Size-Controllable and pH-Sensitive Whey Protein Microgels as High-Performance Aqueous Biolubricants.

Developing efficient aqueous biolubricants has become a significant focus of research due to their prevalence in biotribological contacts and enormous potential in soft matter applications. In this study, size-controllable, pH-sensitive whey protein microgels were prepared using a water-in-water emulsion template method from protein-polysaccharide phase separation. The granular hydrogel from the protein microgels exhibited superior lubricity, obtaining 2.7-fold and 1.7-fold reductions in coefficient of friction (µ) compared to native protein and human saliva (µ = 0.30 compared to 0.81 and 0.52, respectively). The microgels also exhibited outstanding load-bearing capabilities, sustaining lubrication under normal forces up to 5 N. Microgels with a smaller size (1 µm) demonstrated better lubricating performance than 6 and 20 µm microgels. The exceptional lubricity was from a synergistic effect of the ball-bearing mechanism and the hydration state of the microgels. Particularly at pH 7.4, the hydration layer surrounding highly negative charges contributed to the electrostatic repulsion among the swollen microgels, leading to an improved buffer ability to separate contact surfaces and effective rolling behavior. Such pH-dependent repulsion was evidenced using a surface forces apparatus that the adhesion between the whey protein-coated surfaces and protein-mica surfaces decreased from 4.49 to 0.97 mN/m and from 7.89 to 0.36 mN/m, respectively, with pH increasing from the isoelectronic point to 7.4. Our findings fundamentally elucidated the tribo-rheological properties and lubrication mechanisms of the whey protein microgels with excellent biocompatibility and environmental responsiveness, offering novel insights for their food and biomedical applications requiring aqueous biolubrication.

Numerical analysis of stresses on angular contact ball bearing under the static loading with respect to race thickness and housing stiffness.

A 3-dimensional model of the angular contact ball bearing (ACBB) was modeled using Abaqus/standard (Dassault systems- version 2017) to investigate the influence of race thickness on the bearing performance. It was found that the ability to support higher contact stress increased with race thickness. However, large deformations were found to occur on outer race with thickness of 3.3 mm and only small deformations were observed on outer race with a thickness of 9.9 mm. The large deformations induce higher shear stresses on thin races than on thick races. These stresses cause spall growth in bearings and propagate into a network of cracks. As a result of these findings, thin races are prone to failure compared with thick races.

Orbitocranial Penetration of Ball Bearing Gun Trauma: A Prospective Human Cadaveric Study.

Objective We completed a prospective human cadaveric study to determine the ability of a ball bearing (BB) pellet to penetrate the orbit and/or surrounding structures. Methods A single trained sergeant officer discharged an alloy steel air rifle to eight cadaver orbits from four adult human cadaver heads. Five BB pellets each were aimed at three locations (caruncle, upper eyelid, or lower eyelid) at 10 cm and 1 m, and then less specifically, at the orbital region for 3- and 5-m distances. Computed tomography (CT) of the cadaver heads was performed. Final locations of BB pellets are divided into three categories: intracranial, surrounding orbital structures including the pterygopalatine fossa and infratemporal fossa, and orbit. Results Of 40 BB pellets, 37 penetrated soft tissue and were visualized on CT: 19 (51%) rested in the intracranial space, 17 (46%) in surrounding orbital structures, and 1 (3%) within the orbit. The deepest position of a pellet was in the parietal lobe, and most superficial location anterior to the frontal bone. Pellets discharged from 1 m were more likely to rest in the intracranial space compared with those from 10 cm ( p < 0.001), 3 m ( p = 0.011), and 5 m ( p = 0.004). The distance of discharge was associated with final pellet location ( p = 0.001). Conclusion BB guns should be considered dangerous and potentially deadly when aimed at the orbit. Although the thick calvarium can protect the intracranial space from BB penetration, the orbit may be a vulnerable entry point with relatively low resistance, allowing penetration of the intracranial and periorbital spaces.

Analyzing the Accuracy of Digital Sizing on Long-Leg Alignment X-rays by Using a 1-Inch Ball Bearing: A Cheap and Effective Method.

