Research on Bearing Surface Scratch Detection Based on Improved YOLOV5.

Bearings are crucial components of machinery and equipment, and it is essential to inspect them thoroughly to ensure a high pass rate. Currently, bearing scratch detection is primarily carried out manually, which cannot meet industrial demands. This study presents research on the detection of bearing surface scratches. An improved YOLOV5 network, named YOLOV5-CDG, is proposed for detecting bearing surface defects using scratch images as targets. The YOLOV5-CDG model is based on the YOLOV5 network model with the addition of a Coordinate Attention (CA) mechanism module, fusion of Deformable Convolutional Networks (DCNs), and a combination with the GhostNet lightweight network. To achieve bearing surface scratch detection, a machine vision-based bearing surface scratch sensor system is established, and a self-made bearing surface scratch dataset is produced as the basis. The scratch detection final Average Precision (AP) value is 97%, which is 3.4% higher than that of YOLOV5. Additionally, the model has an accuracy of 99.46% for detecting defective and qualified products. The average detection time per image is 263.4 ms on the CPU device and 12.2 ms on the GPU device, demonstrating excellent performance in terms of both speed and accuracy. Furthermore, this study analyzes and compares the detection results of various models, demonstrating that the proposed method satisfies the requirements for detecting scratches on bearing surfaces in industrial settings.

Single-shot 3D shape measurement based on RGB dot patterns and stereovision: retraction.

The referenced article [Opt. Express30, 28220 (2022)10.1364/OE.466148] has been retracted by the authors.

An Adaptive Hybrid Sampling Method for Free-Form Surfaces Based on Geodesic Distance.

High precision geometric measurement of free-form surfaces has become the key to high-performance manufacturing in the manufacturing industry. By designing a reasonable sampling plan, the economic measurement of free-form surfaces can be realized. This paper proposes an adaptive hybrid sampling method for free-form surfaces based on geodesic distance. The free-form surfaces are divided into segments, and the sum of the geodesic distance of each surface segment is taken as the global fluctuation index of free-form surfaces. The number and location of the sampling points for each free-form surface segment are reasonably distributed. Compared with the common methods, this method can significantly reduce the reconstruction error under the same sampling points. This method overcomes the shortcomings of the current commonly used method of taking curvature as the local fluctuation index of free-form surfaces, and provides a new perspective for the adaptive sampling of free-form surfaces.

Single-shot 3D shape measurement based on RGB dot patterns and stereovision.

One-shot projection structured light 3D measurement is a method to establish the stereo matching relationship and reconstruct 3D shape by projecting one pattern. However, the traditional stereo matching algorithm does not solve the problem of low matching accuracy and matching efficiency, which fundamentally limits the accuracy of 3D measurement. As the projector and imaging systems have daily higher resolution and imaging quality, RGB dots projection has more application prospects because of its ability to establish a stereo matching relationship through one projection. In this work, we proposed a single-shot 3D measurement method using line clustering stereo matching, and model correction methods. The projected RGB dots are extracted by slope differenced distribution and area constrained erosion method. Area constrained erosion can solve the problem of the segmented connected blobs caused by insufficient projection resolution. The clustering stereo matching method is utilized to coarse match the segmented center red points. A model correction method is utilized to restore and constrain the pattern that cannot be imaged. Experimental results demonstrated that our method achieves the best accuracy of about 0.089mm, better than the traditional disparity and RGB line method, which may shed light on the proposed method can accurately reconstruct the 3D surface.

Robust and accurate sub-pixel extraction method of laser stripes in complex circumstances.

