Automated inspection of spur gears using machine vision approach

In this study emotion-based face expression recognition framework has been proposed using a machine vision (MV) approach. The face emotion dataset has been collected local survey in Bahawalpur city dataset divide into 3 classes happy, sad, and angry. A total of 600 images of size (256 x 256) were transformed into a gray level format and employed a median filter for noise removal. Three non-overlapping regions of interest (ROIs) of size (50 x 50) have been taken and analyze 1800 (600 x 3) ROIs on the overall dataset. Total 45 Statistical features named as texture, histogram, and binary features were extracted. Select optimize features using the correlation-based feature section technique. The optimized dataset employed of MV classifiers namely random forest (RF), logistic (Lg), and J48 are obtained very promising accuracy 96.33%, 95.67%, and 95.33% respectively.

The objective of this study was to observe the potential of machine vision (MV) approach for the classification of eight citrus varieties. The leaf images of eight citrus varieties that were grapefruit, Moussami, Malta, Lemon, Kinow, Local lemon, Fuetrells, and Malta Shakri. These were acquired by a digital camera in an open environment without any complex laboratory setup. The acquired digital images dataset was transformed into the multi-feature dataset that was the combination of binary, histogram, texture, spectral, rotational, scalability and translational (RST) invariant features. For each citrus leaf image, total 57 multi-features were acquired on every non-overlapping region of interest (ROI), i.e. (32x32), (64x64), (128x128), and (256x256). Furthermore, the optimized 15 features using the supervised correlation-based feature selection (CFS) technique were acquired. The optimized multi-features dataset to different MV classifiers namely Multilayer Perceptron (MLP), Random Forest (RF), J48 and Naïve Bayes using10-fold cross-validation method were plugged-in. The results produced by MLP presented an average overall accuracy of 98.14% on ROIs (256x256) outperforming the other classifiers. The classification accuracy values by MLP on the eight citrus leaf varieties, namely; Grapefruit, Moussami, Malta, Lemon, Kinow, Local lemon, Fuetrells, and Malta Shakri were observed 98%, 98.75%, 99.25%, 97.5%, 97%, 95.87%, 95.5%, and 99.37% respectively.

HighlightsWhat are the main findings?A novel method for in situ inspection of gear profiles using a Chromatic Confocal Sensor (CHCS) is proposed.The verification and validation of this gear profile inspection method have been performed via software simulations and experiments.What are the implications of the main findings?Less time-consuming, high throughput gear inspection.Reliable quality control for gear inspection.Gears, as critical power-transmission components in most power equipment, have a particularly urgent need for in situ inspection systems. Traditional gear inspection methods rely on contact inspection instruments, which are not only time-consuming, but also potentially damage the gear surface due to contact. This study delves into the detection requirements in the gear manufacturing process and establishes a rapid, non-contact detection mechanism and model using a CHCS. This model employs a CHCS to achieve high-speed, non-contact measurement on various surfaces with extremely high accuracy, enabling real-time monitoring of production process details, thereby improving production efficiency and ensuring product quality. Through actual inspection and comparison with a standard involute spur gear tooth profile model, this study implements a complete inspection system in a prototype. The results of gear inspection using a CHCS with an accuracy of 1 μm showed that the interquartile range of qualified gears under test (GUTs) was within 2.5 μm, and the beard line value was within 10 μm. The experiment demonstrated a layout equipped with a CHCS where the rotating axis represents the hobbing machine spindle. This method can be completed without moving the gear, enabling subsequent finishing processes.

PurposeThe purpose of this paper is to describe innovative machine vision methods that have been employed for the capture and analysis of 3D skin textures; and the resulting potential for assisting with identification of suspicious lesions in the detection of skin cancer.Design/methodology/approachA machine vision approach has been employed for analysis of 3D skin textures. This involves an innovative application of photometric stereo for the capture of the textures, and a range of methods for analysing and quantifying them, including statistical methods and neural networks.Findings3D skin texture has been identified as a useful indicator of skin cancer. It can be used to improve realism of virtual skin reconstructions in tele‐dermatology. 3D texture features can also be combined with 2D features to obtain a more robust classifier for improving diagnostic accuracy, thereby assisting with the long‐term goal of implementing computer‐aided diagnostics for skin cancer.Originality/valueThe device developed for capturing 3D skin textures is known as the “Skin Analyser”, and as far as the authors know it is unique in the world in being able to recover 3D textures from pigmented lesions in vivo. There currently exist numerous methods for analysing lesions, including manual inspection (using established heuristics commonly known as ABCD rules), dermoscopy and SIAoscopy. The ability to capture and analyse 3D lesion textures complements these existing techniques and forms a valuable additional indicator for assisting with the early detection of dangerous skin cancers such as melanoma.