Background and objective Sizing on digital films is important for implants and planning deformity correction. CT is the most accurate digital measurement method. We use a 1-inch ball bearing (cost: $1) to size our long-leg standing films (LLSFs) when planning deformity correction. In this study, we aimed to assess the accuracy of digital measurements calibrated by this method. Methods We conducted An IRB-approved study involving 25 patients having both an LLSF with a 1-inch ball bearing taped to the inner mid-thigh and a CT scanogram. The longest distance in the axial cut of the bilateral ankle, knee, and femoral heads of the CT images were compared to the same anatomic locations on LLSFs calibrated with the ball bearing using the online digital planning software DetroitBonesetter (DBS) and measurements from our Picture Archiving Communication Software (PACS). Five observers performed each measurement. Results The average measurement differences between the gold standard CT scan and LLSFs calibrated with DBS were as follows: 0.110 ± 0.432 mm (femoral head); 2.173 ± 0.0619 mm (knee); and 3.671 ± 0.30 mm (ankle). In PACS, they were as follows: 5.470 ± 0.381 mm (femoral head); 6.248 ± 0.712 mm (knee); and 1.806 ± 0.548 mm (ankle). The intraclass correlation coefficient for 600 measurements by five observers was 0.972. Conclusions The $1 ball-bearing sizing on DBS using LLSFs provides accuracy to <1 mm for the femoral head, 2 mm at the knee, and 3.7 mm at the ankle. It was significantly better than the PACS system for both the femoral head and knee (<0.001), while PACS was better at the ankle (<0.001).

Understanding mechanisms behind the oily mouthcoating perception of pure vegetable oils using tribology.

Tribology is the science of measuring friction between surfaces. While it has been widely used to investigate texture sensations of food applications, it is seldom applied in pure edible oil systems. In this research, we measured friction, viscosity, and solid fat content (SFC) of nine vegetable oils at 30 and 60°C. Polarized static microscopy was used to assess crystal formation between 60 and 30°C. Descriptive sensory analysis and quantification of oral oil coatings were performed on the oils at 60°C. Expressing the friction factor of oil over the Hersey number (calculated using high sheer-viscosity values) showed no differences in friction between 30 and 60°C, except for shea stearin. Static microscopy revealed crystallization occurred at 30°C for shea stearin, whereas no or few crystals were present for other oils. At 30°C, friction at 1 × 10-2 m/s showed an inverse correlation with SFC (R = -0.95) and with high shear rate viscosity (R = -0.84), as well as an inverse correlation (R = -0.73) with "oily mouthcoating" perception. These results suggest that friction could be a predictor of fat-related perceptions of simple oil systems. Additionally, we hypothesize that the presence of crystals in oils could lower friction via a ball-bearing lubrication mechanism.

Economic tolerance design of the P2 raceway based on the quasi-static model of aerospace ball bearings.

This study focused on designing the raceway tolerance of high-precision rolling bearings. After the tolerance error equivalence relationship is defined, the application of the rolling bearing error equalization effect in tolerance design is studied. First, a five-degree of freedom quasi-static model was established for angular contact ball bearings. The waviness was included in the manufacturing error, and the radial and axial runouts of the bearing inner ring were calculated. Second, the error homogenization effect of the rolling bearing was studied, and the error homogenization coefficient was defined. The results of the study demonstrated that the bearing rotary accuracy was higher than the raceway error by an order of magnitude. Third, the manufacturing error range of each precision grade of the rolling bearing raceway was obtained by investigating errors of the equalization coefficient. Finally, the specific value of the raceway tolerance of P2 rolling bearings was obtained.

Tribological Characterization of Micro Ball Bearings with and without Solid-State Lubrication.

