On-line Inspection Research Articles

Automated Product Inspection in Industry 4.0 Environment

The emergence of Industry 4.0 technologies demands new techniques of measurement which would facilitate seamless integration with other devices in the wireless IoT network. Many of the existing product inspection methods cannot be deployed directly in the IoT environment. Thus, there is a need for innovative inspection techniques in the Industry 4.0 environment. In this context, the proposed inspection technique assumes special significance. Surface irregularities observed in product manufacturing can be due to chatter, vibration, worn-out cutting tools, condition of the machine tools, etc. Evaluation of surface texture helps in predicting a product’s functionality. In this work, an attempt has been made to identify the surface texture images acquired from Shaping, Milling, Electric discharge machining (E.D.M.) and Sand Blasting processes during online inspection. In addition to surface texture identification, the proposed method will also measure surface roughness and component dimensions. Thus, entire product inspection can be done online, and in a single setup. This is also an 100% online inspection method. The main contribution of this proposed research work is that all types of inspection are completed in a single set up, resulting in significant savings.

Defect detection method using laser vision with model-based segmentation for laser brazing welds on car body surface

This paper proposes a defect detection method using laser vision with model-based segmentation for laser brazing welds on car body surface, in which a dynamic ideal surface (DIS), composed of a series of dynamic ideal contours (DIC), is first established for welding surface. The DIC model is obtained by dynamically fitting welding surface contours using the weighted cubic spline regression (WCSR) model with the fast Expectation-Maximization (Fast-EM) algorithm. Then through multi-threshold analysis on the DIC model residuals, whether the contour is defective and the nature of defect can be identified, and finally the defect can be completely separated. The whole process is fully automatic and requires no prior knowledge of defect. Experimental results show that the model-based segmentation method has managed to separate multiple types of defect effectively while meeting the actual accuracy requirements. The robustness of the proposed method has been proved through the test in the case of noise interference and unstable movement of the sensor, which helps to realize online inspection of weld defects in a factory environment.

Development of measurement system using online software for shear-horizontal surface acoustic wave sensor

In the medical field, point-of-care testing (POCT) is garnering attention. An online inspection system can be realized by combining POCT and the Internet. Herein, we report the performances of a measurement system developed using online software for a shear horizontal surface acoustic wave (SH-SAW) sensor. The developed SH-SAW measurement system does not require a phase detection circuit. The developed system can be used when the system is connected to the Internet. This enables an online measurement system that can be used “by anyone, at anytime, and anywhere.” In this study, we used samples of glycerol and water mixtures and then measured the amplitude ratio and phase difference of the SH-SAW sensor. The validity of the developed system is confirmed by comparing the measured results with theoretical values.

Improving data reliability: A quality control practice for low-cost PM2.5 sensor network.

Low-cost air quality sensor networks have been increasingly used for high spatial resolution air quality monitoring in recent years. Ensuring data reliability during continuous operation is critical for these sensor networks. Using particulate matter sensor as an example, this study reports a data quality control method, including sensor selection, pre-calibration, and online inspection. It was used in developing and operating the dense low-cost particle sensor networks in two Chinese cities. Firstly, seven mainstream sensors were tested and one model of particle sensor was selected due to its better linearity and stability. For a batch of sensors of the same model, although they were calibrated after manufactured, there are differences in response toward the same concentration of pollutants. The systematical variation of sensors was corrected and unified through pre-calibration. After deploying them in the field, a data analysis method is established for online inspecting their working status. Using data from these sensors, it evaluates parameters such as intraclass correlation coefficients and normalized root mean square error. These two metrics help to construct a two-dimensional coordinate system and to classify sensors into four status, including normal, fluctuation, hotspots, and malfunction. During a one-month operation in the two cities, 8 (out of 82) and 10 (out of 59) sensors with suspected malfunctions were screened out for further on-site inspection. Moreover, the sensor networks show potential in identifying illegal emission sources that cannot be typically detected by sparse regulatory air quality monitoring stations.

Research on on-line ultrasonic testing of small diameter thin wall stainless steel straight welded pipe

In the production process of thin-walled stainless steel longitudinal welded pipe, there are many defects, such as cracks, porosity, partial welding, incomplete penetration, incomplete fusion. In this study, a local coupling water tank and six ultrasonic linear focusing probes with a center frequency of 5MHz were used to realize the defect identification of the straight welded pipe in the twisted state through the combined detection of the ultrasonic shear wave, longitudinal wave and creeping wave, which can meet the on-line inspection requirements of small-diameter thin-walled stainless steel straight welded pipe.