Line laser scanning measurement is a major area of interest within the field of 3D laser scanning measurement. Traditionally, sub-pixel extraction of laser stripes is a dominant point for line laser scanning measurement. In particular, the noise separation of laser stripe images and the accuracy of feature extraction of the laser stripe are the main challenges for sub-pixel extraction of laser stripes in complex circumstances. To this end, this study utilizes a robust and accurate method with two steps to extract sub-pixel features of laser stripes for 3D laser scanning measurement. Laser stripe segmentation based on a deep semantic segmentation network is initially implemented for noise elimination of images. Then, the sub-pixel extraction of the gray peak points of laser stripes is accomplished by Shepard sub-pixel interpolation and gray surface fitting, which can adequately utilize the gray distribution of laser stripes and obtain high-precision and anti-interference results. The robustness, effectiveness, and accuracy are verified by comparative experiments with classical methods. The results indicate that the proposed method can obtain much more complete, denser, and smoother results than traditional methods, especially in challenging measurement conditions, such as a large curved surface, a highly reflective surface, or intense ambient light. The accuracy of the proposed method can meet the requirements of high-precision measurement.

Absolute Positioning Accuracy Improvement in an Industrial Robot.

The absolute positioning accuracy of a robot is an important specification that determines its performance, but it is affected by several error sources. Typical calibration methods only consider kinematic errors and neglect complex non-kinematic errors, thus limiting the absolute positioning accuracy. To further improve the absolute positioning accuracy, we propose an artificial neural network optimized by the differential evolution algorithm. Specifically, the structure and parameters of the network are iteratively updated by differential evolution to improve both accuracy and efficiency. Then, the absolute positioning deviation caused by kinematic and non-kinematic errors is compensated using the trained network. To verify the performance of the proposed network, the simulations and experiments are conducted using a six-degree-of-freedom robot and a laser tracker. The robot average positioning accuracy improved from 0.8497 mm before calibration to 0.0490 mm. The results demonstrate the substantial improvement in the absolute positioning accuracy achieved by the proposed network on an industrial robot.

Compensation of Rotary Encoders Using Fourier Expansion-Back Propagation Neural Network Optimized by Genetic Algorithm.

The measurement accuracy of the precision instruments that contain rotation joints is influenced significantly by the rotary encoders that are installed in the rotation joints. Apart from the imperfect manufacturing and installation of the rotary encoder, the variations of ambient temperature could cause the angle measurement error of the rotary encoder. According to the characteristics of the 2π periodicity of the angle measurement at the stationary temperature and the complexity of the effects of ambient temperature changes, the method based on the Fourier expansion-back propagation (BP) neural network optimized by genetic algorithm (FE-GABPNN) is proposed to improve the angle measurement accuracy of the rotary encoder. The proposed method, which innovatively integrates the characteristics of Fourier expansion, the BP neural network and genetic algorithm, has good fitting performance. The rotary encoder that is installed in the rotation joint of the articulated coordinate measuring machine (ACMM) is calibrated by using an autocollimator and a regular optical polygon at ambient temperature ranging from 10 to 40 °C. The contrastive analysis is carried out. The experimental results show that the angle measurement errors decrease remarkably, from 110.2″ to 2.7″ after compensation. The mean root mean square error (RMSE) of the residual errors is 0.85″.

Microfluidics-aided fabrication of 3D micro-nano hierarchical SERS substrate for rapid detection of dual hepatocellular carcinoma biomarkers.

The simultaneous analysis of trace amounts of dual biomarkers is crucial in the early diagnosis, treatment, and prognosis of hepatocellular carcinoma (HCC). In this study, we prepared SERS-active hydrogel microparticles (SAHMs) with 3D hierarchical gold nanoparticles (AuNPs) micro-nanostructures by microdroplet technology and in situ synthesis, which demonstrated high reproducibility and sensitivity. Compared with traditional 2D SERS substrates, this newly prepared 3D SERS substrate provided a high density of nano-wrinkled structures and numerous AuNPs. Furthermore, a newly designed SERS-active substrate was proposed for the simultaneous microfluidic detection of AFP and AFU. The Raman signals of sandwich immunocomplexes on the surface of the SAHMs were measured for the trace analysis of these biomarkers. The proposed microfluidic platform achieved AFP and AFU detection in the range of 0.1-100 ng mL-1 and 0.01-100 ng mL-1, respectively, which represents a good response. Indeed, this platform is easy to fabricate, of low cost and has short detection time and comparable detection limits to other methods. As far as we know, this is the first study to achieve the simultaneous detection of AFP and AFU on a microfluidic platform. Therefore, we proposed a new simultaneous detection platform for dual HCC biomarkers that shows strong potential for the early diagnosis of HCC.

