fusion dual channel network

In addressing the problem of low prediction accuracy in the remaining useful life (RUL) prediction of rolling bearings, caused by noise interference and insufficient extraction of sensitive features by deep learning models, this paper presents a life prediction method based on signal reconstruction and dual-channel network fusion. First, addressing the issue of extracting weak features from rolling bearing vibration signals, an optimized combination of variational mode decomposition (VMD) and Teager–Kaiser energy operator (TKEO) for signal reconstruction is proposed. TKEO is used to track the energy of high-frequency impulses in the original signal. The sparrow search algorithm is employed for optimizing VMD to perform high-frequency filtering, resulting in an optimized impulse energy signal. This signal is then multiplied with the original signal to enhance weak impulse features under noisy background. Next, a dual-channel network model for RUL prediction is constructed based on temporal convolutional network (TCN) and convolutional neural networks (CNNs). The one-dimensional time-series signal obtained after reconstruction and denoising serves as the input for the TCN network channel. Simultaneously, the signal obtained after reconstruction and denoising undergoes synchrosqueezed wavelet transforms to generate a two-dimensional time–frequency representation as input for the CNN network channel. This setup allows TCN and CNN to respectively extract temporal features from the vibration signal and time–frequency features from the spectrogram. By incorporating self-attention mechanisms, internal correlations between different features are fully explored, thereby enhancing prediction accuracy.

To the question of survival of the fungi Colletotrichum lupini var. lupini – the lupin anthracnose pathogen – in the conditions of the Bryansk region

The results of studying the viability of the fungus Colletotrichum lupini var. lupini, which is the causative agent of a dangerous disease of lupine – anthracnose, are presented. The purpose of the study was to determine the viability of the fungus under different terms and conditions of storage of lupin seeds and plant residues in climatic conditions of the Bryansk region. The work was done in 2018–2021. The object of research were the seeds, seedlings and plant residues of the Michurinsky variety white lupine and the Vityaz variety narrow-leafed lupine stored under different conditions for four years. Seed anthracnose infestation before and after storage was determined by paper roll method. The fungus was identified by conidia type using a light microscope. The viability of the fungus on plant residues (bean leaflets, stems) was determined by isolation into pure cultures on nutrient medium. It was found that infected seeds were the main source of anthracnose infection. In the conditions of a granary with a natural regime of temperature and humidity for the first year of storage the infection of white lupine seeds with anthracnose decreased from 8.1 to 4.0%, and narrow-leafed lupine – from 4.8 to 1.7%. After three years of storage, the number of the seeds with viable infection of the fungus was 1.1 and 0.4%, respectively. Consequently, lupin seeds from previous years pose less risk as a source of infection. The viability of the fungus decreased on the infected crop residues under room conditions and under the roof of the granary from the first year of storage. By spring 2021, it was 12.0 and 19.0%, respectively. The viability of the fungus decreased faster on unplowed crop residues. It was 2.0% in the spring of 2020 and leveled off completely by the fall. Decomposition of plant residues in the soil is faster than on the surface, and after overwintering, the fungus on them completely loses its viability and cannot be the source of infection.

DIFFERENCES IN HYGIENE INDEX OF BRUSHING TEETH WITH HORIZONTAL, ROLL AND FONE'S METHOD IN CHILDREN AGED 9-12 YEARS

Brushing your teeth is one way to clean your teeth and take preventive measures in improving dental and oral hygiene. The horizontal technique is a technique that is widely used when brushing teeth. The roll technique is often recommended because it is simple and efficient, while the fone's technique is a proven technique suitable for children because it is easy to do. The purpose of the study was to analyze the hygiene index of brushing teeth with horizontal method, roll and fone's method in children aged 9-12 years. This type of research is an quasi-experimental research with pre-test and post-test designs. There search was carried out by treating respondents and checking the n hygiene index before and after brushing teeth with the horizontal method and the fone's method. The study sample amounted to 45 people with three treatments. The data were analyzed by t-test. The results of the study with paired t-test ? value (0.00<0.05) mean that there is a significant difference between before and after brushing teeth with horizontal, roll and fone's techniques. The average surface of these cleaned teeth is no more than 75% of the tooth surface. The horizontal method averages 51.55%, roll 72.57% and fone's 70.05%. Judging from the increase in hygiene index score, the highest horizontal method increased hygiene index by 34.5%. There is no difference in hygiene index after brushing teeth with horizontal roll and fone's methods. It is recommended to respondents to brush their teeth at least twice in the morning before eating and at night before going to bed with brushing techniques that have been taught, especially fone's. For further researchers, it is recommended to examine the hygiene index with other brushing methods in order to see more effective methods in cleaning all tooth surfaces.

