Gear Inspection Research Articles

Quantitative Detection Method for Surface Angled Cracks Based on Laser Ultrasonic Full-Field Scanning Data

Surface angled cracks on critical components in high-speed machinery can lead to fractures under stress and pressure, posing a significant threat to the operational safety of equipment. To detect surface angled cracks on critical components, this paper proposes a “Quantitative Detection Method for Surface Angled Cracks Based on Full-field Scanning Data.” By analyzing different ultrasonic signals in the full-field scanning data from laser ultrasonics, the width, angle, and length of surface angled cracks can be determined. This study investigates the propagation behavior of ultrasonic waves and their interaction with surface angled cracks through theoretical calculations. The crack width is solved by analyzing the distribution of Rayleigh waves in the full-field scanning data. This paper also discusses the differences in ultrasonic wave propagation between near-field and far-field detection and identifies the critical point between these regions. Different computational methods are employed to calculate the inclination angle and the crack endpoint at various scan positions. Four sets of experiments were conducted to validate the proposed method, with results showing that the errors in determining the width, angle, and length of the surface angled cracks were all within 5%. This confirms the feasibility of the method for detecting surface angled cracks. The quantitative detection of surface angled cracks on critical components using this method allows for a comprehensive assessment of the component’s condition, aiding in the prediction of service life and the mitigation of operational risks. This method shows promising application potential in areas such as aircraft engine blade inspection and gear inspection.

A novel approach based on a mathematical algorithm and measurement of involute flanks for computing spur gear dimensions with unknown parameters

AbstractInvolute gears of all sizes and types are widely utilized in most machinery. Accurate generation of teeth flanks from the nominally required base circle is most vital in gear manufacturing, performance, and inspection. Despite the vital importance of the base circle in actual gear performance, it is not a widely used inspection parameter. Moreover, the size of base circle radius and geometrical center cannot be obtained by direct measurement. In this work, a new mathematical algorithm was developed to evaluate the base circle dimensions by measurement of only the teeth profiles. Measurement of the outer diameter is not required nor is the prior knowledge of any gear dimension such as its module or pressure angle. The evaluated base circle dimensions are then used to compute remaining dominant gear dimensions including its pressure angle. The developed approach was tested on an ideal gear model then applied to the flank points obtained by measurements performed on a Coordinate Measuring Machine (CMM) and by photogrammetry (PG). The experimental results were compared to those measured using a gear roll tester (GRT) and were found to be in very good agreement. The methodology and measurement procedure presented in this work are distinguished by their ability to measure the actual gear dimensions regardless of any tooth modification made to the profile or addendum during manufacturing. This makes the proposed work well-suited for reverse engineering and rapid prototyping reverse calculations.

A Novel Method for Obtaining Analytical Parameters Based on Double-Flank Measurement.

Double-flank measurement is the most commonly used method for full inspection of mass-produced gears and has high measurement efficiency, but it cannot obtain the analytical parameters and is not helpful enough to evaluate the NVH performance of the gears. Based on the double-flank rolling tester with a new principle, a simulation method for double-flank measurement and a solving method for analytical parameters are proposed. Using the simulation method, the double-flank measurements without random error can be obtained through the collision detection algorithm. The solving method uses the iteration to obtain the minimum rolling length of each position of the tooth surface, then obtains the analytical parameters of the gear. In the experiments, the difference between the profile deviations obtained by the solving method and superimposed in the simulation method is less than 0.03 μm. The experiment results have verified the correctness of the simulation method and the solving method. These methods can greatly improve the value of double-flank measurement.

A New Direct and Inexpensive Method and the Associated Device for the Inspection of Spur Gears

This paper proposes a new rapid and straightforward method along with a related device for finding the three basic parameters of an actual external involute spur gear. The number of teeth is easily counted, but the other two parameters—the module and the coefficient of profile shift—are difficult to identify. The method is based on the principle of inspection of the precision of gear teeth, using the dimension over pins, when the maximum distance is measured between the lateral surfaces of two cylindrical rollers of well-controlled dimensions, placed into the spaces between teeth. The dimension over pins is applied as a function of the number of teeth (odd or even) and requires experience (and this is the main disadvantage of the method) for finding the correct maximum distance between pins. The new method eliminates this drawback as it proposes a measuring scheme where four identical rollers are used in a designed inspection device. The system is statically determinate and, therefore, the dimension to be measured is unequivocally found. A new relation for the dimension to be measured is deduced and allows for finding the module and the coefficient of profile shift. The inspection device is described and a concrete case is presented for exemplifying the methodology. A further application permits finding the centre distance for an external spur gearing. Unlike the classical technique where the centre distance is obtained based on the centring surfaces of the wheels, the new method implies only dimensions measured through flank measurements, thus eliminating errors introduced by the deviations between the flanks and the centring surfaces of the wheels.