A machine vision-based defect detection system for nuclear-fuel rod groove

The processing quality of the grooves of a nuclear-fuel rod will directly affect the quality of the finished nuclear-fuel rod. Due to the highly reflective, microscopic, and annular characteristics of nuclear-fuel rod grooves, it has been quite challenging to realize imaging and microscopic defect detection for these grooves. In this work, a machine vision-based defect detection system was developed for nuclear-fuel rod grooves. Through the performance improvement and application of the self-reference template defect detection method, efficient online inspection of nuclear-fuel rod grooves was realized. In the developed system, a combined-light-source imaging system was first designed by combining a coaxial light and a ring light, which realized the clear imaging of a groove. After that, an image expansion strategy was employed to expand the annular groove into a strip-shaped region of interest (ROI). Then, according to the turning processing characteristic of the nuclear-fuel rod groove, the large-size defect detection effect of the self-reference template method was improved by eliminating the anomalous columns prior to generating the self-reference template. The experimental results indicated that the average inspection efficiency of the developed system was 8.026 s/rod, the average false detection rate was 0.183%. The accuracy of the self-reference template method was 87.6%, higher than that of YOLOv2 and Faster R-CNN. The developed system exhibits high inspection efficiency and accuracy, so it can meet the actual detection functions and requirements of production lines, and now it has been successfully applied to actual production.

Orthogonal Optimal Design Method for Spiral Coil EMAT Considering Lift-off Effect: A Case Study

This paper provided a method to improve the conversion efficiency and lessen the lift-off effect of spiral coil electromagnetic acoustic transducer (EMAT) for some specific situations, for example, irregular sample surface, online inspection and varying coating. Moreover, a new evaluation method for the performance of EMAT is proposed. To study the effect of the structural parameters on the performance of EMAT, the orthogonal test method with five factors and four levels is employed. Consequently, the degree of five factors on the lift-off performance and the theoretical maximum value of amplitude are obtained. The results show almost all factors have an obvious impact on the theoretical maximum value of amplitude, while only the magnet-to-coil distance can affect the lift-off performance effectively. The experiment results well agree with the numerical results, in which the improvement of the lift-off performance and the theoretical maximum value of amplitude is 31.94% and 78.79% respectively. In addition, the conversion efficiency of EMAT is increased by 125% when the lift-off is 1 mm. These imply the performance of spiral coil electromagnetic acoustic transducer is improved dramatically and the detectable lift-off is increased as well.

Reducing Noises in Digital Surface Inspection Using a Data Clustering Approach

Abstract Online inspection of manufactured product surfaces is an essential task in today’s industry for the production or product quality control, and coordinate metrology is used for this purpose as the main inspection method in a large variety of manufacturing systems. Optical coordinate metrology which allows acquiring thousands of points from the product surface in a fraction of a minute, is utilized in online and dynamic surface measurement process. However, 3D data points i.e. Point Clouds (PCs) acquired using the optical devices are highly affected by various sources of noises. Hence PCs denoising is a crucial task before any use of PCs in product inspection. Most of the literatures deal with this issue by mesh denoising, which itself requires mesh generation from noise contaminated PCs. Therefore, raw PCs denoising seems a better procedure. To investigate detection of noisy points in PCs, two methodologies are presented in this paper. The methodologies are developed based on a common strategy of gradual learning and developing knowledge from the data points through iterative clustering processes. The developed learning procedures allow self-calibration and defining the clustering parameters. As a result, the clustering of the raw PCs will be conducted smartly considering the specific behaviors recognized in data-points. The developed methodologies are capable to detect noisy clusters for any unorganized data obtained from planer surfaces. The methodologies result in marking data points with high probability of noise presence. The two developed methodologies are compared and their effectiveness are studied. Variety of metrology sensors and inspection tools can adopt the developed methodologies for noise reduction or filtration and significant enhancement in inspection accuracy is expected to be acheived.

High-Precision Medicine Bottles Vision Online Inspection System and Classification Based on Multifeatures and Ensemble Learning via Independence Test

To address the problem of online automatic inspection of drug liquid bottles in production line, an implantable visual inspection system is designed and the ensemble learning (EL) algorithm for detection is proposed based on multifeatures. A tunnel structure is designed for visual inspection system, which allows the bottles inspection to be automated without changing original processes and devices. A high-precision method is proposed for vision detection of drug liquid bottles. A local background difference method is utilized as a soft ON–OFF to capture the bottle image. An image gray-level equalization preprocessing technology is used to eliminate the impact from illumination. Three features are designed, which contains blocked histogram of gradient, blocked histogram of gray, and raw pixel. An EL algorithm is proposed based on independence test and multifeatures, after theoretically analyzing the precondition of precision boosting to EL. Some results of analysis and comparison prove that the proposed method is advanced compared with baseline methods. Specially, there exist remarkable advantages in our method when there are some noises in sample labels. Then, we carried on a 72-h continuous test on a practical production line, in which the error rate of inspection is less than 1‰. In terms of time and precision, it is superior to the traditional manual detection. Hence, the test results prove that the visual inspection system designed and algorithm proposed are advanced and practical.