IESMGCFFOgram: A new method for multicomponent vibration signal demodulation and rolling bearing fault diagnosis

Resonance demodulation of vibration signals is an essential strategy for rolling bearing fault diagnosis. However, most resonance demodulation methods focus on searching for only one optimal informative frequency band, which is only valid for single-component signal analysis. In industrial applications, many interfering components are included in the monitored signal because of the complex equipment composition and running environment. Therefore, multicomponent vibration signal demodulation methods are more practical for bearing fault diagnosis. Although decomposition methods are commonly used to extract multiple components from a mixed signal, they are not accurate enough because they are implemented based on empirical statistical indicators rather than fault-related information with physical meaning. To achieve accurate multicomponent vibration signal demodulation, the improved envelope spectrums via multigroup candidate fault frequency optimization-gram (IESMGCFFOgram) is proposed. Firstly, the candidate fault frequencies (CFFs) are identified based on the local maxima distribution of the Spectral coherence (SCoh). Secondly, multigroup CFFs (MGCFFs) are extracted from the original CFFs according to the multiple relationship. Thirdly, the MGCFFs are optimized by redundant group elimination, significance ranking, and homologous group fusion. Finally, several IESMGCFFOgrams and corresponding improved envelope spectrums (IESs) are respectively obtained by using different diagnostic indicators and the classical 1/3-binary tree filter structure, where the diagnostic indicators are adaptively designed based on each group of CFFs in the final MGCFFs. It is worth noting that the noise benchmark of all IESs will be unified by subtracting the minimum before constructing each IESMGCFFOgram. The effectiveness and superiority of the proposed method are validated by simulated and experimental data. Results show that the proposed IESMGCFFOgram has higher demodulation accuracy and computational efficiency compared with three state-of-the-art methods.

Fault diagnosis of low-speed heavy load super large rolling bearing based on deep learning

The conventional eigenvalue alarm mode has a high rate of false alarm and missed alarm for the low-speed heavy load super large rolling bearing. Besides, the traditional signal processing method such as envelope spectral analysis is difficult to extract its fault characteristic frequencies, resulting in a high rate of false diagnosis and missed diagnosis. In order to solve the above problems, an intelligent diagnosis method for the low-speed heavy load super large rolling bearing based on deep learning is proposed. The proposed method mainly utilizes the strong robustness of deep learning algorithm to the quality of original vibration data in the field of fault diagnosis. Firstly, an effective signal acquisition scheme is designed to solve the problem that the signal characteristics of low-speed heavy load super large rolling element bearing are difficult to be acquired. Then, the collected data are randomly divided into training sets, verification sets and test sets by using data enhancement technology. Subsequently, input the divided training set samples into 1-dimensional convolution neural network (1DCNN) deep learning model for learning and training to construct the 1DCNN learning model and set network structure parameters. Meanwhile, the optimal training model is obtained by validating the updating effect of model parameters through validation set. Finally, the test data is input into the trained model to realize intelligent diagnosis. Effectiveness of the proposed method is verified by the vibration data of a wind power main bearing.

칫솔질 방법에 따른 대상자의 주관적 효과와 구강상태의 전후 차이

Objectives: This Study was conducted to find out the effects on the oral cavity and the subjective effects of subjects between brushing methods based on the Bass’ method.
 Methods: From May 4 to May 11, 2023, Group2 was trained on rotational and random methods based on the Bass’ method, and an evaluation was conducted one week later.
 Results: BOP, O’Leary index, and BB check levels were reduced for both Bass’+Rolling method and Bass’+Random method.
 Conclusions: The Rolling method may not necessarily be an advantageous method of brushing teeth, and repeated brushing education and efforts by dental hygienists for follow-up care are required to change tooth brushing.