Transmission-error- and vibration-based condition monitoring of gear wear with contaminated lubricant

The presence of contaminant particles in lubricants could cause severe gear wear and damage. Conventional condition monitoring techniques, i.e., gear inspection and oil/wear debris analysis, are often utilised to assess the presence of wear due to contaminants and its severity such as wear depth. However, the traditional approaches require experience or off-line analysis. Although vibration is a non-invasive technique for fault detection and diagnostics, it is less well developed for wear analysis. Recently, an alternative sensor technology, i.e., transmission error (TE), combining angular measurements obtained from the input and output shafts has shown strong potential for wear monitoring. This work aims to investigate the usage of vibration and TE to identify the existence of contaminants and measure the gear wear severity caused by the oil contamination. This is achieved by conducting gear wear tests on a spur gearbox rig, using clean and contaminated lubricants with silica sand. The test in a normal lubrication condition provides a healthy reference of the spur gearbox under a nominal operating condition. In addition to the nominal operating condition, TE and vibration measurements are collected throughout the tests at different operating conditions, together with moulds of the gear tooth surfaces for wear analysis. The results show that the absolute TE can quantitively measure wear depth, and the RMS of vibration can qualitatively correlate with the trend of the average wear depths. The proposed technique extends the field of using TE to diagnose and predict gear wear in contaminated lubricant conditions.

Optimising the Geometric Parameters of a Gear in a Tractor Transmission Under Constraints Using Kisssoft

Abstract We optimise the speed gears in a tractor transmission with KISSsoft software under three constraints: input power torque, transmission system volume and the gear ratio for each speed. This study aimed to optimise the module, face width, gear quality, centre distance, number of teeth, helix angle, addendum modification coefficient and pressure angle for each speed while considering the above constraints based on an optimisation chart. Tooth bending stress, tooth contact stress, contact ratio and specific sliding were considered during optimisation. Additionally, the effects of changes in a module on the gear profiles, overlap ratio, number of teeth and weight of the gear pair were examined. Strength calculations of gear pairs that were optimised and defined for all geometric parameters with KISSsoft were calculated with the mathematical model described in ISO 6336, and results were then compared. Finally, backlash was minimised for all gear pairs as defined with geometric parameters, and all dimensions and tolerances were determined for gear inspection after manufacturing. A concept design was also presented. We conclude that both the KISSsoft results and mathematical model results are within the range of the target value.

Automated inspection of spur gears using machine vision approach

Automated inspection of spur gears using machine vision approach

Gear metrology – An update

Twenty years after the publication of a first CIRP Keynote Paper about gear metrology [32], this paper reviews again the state of the art in this area. Gear technology has changed and improved significantly during the past decades, which initiated substantial improvements in gear metrology, too. On the other hand, for up to 50% of gear inspection technologies used today, only minor changes are observed. Consequently, this update focuses on new or significantly improved techniques, such that only the combination of both this publication and [32] gives a comprehensive review of the state of the art in gear metrology. Gear technology in emerging markets, e.g., electric cars, large gears for energy systems, and micro gear drives, extends the scope of inspection tasks and requires new measuring methods and instruments, including optical sensors. Measured data, covering the entire gear flank of all teeth, need areal flank descriptions, areal evaluation methods, and areal deviation parameters. New calibration methods and artifacts will improve measurement uncertainties.

Design of the Automated Calibration Process for an Experimental Laser Inspection Stand.

This paper deals with the concept of the automated calibration design for inspection systems using laser sensors. The conceptual solution is based on using a laser sensor and its ability to scan 3D surfaces of inspected objects in order to create a representative point cloud. Problems of scanning are briefly discussed. The automated calibration procedure for solving problems of errors due to non-precise adjustment of the mechanical arrangement, possible tolerances in assembly, and their following elimination is proposed. The main goal is to develop a system able to measure and quantify the quality of produced objects in the environment of Industry 4.0. Laboratory measurements on the experimental stand, including the principal software solution for automated calibration of laser sensors suitable for gear wheel inspection systems are presented. There is described design of compensation eccentricity by Fourier transform and sinusoidal fitting to identify and suppress the first harmonic component in the data with high precision measuring.

Comprehensive Areal Geometric Quality Characterisation of Injection Moulded Thermoplastic Gears.

