Worm Gearbox Research Articles

Study of Variation in Physiochemical Properties of a Worm Gearbox Lubricant by Blending Castor Oil in the Base Lubricant

In both industrial & heavy-duty applications, oil analysis can be a very effective strategy for preventing gearbox failure, cutting downtime, and managing maintenance costs. In any application, gearboxes are essential to production, the unplanned downtime and the resulting productivity losses can prove to be extremely expensive. Used oil analysis is a preventative maintenance procedure that can point out and pinpoint mechanical breakdown inside the system before it gets serious or becomes too expensive to fix. A thorough oil examination can identify wear, corrosion, and other changes that could be harmful to the health and durability of the machinery. To reduce machine wear, prevent contamination, or optimize working conditions in challenging environments, lubricants are carefully formulated by mixing additives in the base oil. A lubricant with a high acid content can cause varnish and sludge to accumulate, which can clog oil filters and cause machine parts to corrode. When a lubricant degrades, acidic byproducts are produced because of the base stock and additives' process of decomposition in the presence of heat and air. To assess the presence of acidic constituents in the oil or its acid concentration, Total Acid Number (TAN) oil testing is employed. The present work studies the effects on Total Acid Number (TAN) and Total Base Number (TBN) properties of commercial lubricant (EP 90) when castor oil is mixed in percentage concentration in it. The findings of this research have unveiled that the acidic characteristics of the base lubricant (EP90 commercial gear oil) increases as the percentage of castor oil additive is increased in the base lubricant oil.

The new constructive design of the planetary gearbox winches

Although the constructions of planetary reducers are complex, their technical indicators are higher than other gear boxes, and their overall dimensions are smaller. In this article, the constructions of planetary gearboxes were investigated, and an in-depth literature analysis was conducted on their modernization. Application of these gearboxes with high technical indicators and compact dimensions in lifting mechanisms would be suitable for the purpose. Thus, small overall size, large efficiency, and high reliability are the main requirements for winches, and planetary gearboxes can also provide these features. But for some reason, traditionally, geared elevator winches are mainly made with worm gearboxes. This is mainly due to the fact that they provide a high gear ratio in single stage and self-braking. But the efficiency of worm gear box is small, and the friction losses are larger. Therefore, it is time and necessity to replace them with other progressive designed gearboxes in the lifing winches. Taking these into account, in the article, as a result of structural and kinematic analysis and synthesis of planetary gearboxes, new constructions were developed and structural schemes were proposed for their application in the lifting mechanisms. For each of these schemes, the transmission ratio and efficiency were evaluated and compared with each other. The proposed schemes can be applied in the different types of lifting winches. The application fields of winches are different. Winches are more commonly used in drilling equipment and elevators. Winches are the main units of these equipments. Winches and their braking systems must meet operational requirements and operating conditions. Their strength and traction must be sufficient to perform the most demanding technological operations. Lifting and lowering speeds should ensure accident-free operations, be economically efficient and highly productive.

Model Analysis Of Worm Gear Pair System Using Finite Element Analysis

Spur gear, helical gear, worm gear, and bevel gear are all important components in industrial applications such as vehicles, pushes, conveyors, elevators, bowl mill, rolling mills, ribbon blender, machine tools, aeroplanes, and windmills. When various types of defects, such as wear, tooth breakage, corrosion, and scratches on bearings, appear in gearboxes, normal machine function may be abruptly terminated. As a result, output and dependability suffer. As a result, several quality tracking and evaluation approaches have been adopted by companies. Finite element analysis (FEA) is one of the approaches. This research paper presents the FEA of a ribbon blender worm gear pair by using Ansys 18.0 to identify the weak gear of the worm gear pair, natural frequency, and deformation. Proe-5 utilized for creation of three-dimensional geometry of threaded worm and toothed worm wheels, as well as other related elements such as shafts and bearings. Steel is used for the worm, shaft, and bearing, whereas bronze is used for the worm wheel. Ansys 18.0 is implemented to carry out worm gear pair model analysis. The results demonstrate that the worm wheel had the most deformation when compared to the worm, and that the natural frequency is greater than the operational frequency of the worm gear pair. The findings of the research study, worm wheel deteriorate early than worm, model analysis plays a significant role in vibration monitoring of worm gear pair, and this work is valuable for further fault analysis of ribbon blender worm gearbox utilising vibration response.