Rapid simultaneous SERS detection of dual myocardial biomarkers on single-track finger-pump microfluidic chip.

Acute myocardial infarction (AMI) is a serious disease with high mortality that afflicts many people around the world. The main cause of death from AMI was the inaccurate early diagnosis, which resulted from the medical treatment might be a delay. Therefore, it is crucial to achieve the rapid detection of AMI. The cardiac troponin I (cTnI) level in human serum may significantly increase as the myocardial membrane ruptured, and the creatine kinase-MB (CK-MB) was also associated with the AMI recurrence and the infarct size of myocardial infarction. Both of them are regarded as important cardiac biomarkers for the early diagnosis of AMI. Therefore, we chose these two cardiac biomarkers as indicators for simultaneous detection. We proposed a single-track finger-pump microfluidic chip for simultaneous surface-enhanced Raman scattering (SERS) detection of cTnI and CK-MB. The entire detection process takes only 5 min without the cumbersome syringe pump. Meanwhile, it enables multiple reagent additions and removals of the unbonded reactants. This microfluidic sensor employed "sandwich" immunoassays based on SERS nanoprobes, AMI biomarkers, and magnetic beads. It is possible to detect two cardiac biomarkers simultaneously in a single measurement, greatly simplifying the detection process and reducing the detection time. Magnetic beads with SERS nanoprobes were separated and captured in the microchamber by a round magnet integrated into the chip. Our results showed that the detection limits of cTnI and CK-MB could reach to 0.01 ng mL-1, respectively. The limit of detections (LODs) match with the clinical threshold values for AMI biomarkers. It is believed that the proposed single-track finger-pump microfluidic chip can be used as an effective tool for determining early AMI.

SERS-Based Immunoassay of Myocardial Infarction Biomarkers on a Microfluidic Chip with Plasmonic Nanostripe Microcones.

We developed a new plasmonic nanostripe microcone array (PNMA) substrate-integrated microfluidic chip for the simultaneous surface-enhanced Raman scattering (SERS)-based immunoassay of the creatine kinase MB isoenzyme (CK-MB) and cardiac troponin (cTnI) cardiac markers. The conventional immunoassay usually employs a microtiter plate as the solid capture plate to form the immunocomplexes. However, the two-dimensional (2D) surface of the microtiter plate limits the capture efficiency of the target antigens due to the steric hindrance effect. To address this issue, a gold film-coated microcone array with nanostripes was developed that can provide a large surface area for capture antibody conjugation and serve as a SERS-active substrate. This unique nano-microhierarchical structure showed an excellent light trapping effect and induced surface plasmon resonance to further enhance the Raman signals of the SERS nanoprobes. It significantly improved the sensitivity and applicability of SERS-based immunoassay on the microfluidic chip. With this integrated microfluidic chip, we successfully performed the simultaneous detection of CK-MB and cTnI, and the detection limit can reach 0.01 ng mL-1. It is believed that the PNMA substrate-integrated microfluidic chip would play a critical role in the rapid and sensitive diagnostics of cardiac diseases.

Improving the measuring length accuracy of articulated arm coordinate measuring machine using artificial neural network.

A new method for further improving the measuring length accuracy of the articulated arm coordinate measuring machine (AACMM) is proposed. The detailed procedure of the proposed method involves kinematic error calibration with the Levenberg-Marquardt algorithm and then non-kinematic error (such as link deflection, thermal errors, and error motions of the rotation shaft) compensation with a back-propagation neural network optimized by the mind evolutionary algorithm. In order to verify the effectiveness and correctness of the proposed method, the simulation and experiment were carried out on an AACMM. The simulated and experimental results demonstrate that the measuring length accuracy of the AACMM is improved significantly after kinematic error calibration and non-kinematic error compensation, confirming the effectiveness and correctness of the proposed method.