Study on Microstructure and Properties of TIG Welded Joint of TC2 Titanium Alloy and Quality Inspection

TIG welding experiments of TC2 titanium alloy sheet was carried out,and the well-formed weld was obtained.After welding process,the cross-section microstructure,mechanical properties,fracture morphology and quality inspection of the joint were studied.The results show that the microstructure of the weld consists of a large number of acicularα′andβblock.The microhardness curve shows that the microhardness value in the fusion zone(FZ)of the joint is significantly higher than that in the heat affected zone(HAZ)and the base metal(BM),and the microhardness of the base metal is the lowest.The tensile strength of the joint is equivalent to that of the base metal,and the fracture morphology shows that the fracture mechanism of the joint is mixed ductile-brittle fracture mode.The weld quality is excellent through chemical inspection,penetrant inspection and X-ray inspection.

Numerical Analysis of Quality Inspection of Anchorage System

Sonoprobe method has been applied in non-destructive inspection of anchorage project. The fundament is that dynamic transient excitation causes the elastic vibration of an anchor bar, and flaws can be estimated or deduced by determining transient response of the anchor bar. FEA numeric solution of hyperbolic equation indicates that deductions must comply with acoustic parameters such as velocity of sound, vibration range, wave shape etc when inspecting interior flaws in the grout of an anchor bar. Based on wavelet packet analysis, the energy eigenvector is a flaw vector, which could be regarded as the basis in the nondestructive inspection of anchors. As a non-linear dynamical system, artifical neural networks dealing with quality inspection of gray system have been proved efficient.

On-line quality inspection in laser blank welding using ART2 neural network

Laser blank welding is becoming more and more important in the automotive industry and the quality of the weld is critical for a successful application. A fully automated solution is required to inspect the quality of the blanks. This paper presents a vision inspection system with a CMOS camera which uses ART2 network to inspect the defects on-line to obtain the geometry and the quality of the weld seam. The neural network ART2 has the capability of self-learning fiom the environment. It can distinguish the defects that have been learned before and give new outputs for new defects. So ART2 network is suitable for weld quality inspection in laser blank welding. Additionally, a CO2 laser is used for the blank butt-welding.

Comprehensive Management Strategy for Tunnel Construction Quality Inspection and Monitoring

With the increase in the number and scale of tunnel projects in China in recent years,the comprehensive management for quality inspection and monitoring measurement in the tunnel construction process is receiving more and more attention.Based on this,this paper analyzes the comprehensive management of quality inspection and monitoring during tunnel construction to ensure the quality of tunnel construction.

Digital Quality Inspection

During 2001 and 2005, 1,440,000,000 people got entry-exit quarantine and8,186,000 people were quarantined infectious disease. 675,000 patients different diseases were foundand control measures were taken for 1,908 IV infectors, 5,093 tubercular, 14,000 venereal patientsand infectors, 28,000 viral hepatitis infectors.

Research on the Method of Balance Sheet Quality Inspection based on the Ford Law

This paper based on Benford＇s Law （Benford Law）, the Shanghai stock exchange 15 listed bank balance sheets for the past 5 years data for the study sample, results show that the model in the inspection report can quantify the quality in the process of accounting information quality comparison and has the outstanding advantages of simple, clear, this paper focuses on the Benford＇s law of balance sheet quality inspection method based on in our country and how to test the quality of balance sheets in the introduction and promotion.

Application of Non-Destructive Testing Technology in Quality Testing and Control of Water Conservancy Projects

In view of social development,the demand for water conservancy engineering applications continues to increase.The number and scale of water conservancy projects in China have been in a state of continuous expansion in recent years.As a result,how to achieve efficient testing and effective control of the quality of water conservancy projects has always been a topic of discussion in the field of water conservancy engineering in our country.This paper summarizes the application of non-destructive testing technology in the quality testing and control of water conservancy projects.On the basis of explaining the connotation and application advantages of non-destructive testing technology,the non-destructive testing application strategies for concrete strength,steel corrosion and shallow cracks in water conservancy projects were studied.

Integrated Inspection of QoM, QoP, and QoS for AOI Industries in Metaverses

With the rapid development of information technologies such as digital twin, extended reality, and blockchain,the hype around "metaverse" is increasing at astronomical speed. However, much attention has been paid to its entertainment and social functions. Considering the openness and interoperability of metaverses, the market of quality inspection promises explosive growth. In this paper, taking advantage of metaverses, we first propose the concept of Automated Quality Inspection(Auto QI), which performs integrated inspection covering the entire manufacturing process, including Quality of Materials, Quality of Manufacturing(Qo M), Quality of Products, Quality of Processes(Qo P), Quality of Systems, and Quality of Services(Qo S). Based on the scenarios engineering theory, we discuss how to perform interactions between metaverses and the physical world for virtual design instruction and physical validation feedback. Then we introduce a bottomup inspection device development workflow with productivity tools offered by metaverses, making development more effective and efficient than ever. As the core of quality inspection,we propose Quality Transformers to complete detection task,while federated learning is integrated to regulate data sharing.In summary, we point out the development directions of quality inspection under metaverse tide.