The tribological characteristics of a below 1 mm micro ball bearing comprising steel disc and cages coated with thin copper and silver films were investigated. Electroplating and laser cutting were used to manufacture used elements. Friction was measured using a linear stage and an adapted version of a friction-loop method. The obtained results show an interesting relationship between the geometric properties of the micro scale thrust bearing and their performance and operational stability, which can be correlated to similar relationships observed in the macro scale. The most optimal design of the bearing showed stable operation, with the simplified rolling resistance coefficient in the range 0.002 to 0.003, independently of applied load, which was in range 150 mN to 1500 mN. The possibility of creating easily manufacturable micro ball bearings with a low rolling resistance coefficient comprised solely of cheap and sturdy elements was shown.

Lubrication mechanisms of dispersed carbon microspheres in boundary through hydrodynamic lubrication regimes.

HYPOTHESIS: Carbon microspheres have been shown to reduce friction and surface wear at relatively low speeds and high applied loads (i.e., within the boundary lubrication regime). We hypothesize that in dilute colloidal lubricating systems there is an interplay between the size of the carbon microspheres and the lubrication gap size, which determines the dominant lubricating mechanism of the system. EXPERIMENTS: A 60 wt% aqueous glycerol solution was used as the base lubricant and compared to various carbon particle-based lubricant formulations ranging in particle concentrations from 0.05 to 0.30 vol%. The tribological properties of the various lubricant formulations were tested on a pin-on-disk tribometer. A simplified Stribeck plot was produced to understand the changing mechanism of lubrication over a wide range of conditions. FINDINGS: The Stribeck curves show that the carbon microspheres assist lubrication by a rolling mechanism primarily in the boundary lubrication regime. A 0.20 vol% carbon-based lubricant formulation showed the best friction reduction compared to the base lubricant. Increasing speed increases the lubricating gap between the friction pair beyond the size of the particles, thereby nullifying the rolling mechanism of the particles. We introduce a modified specific film thickness parameter to determine the lubrication regime in a particle-lubricant system.

Analysis of Mechanical Properties and Fatigue Life of Microturbine Angular Contact Ball Bearings under Eccentric Load Conditions.

Angular contact ball bearings are common basic components in rotating machinery. During the operation of the bearing, the rolling slips, resulting in contact sliding friction between it and the raceway, which in turn causes wear in the rolling element and increase in the radial clearance of the bearing. The increase in clearance also affects the stiffness of the bearing, which in turn affects the natural frequency and fatigue life of the bearing. At present, there are few studies on the influence of bearing wear (variation of clearance) on life. In this paper, the finite element model based on the theory of contact mechanics is established for the angular contact ball bearing with medium- and high-speed rotation, and the mechanical properties and fatigue life influenced by the internal action of the bearing are analyzed. The effects of radial load and deflection angle on the mechanical properties and fatigue life of the bearing are also studied. Based on the analysis results of bearing contact mechanical properties and clearance changes, the calculation method of bearing life under rolling element wear is established. The influence of the variation of clearance and preload clearance on bearing life is analyzed, and the optimal preload is obtained. The research results of this paper can provide a theoretical basis for optimizing the installation of angular contact ball bearings, reasonably determining the service conditions, and prolonging the service life of bearings, which is necessary for engineering practice.

Vibration, acoustic, temperature, and motor current dataset of rotating machine under varying operating conditions for fault diagnosis.

Rotating machines are often operated under various operating conditions. However, the characteristics of the data varies with their operating conditions. This article presents the time-series dataset, including vibration, acoustic, temperature, and driving current data of rotating machines under varying operating conditions. The dataset was acquired using four ceramic shear ICP based accelerometers, one microphone, two thermocouples, and three current transformer (CT) based on the international organization for standardization (ISO) standard. The conditions of the rotating machine consisted of normal, bearing faults (inner and outer races), shaft misalignment, and rotor unbalance with three different torque load conditions (0 Nm, 2 Nm, and 4 Nm). This article also reports the vibration and driving current dataset of a rolling element bearing under varying speed conditions (680 RPM to 2460 RPM). The established dataset can be used to verify newly developed state-of-the-art methods for fault diagnosis of rotating machines. Mendeley Data. DOI:10.17632/ztmf3m7h5x.6, DOI:10.17632/vxkj334rzv.7, DOI:10.17632/x3vhp8t6hg.7, DOI:10.17632/j8d8pfkvj2.7.