Defect Categorization of Ribbon Blender Worm Gearbox Worm Wheel and Bearing Based on Artificial Neural Network

There is a demand for worm gearboxes in diversified industrial fields that include machinery such as escalators, ribbon blenders, pulverisers, bowl mills, etc. because of their peculiar characteristics like torque and quick retardation. The most commonly occurring defects in a worm gear box are scratches that develop in the worm gear and in bearings. Early defect categorization is required to prevent a sudden breakdown that would decrease production. The defect is depicted in different cases, which include defects in the gear tooth and the outer and inner races of the bearing. In another case, the defect is considered in the gear tooth as well as the bearing. The severity is designated using the ANN. The experiments were performed under these conditions with a good worm gearbox to capture vibration response signatures. Using these values as an input to the ANN, the model is trained. Experimental results show that vibration amplitude increases with fault progression in the worm gearbox, and the trained ANN model effectively categorizes worm gearbox faults with an accuracy of 97.12%.

Influence of the Lubricant Temperature of Splashed Lubricated Worm Gearbox on Churning Power Losses

Influence of the Lubricant Temperature of Splashed Lubricated Worm Gearbox on Churning Power Losses

Increasing the operational parameters of worm gears

An analysis of factors that reduce the operational parameters of worm gearboxes has been carried out. Equations for calculating the efficiency of a worm gear, contact pressure, and the residual service life are given. It is shown that the friction angle and friction coefficient depend on the material of the worm and wheel teeth, the roughness of the working surfaces of the worm pair matings, the quality of the lubrication, and the sliding speed. Significant contact pressures and friction losses in meshing lead to heat generation, a decrease in efficiency and service life of the worm gear. To produce a worm gear with a maximum radius of a conjugate globoid-cylindrical pair with an involute profile, it is proposed to use rotational turning of the globoid worm turns with a multi-blade forcedly rotating tool, and the wheel teeth with a hob cutter. A nickel antifriction solid lubricating coating containing carbon nanotubes has increased hardness and resistance to abrasion. A method for manufacturing a ZT-type worm gear is proposed, as well as methods that reduce contact stresses and the coefficient of friction during operation of a mating worm pair to increase the service life of worm gears.

Investigation of parameters for fault detection of worm gear box using denoise vibration signature

In industrial applications, worm gearboxes are a key element. In a worm gearbox, as the material of a worm wheel is softer than that of a worm screw, the worm wheel gear is vulnerable to failure through various modes like pitting, wearing out, or tooth breakage during the sliding process. Due to this, it is essential to monitor the failure of the worm wheel gear of the worm gearbox, and it has gained importance for the diagnosis of faults in gearboxes. The present work focuses on the investigation of the effect of worm wheel tooth breakage, worm wheel bearing outer race, and varying load on vibration signature amplitude and frequency domain statistical features such as root mean square (RMS), crest factor, kurtosis, mean, peak to peak, skewness, sample variance, and standard deviation. The experimental setup is fabricated to conduct the experimental trials. An OR34 FFT analyzer with NVGate software is used to acquire the frequency domain vibration signature. Experimental results show that captured vibration signature amplitude for healthy worm wheel and bearing increased as fault occurred on the worm wheel, and bearing and frequency domain statistical features value changed with the change in fault location in the worm gearbox.