Injection moulding is currently the most widely employed production method for polymer gears. Current standardised gear metrology methods, which are based on metal gear inspection procedures, do not provide the key information regarding the geometric stability of injection moulded gears and are insufficient for a thorough gear inspection. The study developed novel areal quality parameters, along with a so-called moulding runout quality parameter, with a focus on the injection moulding method. The developed parameters were validated on twenty-nine gear samples, produced in the same moulding tool using various processing parameters. The gears were measured using a high-precision structured-light 3D scanner. The influence of injection moulding process parameters on the introduced novel quality parameters was investigated. The developed moulding runout quality parameter proved to be effective in evaluating the shrinkage that can occur in the injection moulding process. The novel moulding runout parameter returned an average value of −21.8 μm in comparison to 29.4 μm exhibited by the standard parameter on all the gears, where the negative value points directly to mould shrinkages. The rate of cooling was determined to be the most influential factor for the shrinkage of the gear. The developed areal parameters demonstrated to be advantageous in characterising the deviations on the teeth more comprehensively.

Automated inspection of spur gears using machine vision approach

Automated inspection of spur gears using machine vision approach

Detecting Teeth Defects on Automotive Gears Using Deep Learning.

Gears are a vital component in many complex mechanical systems. In automotive systems, and in particular vehicle transmissions, we rely on them to function properly on different types of challenging environments and conditions. However, when a gear is manufactured with a defect, the gear’s integrity can become compromised and lead to catastrophic failure. The current inspection process used by an automotive gear manufacturer in Guelph, Ontario, requires human operators to visually inspect all gear produced. Yet, due to the quantity of gears manufactured, the diverse array of defects that can arise, the time requirements for inspection, and the reliance on the operator’s inspection ability, the system suffers from poor scalability, and defects can be missed during inspection. In this work, we propose a machine vision system for automating the inspection process for gears with damaged teeth defects. The implemented inspection system uses a faster R-CNN network to identify the defects, and combines domain knowledge to reduce the manual inspection of non-defective gears by 66%.

Quality estimation method for gear hobbing based on attention and adversarial transfer learning

Because the gear inspection process is time-consuming and the equipment wears easily, gear machining and inspection data collection are costly; therefore, it is difficult to collect sufficient gear machining data for deep learning training. To solve this problem, this study proposes an attention and adversarial transfer learning method based on ResNet with adaptive Coral loss (A2ResNet-aCoral) for training small-sample datasets. The method integrates sample-spatial attention and dynamic-channel attention to enhance the feature extraction ability. To realize the transfer ability, an adversarial domain structure is constructed to induce the network to extract the homogeneous features of the source and target domains. Moreover, an adaptive correlation alignment loss is proposed to shorten the distance between the source and target domains. The results of a case study indicate that A2ResNet-aCoral has good transfer ability on small sample gear hobbing signals, and the accuracy can reach 92.71%.

Augmented Reality Aided Inspection of Gears

Augmented Reality Aided Inspection of Gears

Design and Manufacturing of Gear Inspection Conveyor System by Using Image Processing

Design and Manufacturing of Gear Inspection Conveyor System by Using Image Processing

Development of Computer Vision Algorithms for Measurement and Inspection of Spur Gears (Dept.M)

Gears are one of the most common mechanisms for transmitting power and motion. Error in gears causes two main problems, increased acoustic noise in operation and increased wear, both of which are sufficiently troublesome to cause concern. Therefore, precision measurement of gears plays a vital role in gear measurement and inspection. The current methods of gear measurement are either time consuming or expensive. In addition, no single measurement method is available and capable of accurately measuring all gear parameters while significantly reducing the measurement time. The aim of this paper is to utilize the computer vision technology to develop a non-contact and rapid measurement system capable of measuring and inspecting most of spur gear parameters with an appropriate accuracy. The vision system has been established in the metrology lab and it is used to capture images for gears to be measured or inspected. A software (named GearVision) has been especially developed in-house using Microsoft Visual C++ to analyze the captured images and to perform the measurement and inspection processes. The introduced vision system has been calibrated for both metric and pixel units. After calibration, the system was verified by measuring two sample gears and comparing the calculated parameters with the actual values of gear parameters. The maximum variations between the calculated parameters and the actual values were +0.101 mm for a spur gear with 156 mm outside diameter. This variation can be decreased by measuring small gears.