A Blockchain-Based Traceability System in Agri-Food SME: Case Study of a Traditional Bakery

In this paper we present a blockchain based system for the supply chain management of a particular Italian bread. Goal of the system is to guarantee a transparent and auditable traceability of the Carasau bread where each actor of the supply chain can verify the quality of the products and the conformity to the normative about the hygienic-sanitary conditions along the chain. To realize this system we relied on the Blockchain and the Internet of Thing technologies in order to provide a trustless environment, in which trust is placed in cryptography, in mathematical operations and on the network, and not in public or private companies. Thanks to the use of digital technologies the system aims to reduce the data entry errors and the risk of tampering. Our system is designed so that along the supply chain, the nodes equipped with several sensors directly communicate their data to Raspberry Pi units that elaborate and transmit them to Interplanetary File System and to the Ethereum Blockchain. Furthermore, we designed ad hoc Radio Frequency Identification and Near Field communication tags to shortly supply the proposed system with information about the products and batches. The dedicated RFID tags robustness during on-bread operation was numerically tested. The system will easily allow end consumers to have a transparent view on the whole journey from raw material to purchased final product and a supervisory authority to perform online inspections on the products’ quality and on the good working practices.

Online moisture detection technology and its application prospect in drying of traditional Chinese medicine

Drying is one of the most common unit operations in the production of traditional Chinese medicine. The drying process of traditional Chinese medicine materials is accompanied by the dynamic reduction of water content. As a key index to determine the end of the drying process, the moisture content of materials plays an important role in improving drying efficiency and saving energy. Recently, the drying process of traditional Chinese medicine is mostly monitored by offline detection, and there are few reports of online moisture detection applications. In this paper, the principle and current application of online inspection technology for the material drying process in different fields were introduced. The significance of online detection technology in drying of traditional Chinese medicine was also analyzed. Meanwhile, the application prospect of online detection technology in the field of drying of traditional Chinese medicine was predicted. In response to urgent transformation and upgrading of the traditional Chinese medicine manufacturing industry, the application of online moisture detection technology is expected to be a key breakthrough in the intelligent upgrading of traditional Chinese medicine drying technology and equipment.

Shortwave infrared hyperspectral imaging system coupled with multivariable method for TVB-N measurement in pork

Monitoring and maintaining the freshness of meat is important to ensuring a supply of meat that is safe for consumption. The objective of this study is to present a shortwave infrared (SWIR) hyperspectral imaging system in combination with partial least-squares regression (PLSR) model and feature selection methods that can be used for the prediction of the total volatile basic nitrogen (TVB-N) content in fresh pork. The SWIR hyperspectral reflectance images were acquired for pork samples removed from refrigerated storage after 1, 4, 8, 11, 15, and 21 days. The hyperspectral SWIR images and actual TVB-N contents were used for constructing the PLSR model. PLSR models were optimized by using feature selection strategies such as random frog (RF) and variable importance in projection (VIP) score. The predictions from the optimal RF-PLSR model value with maximum normalization preprocessing exhibited correlation coefficient values for Rc2 and Rp2 of 0.94 and 0.90, respectively. Moreover, this research showed that visualization of TVB-N levels applied to the optimal model based on selected wavebands provide an intuitive way to interpret the spatial information of the sample. This study revealed that the multivariate models developed here for rapid and nondestructive evaluation of pork freshness can be feasible for use in online inspection systems as an effective substitute for traditional methods to evaluate pork freshness.