Dynamic estimation and reliability control of quality assurance for bored piles

A systematic method was proposed to estimate the occurrence probability of defective piles(OPDP) from a site according to quality assurance inspection. The OPDP was firstly suggested as the criterion to weight the performance of a pile foundation. Its prior distribution and updating distribution were deduced to follow Beta distributions. To calibrate the OPDP, a dynamic estimation model was established according to the relationships between prior mean and variance and updating mean and variance. Finally, a reliability-control method dealing with uncertainties arising from quality assurance inspection was formalized to judge whether all the bored piles from a site can be accepted. It is exemplified that the OPDP can be substantially improved when more definite prior information and sampling formation become available. For the example studied herein, the Bayesian estimator of updating variance for OPDP is reduced from 0.0037 to 0.0014 for the first inspection, from 0.0014 to 0.0009 for the second inspection, and with less uncertainty by incorporating experience information.

Statistical Analysis and Countermeasures for Specimen Rejection in the Emergency Department

The rejected specimens from the Emergency Department of the Center of Clinical Laboratory from January 1,2022 to January 1,2023 were analyzed to reduce the specimen rejection rates and to improve the quality of inspection.The results showed that there were 1488 samples of rejected specimens and the non-conforming rate was 0.58%.The departments involved were mainly the Emergency Department,the Hematology Department,the Cardiology Department,the Intensive Care Department,and the Brain Surgery Department.Among the reasons for rejection,blood hemolysis accounted for 43.15%,blood coagulation accounted for 26.61%,and the rate of insufficient specimens was 17.14%.Among them,the sample rejection rate for arterial blood gas analysis was the highest,which accounted for 1.74%;followed by specimens for coagulation test,which was 1.18%.These results indicate the main reason for producing rejected specimens is mainly due to not following the standard operating procedure.Specimen rejection can largely be avoided if the standards for specimen collection are strictly followed.

Machine Vision Analysis of Characteristics and Image Information Base Construction for Hybrid Rice Seed

Incompletely closed glumes, germination on panicle and disease are three important factors causing poor seed quality of hybrid rice. To determine how many and which categories should be classified to meet the demand for seed in rice production, the effects of various degrees of incompletely closed glumes, germination on panicle and disease on germination percentage at the harvest and after storage for six months were studied by standard germination percentage test. Six categories of seeds with germ (germinated seeds), severe disease, incompletely closed glumes, spot disease, fine fissure and normal seeds were inspected and then treated separately. Images of the five hybrid rice seed (Jinyou 402, Shanyou 10, Zhongyou 27, Jiayou 99 and Ⅱ you 3207) were acquired with a self-developed machine vision system. Each image could be processed to get the feature values of seed region such as length, width, ratio of length to width, area, solidity and hue. Then all the images of normal seeds were calculated to draw the feature value ranges of each hybrid rice variety. Finally, an image information base that stores typical images and related feature values of each variety was established. This image information base can help us to identify the classification limit of characteristics, and provide the reference of the threshold selection. The management of large numbers of pictures and the addition of new varieties have been supported. The research laid a foundation for extracting image features of hybrid rice seed, which is a key approach to future quality inspection with machine vision.

Application of Digital Image Analysis Method in Metric Screw Thread Metrology

This paper describes the use of computer-aided measurement for external metric screw threads. Thread parameters, including thread pitch, thread angle, pitch diameter and major diameter, were measured with CCD cameras and image analysis software. New technologies such as digital image processing were used to increase the efficiency of measurements. In this study, by reconstructing the toolmaker’s microscope, the computer-aided semi-automated measuring system was developed, which could evaluate the accuracy of screw thread profile. It is concluded that the measurement accuracy is comparable to that of traditional toolmaker’s microscope method. Key words screw threads - quality inspection - accuracy - digital image processing

Fabric Defect Detection Technique Based on Two-double Neural Network

This paper introduces the identification of the defects on the fabric by using two-double neural network and wavelet analysis. The purpose is to fit for the automatic cloth inspection system and to avoid the disadvantages of traditional human inspection. Firstly, training the normal fabric to acquire its characteristics and then using the BP neural network to tell the normal fabric apart from the one with defects. Secondly, doing the two-dimeusional discrete wavelet transformation based on the image of the defects, then wiping off the proper characteristics of the fabric, and identifying the defects utilizing the trained BP neural network. It is proved that this method is of high speed and accuracy. It comes up to the requirement of automatic cloth inspection.