Application of the Bilateral Filter for the Reconstruction of Spiral Bevel Gear Tooth Surfaces From Point Clouds

Abstract Reconstruction of gear tooth surfaces from point clouds obtained by noncontact metrology machines constitutes a promising step forward not only for a fast gear inspection but also for reverse engineering and virtual testing and analysis of gear drives. In this article, a new methodology to reconstruct spiral bevel gear tooth surfaces from point clouds obtained by noncontact metrology machines is proposed. The need of application of a filtering process to the point clouds before the process of reconstruction of the gear tooth surfaces has been revealed. Hence, the bilateral filter commonly used for 3D object recognition has been applied and integrated in the proposed methodology. The shape of the contact patterns and the level of the unloaded functions of transmission errors are considered as the criteria to select the appropriate settings of the bilateral filter. The results of the tooth contact analysis of the reconstructed gear tooth surfaces show a good agreement with the design ones. However, stress analyses performed with reconstructed gear tooth surfaces reveal that the maximum level of contact pressures is overestimated. A numerical example based on a spiral bevel gear drive is presented.

A method for enhanced polymer spur gear inspection based on 3D optical metrology

Accurately manufactured gears require a reliable, holistic, and fast inspection method. Standardised geometrical parameters enable a consistent and regulated inspection of gears; however, current inspection methods include only a limited set of measurements for gears at specific locations. Therefore, a method to obtain holistic three-dimensional (3D) measurements with an optical inspection was thoroughly investigated. The measurement data were acquired via 3D optical scanning. The data were then processed and evaluated using the developed software. This was first tested on a simulated scan of an ideal shape with different mesh resolutions and subsequently on a simulated scan with synthetic deviations. The method was finally validated by measuring the gears using a coordinate-measuring machine; the results obtained were compared with those obtained using the developed optical method. A good agreement between the methods was observed. The optical method offers a more holistic measurement approach with many important advantages being identified compared with the tactile method.

Determination of the mean base circle radius of gears by optical multi-distance measurements

Abstract. The required reliability of wind turbine gearboxes increases the requirements for large gear measurements. Extensive measurements to reliably assess the geometry of large gears in the single micrometer range are necessary. Due to an individually fixed measuring volume, standard methods like coordinate and gear measuring instruments reach their limits for large gears with diameters > 1 m. Therefore, a scalable optical measurement approach consisting of a single sensor in combination with a rotary table for multi-distance measurements with subsequent model-based evaluation of shape parameters of gears is presented. The scalable measurement approach is to be extended to a multisensory system in further work. As a fundamental shape parameter the mean base circle radius using the example of spur gears is determined. The base circle radius is used due to the geometric relationship to further shape parameters for example to the profile slope deviation. The theoretically achievable measurement uncertainty of the mean base circle radius due to sensor noise is estimated to less than 5 µm (k=2) for a small and a large gear, which verifies the scalability of the sensor system. In order to show a general proof of principle, two series of optical measurements on a gear with a diameter of 0.105 m are performed and referenced with a tactile measurement. As a result, random errors of 1.2 µm for k=2 are determined. The remaining systematic deviations to the reference value amount to 4.3 and 1.6 µm, respectively. Hence, the total measurement uncertainty is currently limited by systematic effects, and the defined aim of a total uncertainty of less than 5 µm (k=2) is narrowly missed by 1.5 µm. The random errors of 1.2 µm (k=2) show, however, that an adequate measurement precision is achieved and that the multi-distance measurement approach has the potential to reach the aimed measurement uncertainty with appropriate strategies to compensate for the systematic influences. The experimental and theoretical results prove the principle applicability of the proposed single sensor multi-distance approach for the precise inspection of gears.

Gear Shape Parameter Measurement Using a Model-Based Scanning Multi-Distance Measurement Approach.

To reduce wind turbine failures by defective drive trains, deviations in the geometry of large gears (diameter ≳ 1 m) must be extensively determined with single-digit micrometer uncertainties. Fixed measuring volumes limit standard measuring methods like coordinate and gear measuring instruments for large gear measurements. Therefore, a model-based scanning multi-distance measurement approach for gear shape parameters is presented. The measurement approach has a scalable design and consists of a confocal-chromatic sensor, rotary table as a scanning unit and model-based signal processing. A preliminary study on a midsize spur gear demonstrates the general feasibility of the model-based scanning multi-distance measurement approach. As a result, the mean base circle radius as the fundamental gear shape parameter is determined with an uncertainty of <5 μm. The calibration and adjustment of the sensor arrangement were performed with a known calibration gear. Scalability is not experimentally validated in this article. However, simulations verify the scalability of the measurement approach in a first step. For gears with 1 m in diameter and varying tooth flank geometries, the estimated achievable uncertainty of the mean base circle radius is still <5 μm. Therefore, the model-based scanning multi-distance measurement approach is a promising alternative for gear inspection.