LCTF-based multispectral fluorescence imaging: System development and potential for real-time foreign object detection in fresh-cut vegetable processing

Ensuring the supply of safe and contaminant-free fresh-cut vegetables is of importance to consumers and suppliers worldwide. The present research aimed to develop an online inspection system, making use of fluorescence and color imaging for detection of potential foreign materials (FMs) unintentionally added to fresh-cut vegetables. A multispectral fluorescence imager incorporating a liquid crystal tunable filter (LCTF) was developed to sequentially acquire selected band images of fresh-cut vegetables. Custom-built software was developed to synchronize the LCTF with the camera exposure to collect the fluorescence images of selected spectral bands and to collect the digital color images of the same samples using a color camera. Image processing algorithms were developed for both fluorescence and color images to detect a wide range of potential FMs in two different fresh-cut vegetables, namely, cabbage and green onion. The obtained results showed that the combined use of the multispectral fluorescence technique and color imaging can detect most FMs with accuracy > 95%; however, non-fluorescent tiny dark FMs could not be detected. The potential of the developed online detection system was further tested in an industrial environment by installation in a fresh-cut processing unit. All hardware parts were enclosed in a black box and the software program was modified to programmatically control conveyor belt movement and facilitate real-time image processing for FM visualization. FMs mixed with fresh-cut vegetables were manually fed onto the conveyor belt; fluorescence and color images of fresh-cut vegetables were acquired simultaneously while moving through the conveyor belt. The developed online system performed almost identically in the industrial environment, and has the potential to scan an area as large as 24 × 24 cm2 in approximately 1.5 s. Thus, its application in commercial setting may be feasible.

Prediction of Surface Roughness of Turning Operations Using Computer Vision (Dept.M)

It is well known that measuring surface roughness is vital to quality control of the machined work piece. Recently, vision systems have been applied in industries for quality control and online inspection. Thus, measuring surface roughness using computer vision became easier and more flexible. Texture features are one of the most important techniques that have been utilized in industries in many applications. In this pap er, the texture features of the gray level co-occurrence matrix (GLCM) have been utilized to predict surface roughness of specimens machined by tuning operations. The relationship between GLCM texture features and surface roughness has been investigated to discover which texture features can be used to predict surface roughness. The correlation coefficient between each texture feature and the arithinetic average height (Ra) was calculated and discussed. The investigation showed that six texture features are highly correlated with Ra. Therefore, a software has been developed to predict surface roughness for specimens machined by turning operations using these texture features. The results showed that the maximum percentage of error between the actual Ra and the predicted Ra was about ±7%.

Laser ultrasonic imaging for defect detection on metal additive manufacturing components with rough surfaces.

Defects or discontinuities are inevitable during the melting and consolidation process of metal additive manufacturing. Online inspection of microdefects during the processing of layer-by-layer fusion is urgently needed for quality control. In this study, the laser ultrasonic C-scan imaging system is established to detect the surface defects of selective laser melting (SLM) samples that have a different surface roughness. An autosizing method based on the maximum correlation coefficient and lag time is proposed to accurately measure the defect length. The influences of the surface roughness on the laser ultrasound signal-to-noise ratio distribution and defect sizing accuracy are also studied. The results indicate that the proposed system can detect notches with a depth of 50 µm and holes with a diameter of 50 µm, comparable in size to raw powder particles. The average error for the length measurement can reach 1.5% if the notch is larger than 2 mm. Meanwhile, the sizing error of a 1 mm length notch is about 9%. In addition, there is no need to remove the rough surface of the as-built SLM samples during the detection process. Hence, we propose that the laser ultrasonic imaging system is a potential method for online inspection of metal additive manufacturing.

Blade Segment with a 3D Lattice of Diamond Grits Fabricated via an Additive Manufacturing Process

Diamond tools with orderly arrangements of diamond grits have drawn considerable attention in the machining field owing to their outstanding advantages of high sharpness and long service life. This diamond super tool, as well as the manufacturing equipment, has been unavailable to Chinese enterprises for a long time due to patents. In this paper, a diamond blade segment with a 3D lattice of diamond grits was additively manufactured using a new type of cold pressing equipment (AME100). The equipment, designed with a rotary working platform and 16 molding stations, can be used to additively manufacture segments with diamond grits arranged in an orderly fashion, layer by layer; under this additive manufacturing process, at least 216000 pcs of diamond green segments with five orderly arranged grit layers can be produced per month. The microstructure of the segment was observed via SEM and the diamond blade fabricated using these segments was compared to other commercial cutting tools. The experimental results showed that the 3D lattice of diamond grits was formed in the green segment. The filling rate of diamond grits in the lattice could be guaranteed to be above 95%; this is much higher than the 90% filling rate of the automatic array system (ARIX). When used to cut stone, the cutting amount of the blade with segments made by AME100 is two times that of ordinary tools, with the same diamond concentration. When used to dry cut reinforced concrete, its cutting speed is 10% faster than that of ARIX. Under wet cutting conditions, its service life is twice that of ARIX. By applying the machine vision online inspection system and a special needle jig with a negative pressure system, this study developed a piece of additive manufacturing equipment for efficiently fabricating blade segments with a 3D lattice of diamond grits.