Reliable and accurate measurement of particle size and subsequent analysis of particle size distribution (PSD) is central to characterization of particulate minerals. Traditionally PSD of particulate materials was determined using standard mechanical sieving, but it was shown to be deficient in accurate particle size measurements, hence PSD analysis. In this paper a highly inexpensive machine vision based approach was proposed as an alternative to mechanical sieving. The test mineral used was ball-milled celestite. This machine vision approach used a document scanner as imaging device and a user-coded java ImageJ plugin performed the image processing and automated the PSD analysis. The acquired color images were preprocessed to binary images and the particles analyzed after grouping them based on their distinct length. Volumes of all particles were evaluated assuming a prolate spheroid geometry from measured lengths and widths. A new approach of using sum of volumes as weighting factor (Svolume) was utilized for particle grouping in ASABE Standard PSD analysis. The plugin also evaluated several significant dimensional (16) and distributional (27) parameters that characterize the PSD of samples. Standard mechanical sieving of samples was performed for experimental verification and results compared with machine vision method. The cumulative undersize PSD followed a log-normal distribution, and the plot against particle size exhibited a linear trend. Shapes of log-normal plot of cumulative undersize PSD were similar between both methods; however, the mechanical sieving curves lagged by 0.2-0.5 mm. The deviation was attributed to the falling-through effect of longer particles through sieve openings.

Currently available driver assistance systems (i) warn the driver based on vehicle state sensors (e.g., door open, outside temperature near or below the freezing point), (ii) offer route guidance information (navigation systems based on GPS and digital road maps), or—in some critical situations—(iii) even actively influence vehicle handling under carefully delimited conditions (anti-blocking-system, electronic-stability-program). This contribution reports about investigations to combine passive GPS- and map-based route guidance with model-based machine vision in order to automatically assess or even execute driving maneuvers in inner-city traffic situations. Information provided by todays route guidance systems is treated as a generic description of lane structure. The schematic description of lane structures extractable from commercially available standard digital maps is automatically instantiated by a machine vision approach which interprets video image sequences recorded by cameras from within a driving vehicle. The resulting model of the lane structure in front of the vehicle is subsequently exploited in order to control vehicle maneuvers in real-time as a proof of principal system competences. The machine-vision-based execution of driving maneuvers can at any time be overridden by the driver.

In the past, roughness values measured directly on machined surfaces were used to develop mathematical models that are used in predicting surface roughness in turning. This approach is slow and tedious because of the large number of workpieces required to obtain the roughness data. In this study, 2-D images of cutting tools were used to generate simulated workpieces from which surface roughness and dimensional deviation data were determined. Compared to existing vision-based methods that use features extracted from a real workpiece to represent roughness parameters, in the proposed method, only simulated profiles of the workpiece are needed to obtain the roughness data. The average surface roughness Ra, as well as dimensional deviation data extracted from the simulated profiles for various feed rates, depths of cut, and cutting speeds were used as the output of response surface methodology (RSM) models. The predictions of the models were verified experimentally using data obtained from measurements made on the real workpieces using conventional methods, i.e., surface roughness tester and a micrometer, and good correlation between the two methods was observed.

A machine vision approach to on-line estimation of run-of-mine ore composition on conveyor belts

Cumulative trauma disorders (CTDs) are a leading cause of disability which generates huge direct and indirect costs in the construction industry. Existing efforts identify the risk of CTDs through survey or observatory studies which are subjective and expensive. This paper attempts to automatically identify the risk of CTDs on construction sites with the aid of a video surveillance system. Specific emphasis is placed on low back disorders (LBDs) which is one of the major work-related CTDs. To this end, a risk identification method is proposed based on location changes of region of interests in the resulting video streams at minimum supervision. It is used to detect repetitive trunk flexion-extension motions, awkward trunk postures, and assess risk of job-related LBDs. Preliminary experimental analysis on lab datasets demonstrates the promise of the proposed method.