IOT-ENABLED SMART LIGHTING SYSTEM WITH INTEGRATED PULLEY MECHANISM FOR HOME AUTOMATION

Lighting systems play a fundamental role in our daily life operations. However, the typical light bulb has a finite lifespan and is often installed in hard-to-reach locations, posing a challenge when it needs replacement. To address this issue, we've developed an IoT bulb holder equipped with a pulley mechanism designed to lower itself to a user-accessible height. This innovative system also features built-in light control functionality. Our solution integrates a microcontroller with a Wi-Fi module, a mechanical motor for the pulley mechanism, and an Android app. The Android app serves as the central control hub for the system. The microcontroller manages the DC motor's position and light control, responding to instructions from the app. Light control is achieved through a relay circuit, while the pulley mechanism is operated using a DC motor with a worm gearbox. The worm gearbox was chosen for its self-locking capabilities, eliminating the need for continuous power to maintain the light bulb at the desired height. 3D modeling software was utilized to create the casing and gearing components. Tests with two different microcontrollers: NodeMCU ESP8266 and ESP32 have been conducted. A comparative analysis revealed that the ESP32 is the superior choice for IoT projects due to its enhanced Wi-Fi efficiency and connectivity capabilities.

Experimental investigation for fault diagnosis of a single stage worm gearbox using response surface methodology

Gearbox transmission system is a fundamental factor in the industrial applications. If gearbox stop working due to different faults, it may break normal machine operation and cause a production loss. This research paper focused on fault analysis of combination of worm wheel and bearing by using root mean square (RMS) and response surface method (RSM). Faults considered on outer race and inner race of worm wheel bearing, on worm wheel tooth. These faults and load were considered as an important independent factor to understand their effects on RMS response of worm gearbox. Worm gearbox experimental setup is for laboratory experimentation and RSM for analysis. Twenty-seven experimental trial conducted for three level of parameters based on design of experiment (DOE). Vibration based response measured in frequency domain and root mean square parameter extracted for the fault analysis. Box behnken design RSM is implemented to investigate independent parameters effect on output parameter i.e. RMS. The result shows that effect of faulty inner race bearing is more on worm wheel as compared to faulty outer race bearing and fault parameter influence RMS than load parameter.

Intelligent fault diagnosis of worm gearbox based on adaptive CNN using amended gorilla troop optimization with quantum gate mutation strategy

The worm gearbox is a power transmission system that has various applications in industries. Being vital element of machinery, it becomes necessary to develop a robust fault diagnosis scheme for worm gearbox. Due to advancements in sensor technology, researchers from academia and industries prefer deep learning models for fault diagnosis. The optimal selection of hyperparameters (HPs) of deep learning models plays a significant role in stable performance. Existing methods mainly focused on manual tunning of these parameters, which is a troublesome process and sometimes leads to inaccurate results. Thus, exploring more sophisticated methods to optimize the HPs automatically is important. In this work, a novel optimization, i.e. amended gorilla troop optimization (AGTO), has been proposed to make the convolutional neural network (CNN) adaptive for extracting the features to identify the worm gearbox defects. Initially, the vibration and acoustic signals are converted into 2D images by the Morlet wavelet function. Then, the initial model of CNN is developed by setting hyperparameters. The search space of each HP is identified and optimized by the developed AGTO algorithm. The classification accuracy has been evaluated by AGTO-CNN, which is further validated by the confusion matrix. The performance of the developed model has also been compared with other models. It has been observed that the proposed AGTO not only achieved the highest degree of recognition accuracy i.e. 98.95 % but also achieved the least standard of deviation of 0.2145 than that of other classifiers. The AGTO algorithm is examined on twenty-three classical benchmark functions and the Wilcoxon test which demonstrates the effectiveness and dominance of the developed optimization algorithm. The results obtained suggested that the AGTO-CNN has the highest diagnostic accuracy, more stable while diagnosing the worm gearbox.