Bearing faults classification under various operation modes using time domain features, singular value decomposition, and fuzzy logic system

Nowadays, multi-fault diagnosis has become the most interesting topic for researchers, since it has lately attracted a substantial attention. The most published works recently have considered defects detection, identification, and classification as the toughest challenge for rotating machinery monitoring. As feature extraction requires robust techniques for online inspection with a high level of expertise to make automatic decisions on the running machine health status, a robust approach is required to adjust the misclassification of the extracted features, especially under various working conditions. In this paper, we propose the combination of two Time Domain Features (TDFs) in tandem with Singular Value Decomposition (SVD) and Fuzzy Logic System (FLS) to build an enhanced fault diagnosis technique for rolling bearing. The original vibration signal is divided first into several data samples. Thereafter, TDFs are applied on each sample to construct a feature matrix during the feature extraction step. Afterwards, SVD is performed on the obtained matrices in order to reduce their dimension and select the most stable vectors (singular values). Finally, FLS is employed as a powerful tool for automatic feature classification. Experimental results confirm that our suggested approach can enhance the ability to assess the degradation of bearing faults with a higher recognition sensitivity even under different working conditions.

A low-cost three-dimensional reconstruction and monitoring system using digital fringe projection

In this paper, the design and construction of a low-cost three-dimensional (3D) reconstruction and monitoring system using digital fringe projection (DFP) is proposed, which can perform small- and large-scale measurements in different environments and can be applied to various applications such as intelligent monitoring, 3D online inspection, and obstacle detection for vehicle guidance. The contribution of this paper is threefold: (i) development of a comprehensive 3D measuring system that performs sensors handling, coordinate acquisition, reconstruction, and display process simultaneously and quickly; (ii) proposal of new filters to improve quality and efficiency of the system; and (iii) development of a real-time virtual 3D measurement system to calibrate and analyze the proposed methodologies. Optical and simulation measurement results are presented to verify the feasibility and performance of the developed systems. The observed RMS difference was found to be <0.021 mm in the optical measurement. From measured results, it can be concluded that the proposed systems and adopted methodology are effective in obtaining 3D surface profiles.

Online coverage and inspection planning for 3D modeling

In this study, we address an exploration problem when constructing complete 3D models in an unknown environment using a Micro-Aerial Vehicle. Most previous exploration methods were based on the Next-Best-View (NBV) approaches, which iteratively determine the most informative view, that exposes the greatest unknown area from the current partial model. However, these approaches sometimes miss minor unreconstructed regions like holes or sparse surfaces (while these can be important features). Furthermore, because the NBV methods iterate the next-best path from a current partial view, they sometimes produce unnecessarily long trajectories by revisiting known regions. To address these problems, we propose a novel exploration algorithm that integrates coverage and inspection strategies. The suggested algorithm first computes a global plan to cover unexplored regions to complete the target model sequentially. It then plans local inspection paths that comprehensively scans local frontiers. This approach reduces the total exploration time and improves the completeness of the reconstructed models. We evaluate the proposed algorithm in comparison with other state-of-the-art approaches through simulated and real-world experiments. The results show that our algorithm outperforms the other approaches and in particular improves the completeness of surface coverage.

Development of a novel wavelength selection method VCPA-PLS for robust quantification of soluble solids in tomato by on-line diffuse reflectance NIR.

This work was attempted to evaluate the feasibility of a constructed on-line NIR platform coupled with efficient algorithms for rapid and robust quantification of quality parameter in cherry tomato. Specifically, a system was developed based on shortwave NIR spectroscopy for on-line quality inspection of cherry tomatoes. The spectra were recorded in diffuse reflectance mode from 900 to 1700nm, and the conveyor belt speed was fixed to five samples per second. Three novel methods, namely variable combination population analysis (VCPA), uninformative variable elimination (UVE) and competitive adaptive reweighed sampling algorithm (CARS) were coupled with partial least square (PLS) for selecting optimal dataset, and modeling. The obtained results showed that under the optimal tuning parameters (N=100, k=500, ω=14, σ=10%), a total of 512 original variables, only 9 variables (1.75%) were extracted by VCPA. Subsequently, VCPA-PLS yielded outstanding performance in predicting soluble solid content in cherry tomatoes, with a higher correlation coefficient (RP=0.9053), and lower root mean square errors (RMSEP=0.382) in prediction set. This methodology demonstrated the versatile potential of the proposed installation coupled with VCPA methods for on-line detection of total soluble solids in cherry tomatoes.