Mechanical properties of cross rolled CoCrFeMnNi high entropy alloy

In recent years, there has been a significant interest in high-entropy alloys (HEAs) due to their unique compositions, microstructures, and the ability to customize their properties. This study investigated the microstructure and deformation behaviour of the CoCrFeMnNi HEA under homogenised and rolled conditions. Two rolling methods were employed, namely two-step cross-rolling (TSCR) and multi-step cross-rolling (MSCR). Texture analysis revealed the development of Brass (Bs) {1 10} < 112 > and Goss (G) {011} < 100 > texture components in the samples, with an equivalent strain of 1.609. Notably, the MSCR technique involved rotating the samples at a 45° angle around the normal direction (ND) during alternate passes, which was a novel approach in this study. TSCR samples exhibited the formation of dense lamellar bands, while MSCR samples displayed dense dislocations forming a cell block-type structure. Vickers hardness testing revealed differing hardness outcomes as a result of varying deformation levels in samples. Specifically, the TSCR sample with 80% deformation exhibited the highest hardness, while the 50% deformed MSCR sample displayed a comparatively lower hardness. In the context of room-temperature tensile tests, the 80% deformed TSCR sample showed higher ultimate tensile strength but lower ductility. In contrast, the 80% deformed MSCR sample exhibited a slightly lower ultimate tensile strength but higher percentage elongation compared to the TSCR sample. These findings suggest that multi-step cross-rolling can effectively enhance the strength and ductility of HEAs, providing valuable insights for optimizing their mechanical properties. The microstructural and mechanical characterization presented in this study contributes to understanding deformation mechanisms and property optimization strategies in HEAs, thereby supporting their potential applications across various industries.

Study on the optimization of the classification method of rolling bearing fault type and damage degree based on SFO–VMD

Currently, rolling bearings operate in harsh environments, resulting in acquired signals with a low signal-to-noise ratio. In light of this, this paper proposes an improved variational modal decomposition(VMD) combined with refine composite multi-scale fuzzy entropy (RCMFE) and linear support vector machine (LSVM) for fault diagnosis. Firstly, the sailfish optimization (SFO) algorithm is employed to optimize the important parameter combinations in the VMD algorithm, using the envelope entropy as its objective function. The analysis includes both simulated and real measured signals with varying signal-to-noise ratios. The results demonstrate that, compared to traditional manual parameter setting and empirical modal decomposition methods, this approach effectively addresses the parameter setting issue of VMD in the signal decomposition process. Additionally, it successfully eliminates noise to extract the fault characteristic signal hidden within the original signal. Secondly, the RCMFE algorithm is introduced to overcome the problem of commonly used dimensioned and dimensionless indicators being influenced by load and speed when used as characteristic indicators. By analyzing the influence of load and speed on the RCMFE value, the results demonstrate its strong stability as a feature indicator, unaffected by these factors. For the intelligent classification of failure type and damage degree, LSVM is chosen as the classification method. Analysis results indicate that the distribution characteristics of RCMFE values align better with LSVM compared to the common radial basis function support vector machine, resulting in a significant improvement in diagnosis accuracy.

A noise reduction method of rolling bearing based on empirical wavelet transform and adaptive time frequency peak filtering

The vibration signal of rolling bearing with variable operating conditions contains complex interference components, which will cause low fault diagnosis accuracy, especially in strong noise case. To solve this problem, we proposed a noise reduction method of rolling bearing with variable operating based on empirical wavelet transform and adaptive time-frequency peak filtering (EWT-ATFPF). Firstly, empirical wavelet transform is used to obtain different frequency intrinsic mode functions (IMFs). Secondly, a modified adaptive window length formula for time-frequency peak filtering (TFPF) is constructed by combining the sampling ratio index and a fault sensitivity indicator that calculated by kurtosis and correlation coefficients of IMFs, which can better characterize the impact components. Thirdly, to balance noise reduction effect and the fidelity of IMFs, we proposed an improved TFPF method by adaptively adjusting its windows length. The adaptive method could be carried out using the proposed fault sensitivity indicator and window length formula, and the denoising IMFs could be obtained by ATFPF. Finally, the denoising vibration signal is reconstructed by using the denoising IMFs. The performance of fault diagnosis of the proposed method is verified by using simulated signal and bearing fault test data. The results show that the proposed EWT-ATFPF method could effectively achieve noise reduction under variable operating conditions.