Development of Intelligent Fault Diagnosis Technique of Rotary Machine Element Bearing: A Machine Learning Approach.

The bearing is an essential component of a rotating machine. Sudden failure of the bearing may cause an unwanted breakdown of the manufacturing plant. In this paper, an intelligent fault diagnosis technique was developed to diagnose various faults that occur in a deep groove ball bearing. An experimental setup was designed and developed to generate faulty data in various conditions, such as inner race fault, outer race fault, and cage fault, along with the healthy condition. The time waveform of raw vibration data generated from the system was transformed into a frequency spectrum using the fast Fourier transform (FFT) method. These FFT signals were analyzed to detect the defective bearing. Another significant contribution of this paper is the application of a machine learning (ML) algorithm to diagnose bearing faults. The support vector machine (SVM) was used as the primary algorithm. As the efficiency of SVM heavily depends on hyperparameter tuning and optimum feature selection, the particle swarm optimization (PSO) technique was used to improve the model performance. The classification accuracy obtained using SVM with a traditional grid search cross-validation (CV) optimizer was 92%, whereas the improved accuracy using the PSO-based SVM was found to be 93.9%. The developed model was also compared with other traditional ML techniques such as k-nearest neighbor (KNN), decision tree (DT), and linear discriminant analysis (LDA). In every case, the proposed model outperformed the existing algorithms.

Assessment of error in the MV radiation isocenter position calculated with the Elekta XVI software.

The assessment of the coincidence of imaging and radiation isocenters is an important task of regular quality assurance of medical linear accelerators (linacs) as recommended in national and international quality assurance guidelines. A previously reported investigation of the accuracy of the Elekta XVI software to localize the linac radiation isocenter, by comparing statistically with other independent software, has shown some discrepancies at the sub-mm level. A further investigation is carried out here using a set of reference images and mathematical operations to observe how the Elekta XVI software analyses them. Symmetric mathematical operations on reference images should result in symmetrical outcomes. Three different rotation functions are used in increasing degree of complexity to characterize the Elekta XVI software error in the linac radiation isocenter position. No independent algorithms or phantoms are used in this methodology. The magnitude and direction of the radiation isocenter localization error has been determined to be consistently 0.13 mm or 0.14 mm in the longitudinal direction towards the target depending on the case. The radiation isocenter localization error comprises two separated errors of the Ball Bearing Center by 0.13 mm and MV Field Center by either 0.00 mm or -0.01 mm in the longitudinal direction towards the target. The calculation of the MV Field Center is influenced by the polymethyl methacrylate rod supporting the ball-bearing. The precise value and the root cause of the error cannot be assessed due to the rounding effect of the results reported by the Elekta XVI software and lack of access to the source code.

An Approximate Estimation Approach of Fault Size for Spalled Ball Bearing in Induction Motor by Tracking Multiple Vibration Frequencies in Current.

Fault size estimation is of great importance to bearing performance degradation assessment and life prediction. Until now, fault size estimation has generally been based on acoustic emission signals or vibration signals; an approach based on current signals has not yet been mentioned. In the present research, an approximate estimation approach based on current is introduced. The proposed approach is easy to implement for existing inverter-driven induction motors without complicated calculations and additional sensors, immune to external disturbances, and suitable for harsh conditions. Firstly, a feature transmission route from spall, to Hertzian forces, and then to friction torque is simulated based on a spall model and dynamic model of the bearing. Based on simulated results, the relation between spall size and the multiple characteristic vibration frequencies in friction torque is revealed. Secondly, the multiple characteristic vibration frequencies modulated in the current is investigated. Analysis results show that those frequencies modulated in the current are independent of each other, without spectrum overlap. Thirdly, to address the issue of which fault features modulated in the current are very weak, a fault-feature-highlighting approach based on reduced voltage frequency ratio is proposed. Finally, experimental tests were conducted. The obtained results validate that the proposed approach is feasible and effective for spall size estimation.

Bearing fault diagnosis with nonlinear adaptive dictionary learning.