Performance evaluation of adjustable stroke piston pump with varying fulcrum position

Lubrication of machines is done manually or using automatic systems, the purpose of the automatic lubrication system is to provide specified amount of lubrication at specified lubrication points using a controlled pump system. The proposed work is based on auto control variable displacement pump and referencing to literature survey it is a novel work involves, an innovative kinematic link base stroke changing mechanism. The stoke variation mechanism serves to alter the stroke of the piston pump in controlled manner to control the flow rate of the pump precisely. The design of the linkage for stoke variation has been done. The previous studies deal with the theoretical design and analysis of the components of the variator mechanism. The variator mechanism is used to vary the angle of oscillation of the output and thereby the stroke of the piston of the pumping element. The variator mechanism design includes the design of the worm gear box and its components, variator screw and nut arrangement. Selection of the motor to operate the linkage and the force analysis based on the motor specifications has been carried out. The present work deals the manufacturing and preliminary testing of the device. In that testing was carried out at various input speeds to the pump and the actual discharge from the pump was measured and the volumetric efficiency of the pump at the particular speed and fulcrum position was determined. Results of the two fulcrum positions were compared and evaluated. Further testing will be carried out in the next six months and the optimization of the fulcrum position for desired discharge will be done. The IOT circuit and mechanism will then be configured suitably.

Assessing the Influence of Micro Lubricant Additives on Worm Gearbox Power Consumption: A Tribological Investigation Utilizing Hazelnut Oil-Based Lubricants

As technology progresses, lubricant additives are becoming more prevalent and expanding the application areas of modern tribology. This study aimed to investigate the tribological properties of recently used additive particles and their impact on worm gearbox power consumption. To achieve this, hazelnut oil was chosen as the base oil, and graphite, boric acid (BA), and hexagonal boron nitride (hBN) particles were added in certain proportions. The effect of each additive on the coefficient of friction and wear was investigated under different operating speeds and loads using a pin-on-disc tester. Optimal additive ratios for worm gearbox oils were utilized based on the previous studies, and oils using hazelnut oil as the base were prepared accordingly. The efficacy of the additives was evaluated using a worm gearbox test system.

Experimental Investigation on Churning Power Loss of Splash Lubricated Worm Gear

Churning power losses are a complex phenomenon that produces critical power losses when considering the splash lubrication of gear units. This article describes the method to investigate the churning power loss in a worm gearbox. A particular test rig was designed and fabricated to experiment on single start worm gear incompletely submerged in an oil bath. The direct torque measurement technique was used to determine the churning power losses. Experiments have been conducted to determine the impact of a variety of operating conditions on churning power losses, including worm speeds, gear immersion depth, lubricant temperatures, and lubricant type (mineral and synthetic). It was found that the churning losses were significantly affected by the worm shaft orientation, speed of gear, and the depth of immersion (static head). The lubricant's temperature is more essential than the type of lubricant in terms of churning power loss.

Fault Analysis of Worm Gear Box Using Symlets Wavelet

This research highlights the vibration analysis on worm gears at various conditions of oil using the experimental set up. An experimental rig was developed to facilitate the collection of the vibration signals which consisted of a worm gear box coupled to an AC motor. The four faults were induced in the gear box and the vibration data were collected under full, half and quarter oil conditions. An accelerometer was used to collect the signals and for further analysis of the vibration signals, MATLAB software was used to process the data. Symlet wavelet transform was applied to the raw FFT to compare the features of the data. ANN was implemented to classify various faults and the accuracy is 93.3%.

