Enhanced automated spiral bevel gear inspection

Presented here are the results of a manufacturing and technology program to define, develop, and evaluate an enhanced inspection system for spiral bevel gears. The method uses a multi-axis coordinate measuring machine which maps the working surface of the tooth and compares it with nominal reference values stored in the machine's computer. The enhanced technique features a means for automatically calculating corrective grinding machine settings, involving both first and second order changes, to control the tooth profile to within specified tolerance limits. This enhanced method eliminates the subjective decision making involved in the tooth patterning method, still in use today, which compares contract patterns obtained when the gear is set to run under light load in a rolling test machine. It produces a higher quality gear with significant inspection time and cost savings

Virtual sensing for gearbox condition monitoring based on extreme learning machine

Gearbox, as a critical component to convert speed and torque to maintain machinery normal operation in the industrial processes, has been received and still needs considerable attentions to ensure its reliable operation. Direct sensing and indirect sensing techniques are widely used for gearbox condition monitoring and fault diagnosis, but both have Pros and Cons. To bridge their gaps and enhance the performance of early fault diagnosis, this paper presents a new virtual sensing technique based on extreme learning machine (ELM) for gearbox degradation status estimation. By fusing the features extracted from indirect sensing measurements (e.g. in-process vibration measurement), ELM based virtual sensing model could infer the gearbox condition which was usually directly indicated by the direct sensing measurements (e.g. offline oil debris mass (ODM)). Different state-of-the-art dimension reduction techniques have been investigated for feature selection and fusion including principal component analysis (PCA) and its kernel version, locality preserving projection (LPP) method. The effectiveness of the presented virtual sensing technique is experimentally validated by the sensing measurements from a spiral bevel gear test rig. The experimental results show that the estimated gearbox condition by the virtual sensing model based on ELM and kernel PCA well follows the trend of truth data and presents the better performance over the support vector regression based virtual sensing scheme

An Integrated Approach for Gear Health Prognostics

In this paper, an integrated approach for gear health prognostics using particle filters is presented. The presented method effectively addresses the issues in applying particle filters to gear health prognostics by integrating several new components into a particle filter: (1) data mining based techniques to effectively define the degradation state transition and measurement functions using a one-dimensional health index obtained by whitening transform; (2) an unbiased l-step ahead RUL estimator updated with measurement errors. The feasibility of the presented prognostics method is validated using data from a spiral bevel gear case study

A hybrid multi-objective evolutionary algorithm-based semantic foundation for sustainable distributed manufacturing systems

Rising energy prices, increasing maintenance costs, and strict environmental regimes have augmented the already existing pressure on the contemporary manufacturing environment. Although the decentralization of supply chain has led to rapid advancements in manufacturing systems, finding an efficient supplier simultaneously from the pool of available ones as per customer requirement and enhancing the process planning and scheduling functions are the predominant approaches still needed to be addressed. Therefore, this paper aims to address this issue by considering a set of gear manufacturing industries located across India as a case study. An integrated classifier-assisted evolutionary multi-objective evolutionary approach is proposed for solving the objectives of makespan, energy consumption, and increased service utilization rate, interoperability, and reliability. To execute the approach initially, text-mining-based supervised machine-learning models, namely Decision Tree, Naïve Bayes, Random Forest, and Support Vector Machines (SVM) were adopted for the classification of suppliers into task-specific suppliers. Following this, with the identified suppliers as input, the problem was formulated as a multi-objective Mixed-Integer Linear Programming (MILP) model. We then proposed a Hybrid Multi-Objective Moth Flame Optimization algorithm (HMFO) to optimize process planning and scheduling functions. Numerical experiments have been carried out with the formulated problem for 10 different instances, along with a comparison of the results with a Non-Dominated Sorting Genetic Algorithm (NSGA-II) to illustrate the feasibility of the approach.The project is funded by Department of Science and Technology, Science and Engineering Research Board (DST-SERB), Statutory Body Established through an Act of Parliament: SERB Act 2008, Government of India with Sanction Order No ECR/2016/001808, and also by FCT–Portuguese Foundation for Science and Technology within the R&D Units Projects Scopes: UIDB/00319/2020, UIDP/04077/2020, and UIDB/04077/2020

Automated inspection and precision grinding of spiral bevel gears

The results are presented of a four phase MM&T program to define, develop, and evaluate an improved inspection system for spiral bevel gears. The improved method utilizes a multi-axis coordinate measuring machine which maps the working flank of the tooth and compares it to nominal reference values stored in the machine's computer. A unique feature of the system is that corrective grinding machine settings can be automatically calculated and printed out when necessary to correct an errant tooth profile. This new method eliminates most of the subjective decision making involved in the present method, which compares contact patterns obtained when the gear set is run under light load in a rolling test machine. It produces a higher quality gear with significant inspection time and cost savings