A system is presented which coordinates a parallel-jaw gripper and a vision system with the aim of choosing grasping points taking into account stability conditions. The choice of grasping points is done by a new heuristic approach that tries to manage all kind of planar parts with or without internal centre of gravity, possibly containing holes. It needs only two moment-based features, centroid and the direction of the main axis, easily obtained from a Freeman chain-code representation of the boundary. The results are exclusively visual parameters, expressed in visual coordinates (pixels). These algorithms have been implemented in a 4D0F robot arm with one camera fixed over the hand. Visual feedback was used in the control system to improve precision up to the limits of the available visual system.

The causes of geometrical deviations from the production process and the prediction of application properties, such as noise behavior, wear or material fatigue, are only possible by having detailed information about the gear geometry. The gold standard for the gear quality inspection is represented by dimensional measurements with a tactile sensor system. As a result for industrial applications, the slow serial measurement leads to the compromise of a random inspection of the gear geometry. For the purpose of a faster and more extensive surface acquisition, a laser line triangulation sensor is investigated providing 1280 points at a line width of 25 mm with up to 200 lines/s. The results at the tooth of a large cylindrical involute gear with a pitch circle diameter of 922 mm and a face width of 246 mm show the qualification for fast three-dimensional measurements of the convex and reflective surface. The detection of the complete profile line at once is possible. It is shown that the measurement deviation of laser line triangulation can be minimized by increasing the dynamic threshold. The measurement deviations amount to ± 8.2 µm and can be attributed to random and systematic errors. Compared to the standard gear inspection, an acceleration factor of 5700 was attained. An optical scanning of the complete tooth flank provides the prerequisite for an identification of surface defects in the form of breakouts and blemish.

A mathematical model is proposed for the reconstruction of gear tooth surface features based on the manufacturing process deviations measured with the help of a gear inspection (or coordinate measurement) machine. The model is extended to facilitate the computer simulation of meshing between the measured year and a theoretical master gear, or between two measured gears. This would allow one to predict the transmission errors and bearing contact which are otherwise only possible to investigate using the combination of single-flank testing and a time-consuming work-in and examination procedure. The developed model is applied to the determination of tooth-to-tooth transmission errors of parallel-axis gears and can also be utilized to determine bearing contact and contact stresses and be useful in tooth surface fatigue life calculations.

Machine vision is widely used in many applications of engineering. In this paper, a new approach of machine vision is proposed for autonomous robotics navigation. The proposed methodology uses a sequence of images to locate homologous points and rebuild the objects in the robot surroundings. Using this approach a robot is able to measure an obstacle in order to avoid a collision as well as measure its velocity in maneuver using only information provided by cameras. Experimental results validate the application of the proposed method for autonomous robots applications.

In the case of gearboxes, vibration is the primary mode of failure even at the mid-range of operating speeds. Avoiding such failures requires an understanding of gearbox design, vibration theory, and material properties. This chapter details sources and types of gearbox vibrations; characteristics of gearbox vibrations; fundamentals of periodic vibrations; and vibration theory. It provides housing design for single-stage offset parallel gearboxes, high-speed gearboxes, and epicyclic gearboxes. The chapter then provides an analysis and selection of design factors for vibration reduction. It presents five types of gear tooth geometry errors. The chapter also focuses on gear quality inspection and on bearing-induced vibrations.

Precision measuring techniques have been developed interdependently with the techniques of design and manufacturing in the history. Computer aided inspection plays a very important role in modern manufacturing industry. On account of the CMM (Coordinates Measurement Machine) being applied widely and the precision model inspection concept coming true, it is possible to implement the digital inspection of the spiral bevel gear on the CMM. In this paper the framework of the spiral bevel gear digital inspection based on the integration of CAD/CAM/CMM is put forward. The key techniques of the scheme are investigated, which consist of exact modeling of spiral bevel gear based on manufacturing process, datum matching of CAD model and CMM inspection, CMM Inspection path planning, reconstruction of the tooth surface based on the CMM inspection results, Analysis of the deviation between the real gear tooth and CAD model, adjusting strategy of machining parameters.