Motorised System for Loading and Unloading of Press Dies in Press Machine

Abstract: The paper describes the use of a plate heat exchanger in transferring heat between two fluids. It explains that the design of a plate heat exchanger involves multiple heat transfer plates held together by a fixed plate and a loose pressure plate. These plates have a gasket arrangement that creates separate channel systems for the primary and secondary media, allowing for counter-current flow and effective heat transfer. To manufacture the corrugated plates, the text mentions the use of Hydraulic Press Machines with different capacities depending on the plate dimensions. The process involves changing heavy press dies, which is time-consuming and decreases overall plant efficiency. To address this issue, a proposed solution is to semiautomate the loading and unloading of press dies. A mechanical system is designed, comprising a table with rollers and linear motion guideways. The system utilizes a Ball screw mechanism powered by a servo motor and driven by a Worm Gearbox. The orientation of the motor, perpendicular to the line of motion, saves space and provides more working space on the workshop floor. In summary, the proposed solution aims to increase efficiency and safety by implementing a mechanical system for loading and unloading press dies in plate heat exchanger manufacturing, incorporating a Ball screw mechanism, servo motor, and Worm Gearbox.

Fault Diagnosis of Rotating Machinery Based on 1D–2D Joint Convolution Neural Network

Convolutional neural network has been widely used in fault diagnosis of mechanical devices. In particular, two-dimensional convolutional neural network requires manual selection of multi-scale transformation to transform vibration signal into two-dimensional structure. Although one-dimensional convolution neural network can directly use the vibration signal for convolution processing, it can't make full use of the nonlinear information in one-dimensional space. In order to make full use of the advantages of one-dimensional and two-dimensional convolutional neural networks, this paper develops a one-dimension in tandem with two-dimension joint convolutional neural network (1D-2D JCNN) for rotating machinery fault diagnosis. More specifically, one-dimensional convolution is employed to adaptively obtain the multi-scale feature vectors of the vibration signal, and these feature vectors are constructed into two-dimensional maps, and then these two-dimensional vectors are used as the input of the two-dimensional convolutions neural network. Take the cross-entropy loss function as the loss function, and use the error back propagation algorithm to optimize the filter parameters of the 1D-2D JCNN model to obtain the final fault diagnosis model. Using the motor bearing data set and the worm gearbox data set, the experimental results show the excellent classification performance of bearings and gears under different working conditions. The average diagnostic accuracy of 10 runs on CWRU bearing data set is 99.92%, and the variance is 3.96e-6. The average diagnostic accuracy of 10 runs on worm gear data set is 99.82%, and the variance is 7.82e-6. Compared with the traditional fault diagnosis model and the latest convolution neural network method, the 1D-2D JCNN shows better diagnosis performance.

Evaluation of performance of vibration signatures for condition monitoring of worm gearbox by using ANN

Evaluation of performance of vibration signatures for condition monitoring of worm gearbox by using ANN

Sliding Gate Opener System with Smartphone Control Using Bluetooth Connection

This paper presents the sliding gate opener system that android smartphones can remotely control using Bluetooth protocols. The gate opener is driven by a 12-pole single-phase induction motor of permanent-split-capacitor type (PSC motor) coupled with a worm gearbox and rack-pinion power transmission. The PSC motor installation is wired to rotate either forward or in the reverse direction by using a contactors arrangement controlled by the Arduino microcontroller module. Two limit switches are installed at the ends of the gate opener movement to inform the microcontroller to stop the motor automatically when the gate opener touches them. Moreover, the microcontroller module has been wired with Bluetooth transceiver HC-06 enabling it to communicate with other Bluetooth devices. The microcontroller module has been programmed so that the gate opener movement can be remotely governed from android smartphones and controlled at the site by operating a set of pushbutton switches mounted onto the control panel's door. The functional test has been conducted on the sliding gate opener system, and the result shows that the gate opener movement can successfully be controlled locally through operating pushbutton switches and remotely using an android smartphone with up to 8 meters.

L-Moments Ratio-Based Condition Indicators for Diagnosis of Fault in a Worm Gearbox

L-Moments Ratio-Based Condition Indicators for Diagnosis of Fault in a Worm Gearbox

The Impact of Lubricant Viscosity and Materials on Power Losses and Efficiency of Worm Gearbox

The Impact of Lubricant Viscosity and Materials on Power Losses and Efficiency of Worm Gearbox