Dynamic response and dangerous point stress analysis of gear transmission system

Gear transmission is the principal power transmission mode of many machine, the reliability of transmission system has important influence on the accomplishment of daily task. This paper made a gear transmission system as the research object, we build the two-stage gear transmission system model and calculate its dynamic response in theory. Then, we study the mesh stiffness of gear concerning the variation of the mesh position from the gear transmission system. On the basis of these work, we establish the gear system’s finite element simulation model considering the tooth contact of internal gear system. After the simulation, we had get the contact response and the time history of some important area’s equivalent stress. Through these work, we can study the contact stress of the two-stage gear system in theory method and finite element simulation method, which has a guiding significance on the optimum structural design of two-stage transmission gear system

Model-based sensor location selection for helicopter gearbox monitoring

A new methodology is introduced to quantify the significance of accelerometer locations for fault diagnosis of helicopter gearboxes. The basis for this methodology is an influence model which represents the effect of various component faults on accelerometer readings. Based on this model, a set of selection indices are defined to characterize the diagnosability of each component, the coverage of each accelerometer, and the relative redundancy between the accelerometers. The effectiveness of these indices is evaluated experimentally by measurement-fault data obtained from an OH-58A main rotor gearbox. These data are used to obtain a ranking of individual accelerometers according to their significance in diagnosis. Comparison between the experimentally obtained rankings and those obtained from the selection indices indicates that the proposed methodology offers a systematic means for accelerometer location selection

Predictive modelling and parametric optimization of minimum quantity lubrication assisted hobbing process

Abstract: This paper focuses on parametric analysis, modelling, and parametric optimization of minimum quantity lubrication assisted hobbing (MQLAH) using environment friendly lubricant for manufacturing superior quality spur gears. Influences of hob cutter speed, axial feed, lubricant flow rate, air pressure and nozzle angle on the deviations in total profile, total lead, total pitch and radial runout and flank surface roughness parameters were studied by conducting 46 experiments using Box-Behnken method of response surface methodology. Results revealed that effect of air pressure is negligible but other parameters have significant impact on the considered responses. Back propagation neural network (BPNN) model was developed to predict microgeometry deviations and flank surface roughness values of the MQLAH manufactured spur gears. The BPNN predicted results found to be very closely agreeing with the corresponding experimental results with mean square error as 0.0063. Real-coded genetic algorithm (RCGA) was used for parametric optimization of MQLAH process to simultaneous minimization of microgeometry deviations and flank surface roughness. Standardized values of the optimized parameters were used to conduct confirmation experiment whose results had very good closeness with RCGA computed and BPNN predicted values and produced spur gear of superior quality. This study proves MQLAH to be a potential sustainable replacement of conventional flood lubrication assisted hobbing for manufacturing cylindrical gears of better quality

NASA Tech Briefs, January 1989

Topics include: Electronic Components & and Circuits. Electronic Systems, A Physical Sciences, Materials, Computer Programs, Mechanics, Machinery, Fabrication Technology, Mathematics and Information Sciences, and Life Sciences

MODEL-BASED DIAGNOSTICS OF SIMULTANEOUS TOOTH CRACKS IN SPUR GEARS

This study aims at developing a numerical model that could be used to simulate the effect of tooth cracks on the vibration behavior of spur gears. Gears are a key component that is widely used in various rotating equipment in order to transmit power and change speed. Any failure of this vital component may cause severe disturbance to production and incur heavy financial losses. The tooth fatigue crack is amongst the most common causes of gear failure. Early detection of tooth cracks is crucial for effective condition-based monitoring and decision making. The scope of this work was widened to include the influence of multiple simultaneous tooth cracks on the time and frequency domain responses at various locations and with different severity levels. As cracks significantly alter the gear mesh stiffness, a finite element analysis was performed to determine the stiffness variation with respect to the angular position for different combinations of crack lengths. A simplified six degrees of freedom nonlinear lumped parameter model of a one-stage gearbox was developed to simulate the vibration response of faulty spur gears with the consideration of inter-tooth friction. Four different multiple crack scenarios were proposed and studied. The performances of various statistical fault detection indicators were investigated. The vibration simulation results of the gearbox obtained using MATLAB were verified with those stated in the published research articles. It was observed that as the severity of a single crack increased, the values of the time-domain statistical indicators increased, with different rates. However, the number of cracks had an adverse effect on the values of all the performance indicators, except the RMS indicator. The number and amplitude of the sidebands in the frequency spectrum were also utilized to detect the severity of the faults in each scenario. It was observed that, in the case of consecutive tooth cracks, the number of spectrum peaks and the number of cracks were well consistent in the frequency range of 4 to 5 kHz. The main finding of this study was that the peak spectral amplitude is the most sensitive indicator to the number and severity of cracks