The monitoring of rotating machinery condition has been a critical component of the Industry 4.0 revolution in enhancing machine reliability and facilitating intelligent manufacturing. The introduction of condition-based monitoring has effectively reduced the catastrophic events and maintenance cost across various industries. One of the major challenges of the diagnosis remains as majority of the diagnostic model requires off-line analysis and human intervention. The offline analysis, which is normally done by previous experience, involves tuning model parameters to improve the performance of the diagnostic model. However, for newly developed models, the knowledge of the unknown parameters does not exist. One way to resolve this issue is through learning using adaptation. The adaptation algorithm adjusts itself by newly acquired data. Hence, improvement of the model performance is achieved. In this paper, a nonlinear adaptive dictionary learning algorithm is proposed to achieve early fault detection of bearing elements without using the conventional computation heavy algorithm to update the dictionary. Deterministic and random data separation is implemented using the autoregressive model to reduce the background noise. The filtered data is further analyzed by the Infogram to reveal the impulsiveness and cyclostationary signature of the vibration signal. The dictionary is initialized using random parameters. Instead of using the k means singular value decomposition algorithm to compute the dictionary for adaptation, the unscented Kalman filter (UKF) is implemented to update the dictionaries using the filtered signal from the Infogram. The updating algorithm does not require computation of the dictionary, and no previous knowledge of the dictionary's parameters is needed. The updated dictionary contains the detected fault signature from the Infogram and, therefore, is used for further fault analysis. The proposed algorithm has the advantage of self-adaptation, the capability to map the non-linear relationship of the signal and dictionary weights. The algorithm can be used in the various condition-based monitoring of rotating machineries to avoid additional human efforts and improve the performance of the diagnostic model.

Adaptive online dictionary learning for bearing fault diagnosis.

One of the most common parts to maintain system balance and support the various load in rotating machinery is the rolling element bearing. The breakdown of the element in bearings leads to inefficiency and sometimes catastrophic events across various industries. The main challenge over the last few years for fault diagnosis of bearings is the early detection of fault signature. In this paper, an adaptive online dictionary learning algorithm is developed for early fault detection of bearing elements. The dictionary is trained using a set of vibration signal from a heavily damaged bearing. The enveloped signal of the bearing is obtained using the Kurtogram and split into several sections. The K-SVD algorithm is implemented to the dictionaries corresponding to the enveloped signal. OMP is implemented with the calculated dictionaries to obtain the sparse representation of the testing signal. Then the envelope analysis is implemented to obtain the fault signal from the recovered signal by the dictionaries. The adaptive algorithm is added to the dictionary learning to update the dictionary based on newly acquired data with the weighted least square method. Without retraining the dictionaries using the K-SVD algorithm, the computation speed is significantly improved. The proposed algorithm is compared with a traditional dictionary learning algorithm to show the improvement in detection of new fault frequency and improved signal to noise ratio.

Intelligent Ball Bearing Fault Diagnosis Using Fractional Lorenz Chaos Extension Detection.

In this study we used a non-autonomous Chua's circuit, and the fractional Lorenz chaos system. This was combined with the Extension theory detection method to analyze the voltage signals. The bearing vibration signals, measured using an acceleration sensor, were introduced into the master and slave systems through a Chua's circuit. In a chaotic system, minor differences can cause significant changes that generate dynamic errors. The matter-element model extension can be used to determine the bearing condition. Extension theory can be used to establish classical and sectional domains using the dynamic errors of the fault conditions. The results obtained were compared with those from discrete Fourier transform analysis, wavelet analysis and an integer order chaos system. The diagnostic rate of the fractional-order master and slave chaotic system could reach 100% if the fractional-order parameter adjustment was used. This study presents a very efficient and inexpensive method for monitoring the state of ball bearings.

Detection of weak fault using sparse empirical wavelet transform for cyclic fault.