Study of acoustic signal in the process of resistance spot welding based on array sensor system

This investigation was performed to study acoustic field signal in order to improve RSW quality. Researchers firstly built an acoustic array sensor system, which included 8 MPA-416 acoustic sensors, data acquisition card and LabVIEW. The system obtained the acoustic field information in the process of nugget growing. Due to the nonlinearity field signal, array sensor algorithm was utilized to quantitatively analyze the characteristics of acoustie field and reduced noise. The experiment and calculation results show that array sensor system can acquire acoustic field signal of nugget growing in the RSW process and array processing algorithm based on acoustic field can extract characteristic parameters to evaluate RSW quality. It was concluded that the acoustic array sensor system offers a new methodology for RSW quality inspection.

Technological Analysis on Motor Stall and Its Perspective

The brush lock is due to assembly tightly during assembling three gripper so that it curls after some time in motor and bare motors.The motor happen to have current decreasing and cause rotary too slowly is an important technique problem.At last the motor hasn’t worked due to disconnection.It give company to bring assembly issue for customers.So the motor stall is main issue in motor working.We shall pay more attention to it necessarily and shall be strict quality inspection and we shall monitor the flow line for the sake of decreasing it.We shall solve the problem as soon as possible and communicate with the customer engineers.Unloaded rotation is radical in the base inspection.The engineer need to negotiate with supplying engineers for the qualified material of brush.Increasing inspection into more times is a method to decrease unqualified brushes.Some experiment method is explained to analyze for customer engineers.Two kinds of motors ie.stepping and asynchronous motor and motors used in future in car is explained to further knowledge to motor’s application.Such as the experiment with torque,voice and electric voltage converter.The pseudo soldering results the slow rotary.The reverse voltage is a reason for a motor to slow rotary even stall.

An FPGA-based real-time image processing system

This paper analyzes the current difficulties encountered in on-line inspection systems of strip surface quality, specifically relating to problems with real-time processing of huge amounts of data. To address this need, this paper describes an FPGA-based high-speed image processing module with both hardware and software aspects. Improving these two aspects together will help the system achieve real-time processing of massive image data, and simplifies the architecture of the strip surface quality on-line inspection system.

A novel approach to identifying the ultrasonic echoes for inspecting well cementing quality

In this paper, it is presented that an approach based on Continuous Wavelet Thansform(CWT) and Fourier Transform(FT) for identifying ultrasonic echoes in inspecting oil well cementing quality. First, CWT is used to process echo signals. Then spectra of the processing results in some specilic scale-time segments are calculated as pattern features. The research results show that theoretical calculations basically agree with experimental results. Compared with the aPproach based on the spectra or Discrete Wavlet Transform, our approach has advantages of fine analyzing scales, stable features and high recognizing rate. It is very suitable to identify echo signals whose spectra vary with time.

Tianjin Automotive Test Center & National Passenger Car Quality Supervision and Inspection Center——A State Testing and Universality Techniques Research Base in China

Tianiin Automotive Test Center has a test capability as well as a capability of conducting universality techniques research. With fixed assets of around RMB 76.44 million yuan and an architectural area of 11,200 sqm, test labs mainly include: auto emission test lab, full-scale vehicle crash test lab, auto safety test lab, auto lighting and view field test lab, engine test lab,

Bridging the gap:Neuro-Symbolic Computing for advanced AI applications in construction

Deep Learning(DL)has revolutionized the field of Artificial Intelligence(Al)in various domains such as computer vision(CV)and natural language processing.However,DL models have limitations including the need for large labeled datasets,lack of interpretability and explainability,potential bias and fairness issues,and limitations in common sense reasoning and contextual understanding.On the other side,DL has shown significant potential in construction for safety and quality inspection tasks using CV models.However,current CV approaches may lack spatial context and measurement capabilities,and struggle with complex safety and quality requirements.The integration of Neuro-Symbolic Computing(NSC),an emerging field that combines DL and symbolic reasoning,has been proposed as a potential solution to address these limitations.NSC has the potential to enable more robust,interpretable,and accurate AI systems in construction by harnessing the strengths of DL and symbolic reasoning.The combination of symbolism and connectionism in NSC can lead to more efficient data usage,improved generalization ability,and enhanced interpretability.Further research and experimentation are needed to effectively integrate NSC with large models and advance CV technologies for precise reporting of safety and quality inspection results in construction.

Corn kernel classification from few training samples

This article presents an efficient approach to classify a set of corn kernels in contact,which may contain good,or defective kernels along with impurities.The proposed approach consists of two stages,the first one is a next-generation segmentation network,trained by using a set of synthesized images that is applied to divide the given image into a set of individual instances.An ad-hoc lightweight CNN architecture is then proposed to classify each instance into one of three categories(ie good,defective,and impurities).The segmentation network is trained using a strategy that avoids the time-consuming and human-error-prone task of manual data annotation.Regarding the classification stage,the proposed ad-hoc network is designed with only a few sets of layers to result in a lightweight architecture capable of being used in integrated solutions.Experimental results and comparisons with previous approaches showing both the improvement in accuracy and the reduction in time are provided.Finally,the segmentation and classification approach proposed can be easily adapted for use with other cereal types.