The successful prediction of the remaining useful life of rolling element bearings depends on the capability of early fault detection. A critical step in fault diagnosis is to use the correct signal processing techniques to extract the fault signal. This paper proposes a newly developed diagnostic model using a sparse-based empirical wavelet transform (EWT) to enhance the fault signal to noise ratio. The unprocessed signal is first analyzed using the kurtogram to locate the fault frequency band and filter out the system noise. Then, the preproc signal is filtered using the EWT. The l q -regularized sparse regression is implemented to obtain a sparse solution of the defect signal in the frequency domain. The proposed method demonstrates a significant improvement of the signal to noise ratio and is applicable for detection of cyclic fault, which includes the extraction of the fault signatures of bearings and gearboxes.

A 10-Year Analysis of Head and Neck Injuries Involving Nonpowder Firearms.

Objectives Firearms have an enduring and visible presence within American culture. However, the public health impact of nonpowder firearms and other "toy" guns has not been fully studied. These guns-including BB guns (ie, ball bearing), paintball guns, and pellet guns-are typically marketed to a younger audience. The objective of this study is to analyze head and neck injuries related to nonpowder firearms. Study Design Cross-sectional analysis of a national database. Setting Academic medical center. Subjects and Methods The National Electronic Injury Surveillance System was queried for head and neck injuries involving nonpowder guns, including air, BB, and pellet guns, and associated ammunition. Analysis of age, sex, incidence, injury location, and diagnosis was performed. Results From 2005 to 2014, there were 1695 cases recorded, or 55,060 estimated emergency room visits, due to injuries related to nonpowder guns and fired ammunition. The majority of patients were male (80.9%). These injuries were most common in children 6 to 12 years of age (37.9%), followed by those 13 to 18 years old (27.1%) and adults (≥19 years old; 17.8%), while preschool children (0-5 years) represented 17.2%. The most common injury diagnosis was penetrating foreign body (34.9%), followed by lacerations (24.3%) and contusions/abrasions (13.7%). Conclusion Nonpowder and other nonlethal firearm-related injuries to the head and neck region are a frequent source of emergency room visits nationally. Safety measures and public education on a mainstream level are required.

Research on the Mechanical Properties of "Z" Type Double-Decker Ball Bearings.

The mechanical model of a "Z" type double-decker ball bearing under the action of radial load is established in this paper on the basis of the Hertz contact theory. According to the security contact angle theory, the influences of inner and outer bearings' internal clearances on the bearing's static load carrying capacity, radial deformation, radial stiffness, and load distribution of balls are analyzed. This model is verified in both stationary and rotational loading experiments. Moreover, the simulation results show that the static load carrying capacity of Z type bearing is smaller than that of either inner bearing or outer bearing that is contributed to compose the Z type bearing. The static load carrying capacity of a Z type bearing reduces with the increase of the inner and outer bearings' internal clearance. These simulation results also indicate that the contact angle of the maximum loaded ball in the outer bearing easily exceeds its security contact angle compared with the inner bearing, which, as the main factor, may cause the Z type bearing to overload and to fail. In this sense, the investigated Z type bearings are unfit to apply to situations with heavy load, high speed, or high precision.

Failure analysis of the ball bearings of dental air turbine handpieces.

BACKGROUND: The aim of this study was to identify the nature and causes of deterioration and failure in dental handpiece ball bearings and thus provide guidance for clinical handling for service longevity. METHODS: The bearings of 36 turbine assemblies were dismantled for visual inspection, documented using a digital camera, and examined using scanning electron microscopy, as appropriate. RESULTS: For the metal parts of the ball bearing assembly, defects observed were mainly wear arising from the running load and corrosion. This was in the form of scratches and discoloured circumferential bands on the balls, and dull or worn surfaces extending around the circumference of the raceways. Cage damage including cracking, fracture, surface rubbing and distortion occurred, in varying degrees, in every failed turbine. CONCLUSIONS: Dental ball bearing failure modes have been identified. Cumulative effects of damage from corrosion and mechanical factors lead to handpiece deterioration. The cage was found to be very vulnerable to damage, and this may be the key limitation on bearing lifetime. Autoclaving may contribute to that, as it does to corrosion in the absence of adequate lubrication, but this seems to be minor in comparison to the effects of abuse. There is no justification for failing to observe usage and sterilization instructions.